UNIVERSITY OF HAWAl'I LIB

UTILIZATION OF SHORT-WAVELENGTH NEAR INFRARED REFLECTANCE

SPECTROSCOPY, MORPHOMETRIC MEASUREMENTS, SEX AND LOCATION

OF CATCH TO PREDICT NUTRIENT CONTENT OF WHOLE HOMOGENIZED

PACIFIC HERRING (CLUPEA PALLAS!)

A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OFHAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE

DEGREE OF

MASTER OF SCIENCE

IN

ANIMAL SCIENCES

MAY 2004

ByCarey L. Morishige

Thesis Committee:

James R. Carpenter, ChairpersonYong Soo KimBarbara Rasco

ACKNOWLEDGEMENTS

I would first like to thank the members ofmy thesis committee James R.

Carpenter, Yong Soo Kim and Barbara Rasco for their expertise and assistance in the

completion, compilation and editing ofthis thesis project. More specifically, I would like

to thank James R. Carpenter, PhD ofthe Department ofHuman Nutrition, Food and

Animal Sciences at the University ofHawaii at Manoa for his guidance and mentoring

throughout this project. Within the same department I would also like to thank Yong Soo

Kim, PhD for his willingness to aid in this project and especially for his expertise in

working with meat products. The willingness and expertise ofBarbara Rasco, PhD ofthe

Department ofFood Science and Human Nutrition at Washington State University, was

an invaluable asset to this thesis project. Aside from my committee, I would also like to

thank the Marine Mammal Research Program ofthe University ofHawaii, for their

gracious donation ofthree lots ofPacific herring, as well as the use oftheir fish

preparation facility on the Kaneohe marine base, Hawaii. For the fourth lot ofherring, I

would like to thank the Alaska SeaLife Center and the marine mammal staffand trainers

in Seward, Alaska for their generous donation. From Washington State University

(WSU), I would like to thank Mengshi Lin, PhD for his unfailing support, assistance and

expertise in Near Infrared Reflectance (NIR) spectroscopy. Also from WSU, I would

like to thank Dewi Setiady who aided in the scanning, analysis and NIR calibration ofmy

samples. For his constant encouragement, as well as expertise in marine mammal

husbandry, I would like to thank Jeffrey Pawloski ofthe Laboratory Animal Services at

the University ofHawaii. Finally, I would like to thank Kathryn Stanberry for her

constant support and assistance in the proximate analysis ofmy samples.

iii

ABSTRACT

In marine mammal husbandry, diet analysis is an integral part ofmaintaining

optimal health ofan animal. At many marine parks in the United States, Pacific herring

(Clupea pallasi) makes up a component ofthese animals' diets. Previous studies on

Pacific herring have found correlations between morphometric measurements, sex and

location ofcatch with the nutrient content ofherring. These variables were analyzed to

determine the effects of source and sex on nutrient composition ofherring and to examine

the possibility ofusing morphometric measurements ofPacific herring including length

(em), weight (g), volume (ml) and/or their ratios to predict its proximate composition.

Results showed significant lot and location differences. Ninety-nine samples were

measured, homogenized, the analyzed with standard methods ofproximate analyses.

Results showed that no significant correlations existed between morphometric

measurements or sex and nutrient composition. However, there were significant

differences (P<O.OOI) between all analytes (except carbohydrate) and location of catch.

Traditional methods ofdiet analysis, in the marine mammal field, are time

consuming and costly. Short-Wave Near Infrared Reflectance (SW-NIR) Spectroscopy

was suggested for its speed, efficiency and cost effectiveness. The objective ofthis

project was to develop SW-NIR spectroscopy calibration equations for moisture, fat,

protein and mineral content for whole homogenized Pacific herring. The prediction

equations (R2) for the calibrations for lots ranged from .27-.88, .66-.93, .48-.87, and .24­

.91 for moisture, fat, protein and ash, respectively. Calibration models for each location

were fairly comparable: Alaska (Lot D) R2=O.66-0.91 and Canada (Lots A through C)

R2=0.47-0.81.

IV

TABLE OF CONTENTS

Acknowledgments iii

Abstract iv

List ofTables vii

List ofFigures ix

Chapter 1: Introduction 1

1.1 Marine Mammal Diet Analysis 1

1.2 Pacific Herring (Clupea pallasi) 1

1.3 Morphometric Measurements 2

1.4 Traditional Methods ofProximate Analysis 2

1.5 Near Infrared Reflectance Spectroscopy 4

1.6 Objectives 6

Chapter 2: Literature Review 7

2.1 Marine Mammal Field 7

2.2 Pacific herring (Clupea pallasi) 7

2.3 Effects on Nutrient Content ofPacific Herring 8

2.4 Laboratory Analyses 12

2.5 Near Infrared Reflectance Spectroscopy 13

Chapter 3: Utilization ofmorphometric measurements, sex and/or location ofcatch topredict the nutrient content ofwhole, homogenized Pacific herring (Clupea pallasi) 19

3.1 Introduction 19

3.2 Materials and Methods 21

3.3 Results 25

v

3.4 Discussion 30

3.5 Conclusion 34

Chapter 4: Utilization of Short Wave Near Infrared Reflectance Spectroscopy to predictthe nutrient content ofwhole, homogenized Pacific herring (Clupea pallasi) 51

4.1 Introduction 51

4.2 Materials and Methods 54

4.3 Results 58

4.4 Discussion 61

4.5 Conclusion 66

Chapter 5: Overall Summary and Conclusions 79

Appendix Table 1 87

Appendix Table 2 88

Appendix Table 3 89

Appendix Table 4 90

Appendix Table 5 91

Appendix Table 6 92

References 93

VI

LIST OF TABLES

3.1 Average, standard deviation and minimum and maximum values formorphometric measurement results by lot and location for Pacific herring 36

3.2 Linear regression relationships between morphometric measurements forPacific herring, including the R2 value and Root Mean Square Error (RMSE) 37

3.3 Average, standard deviation and minimum and maximum values for nutrientcomponent results ofPacific herring - overall and by lots and locations 38

3.4 Average, standard deviation and minimum and maximum values for nutrientcomponents ofPacific herring by size and sex (P >0.05 for all) 39

3.5 Linear regression equations and R2 values (>60%) nutrient componentrelationships in Pacific herring analyte correlations (n=99, P<O.OOI) 40

3.6 Sequence oflinear regression equations used to predict energy density (GE(kcaVg», lipid (%), ash (%), and protein (%) content ofPacific herring starting frommoisture content 40

3.7 Calculated proximate composition of samples from a previous study (usinglinear regression equations to predict analyte content) compared to known chemicalreference values 41

3.8 Linear regression R2 values, means and standard deviations (SD) forcalculated (using linear regression equations to predict analyte content) and knownchemical reference results 42

4.1 Linear regression equations and R2 values (>60%) nutrient componentrelationships in Pacific herring analyte correlations (n=99, P<O.OOI) 67

4.2 Sequence oflinear regression equations used to predict energy density (GE(kcaVg», lipid (%), ash (%), and protein (%) content ofPacific herring starting frommoisture content 67

4.3 Number oflatent variables and standard error ofprediction (SEP) for eachcalibration model chosen per component for each lot and location ofPacific herring.... 68

4.4 Actual chemical and SW-NIR predicted results for feed components ofPacificherring 69

vii

4.5 R2 value comparing SW-NIR predicted and calculated (using regressionequations) values with those from chemical analyses ofPacific herring 70

vm

LIST OF FIGURES

Figure: Page:

3.1 Map ofBritish Columbia, Canada. Pacific herring harvest area is circled 43

3.2 Map ofAlaska, United States. Pacific herring harvest area is circled 43

3.3 Quadratic regression fitted line plot for volume (ml) versus length (cm) ofPacific herring 44

3.4 Quadratic regression fitted line plot for weight (g) versus length (cm) ofPacific herring 44

3.5 Quadratic regression fitted line plot for weight (g) versus volume (ml) ofPacific herring 45

3.6 Quadratic regression fitted line plot for weight per unit length (g/cm) versusvolume (ml) ofPacific herring 45

3.7 Quadratic regression fitted line plot for volume per unit length (ml/cm) versusweight (g) ofPacific herring 46

3.8 Quadratic regression fitted line plot for weight per unit volume (g/ml) versuslength (cm) ofPacific herring 46

3.9 Bar graph showing mean length, volume and weight for Pacific herring sexcategories offemale, male and female with eggs 47

3.10 Bar graph showing mean volume per unit length (ml/cm), weight per unitlength (g/cm) and density (g/ml) for Pacific herring sex categories -offemale, maleand female with eggs 47

3.11 Bar graph showing mean length, volume and weight for Pacific herring sizecategories of small, medium and large 48

3.12 Bar graph showing volume per unit length (ml/cm), weight per unit length(g/cm) and density (g/ml) for Pacific herring size categories of small, medium andlarge 48

3.13 Linear regression plot ofmoisture versus gross energy (GE) content on anas fed basis (R2=O.96) for Pacific herring 49

3.14 Linear regression plot ofgross energy (GE) versus crude lipid (EE%)content on an as fed basis (R2=O.97) for Pacific herring 49

IX

3.15 Linear regression plot ofgross energy (GE) versus ash content on a drymatter basis (R2=0.72) for Pacific herring 50

3.16 Linear regression plot ofcrude lipid (EE) versus protein content on a drymatter basis (R2=0.68) for Pacific herring 50

4.1 Short-Wavelength Near Infrared (SW-NIR) spectra for lot A ofPacificherring 71

4.2 Second derivative transformation ofSW-NIR spectra for lot A ofPacificherring 71

4.3 Average chemical reference results for components in Pacific herring byharvest location 72

4.4 Average chemical reference results for components in Pacific herring by lot..... 72

4.5 Linear regression plot ofgross energy (GE) versus moisture content on an asfed basis (R2=O.96) ofPacific herring 73

4.6 Linear regression plot ofgross energy (GE) versus lipid (EE) content on anas fed basis (R2=o.97) ofPacific herring 73

4.7 Linear regression plot ofgross energy (GE) versus ash content on an as fedbasis (R2=0.72) ofPacific herring 74

4.8 Linear regression plot of lipid (EE) versus protein content on a dry matterbasis (R2=0.68) ofPacific herring 74

4.9 Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for protein in Pacific herring lots A (LV=5), B (LV=7), and C (LV=6) 75

4.10 Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for lipid in Pacific herring lots A (LV=8), B (LV=6), and C (LV=6) 75

4.11 Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for protein in Pacific herring lots D (LV=5) 76

4.12 Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for lipid in Pacific herring lots D (LV=5) 76

4.13 Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for protein in Pacific herring lots A, B, & C combined (LV=6) '" '" 77

x

4.14 Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for lipid in Pacific herring lots A, B, & C combined (LV=7). 77

4.15 Pacific herring lot C linear regression plot oflipid (EE) content by chemicalanalysis versus EE predicted by SW-NIR (R2=O.93) using six latent variables 78

4.16 Pacific herring lot D linear regression plot ofash content by chemicalanalysis versus ash predicted by SW-NIR (R2=O.91) using five latent variables 78

..

Xl

CHAPTER 1. INTRODUCTION

1.1 MARINE MAMMAL DIET ANALYSIS

Numerous marine parks in the United States house and exhibit marine mammals

such as Steller sea lions, Hawaiian monk seals and Harbor seals. One ofthe main

components to these animals' diets is herring. For those marine parks located on the

West Coast (including Hawaii and Alaska), the main species ofherring fed is Pacific

herring (Clupea pallasi).

In most marine mammal facilities, diet assessment is a key part ofdaily

husbandry. In a facility that houses an endangered or threatened species ofmarine

mammal, diet analysis is more critical. Diet analysis becomes even more important if

key research is being performed at the facility. Having the animals in captivity facilitates

needed research on the behavior, physiology and nutrition ofmarine mammals. Many

times, one or more components ofa study will rely upon accurate diet analysis.

1.2 PACIFIC HERRING (Clupeapallasi)

Pacific herring, a member ofthe Clupeidae family, is a widely used commercial

species. In the United States, during 2001, 91.3 million pounds ofPacific herring were

caught, equaling approximately 13.2 million dollars (Holliday and O'Bannon, 2001).

Consumed by humans and used as a component offeed for animals, the nutrient

content ofherring is important to know. According to the U.S. Department of

Agriculture's (USDA) National Nutrient Database for Standard Reference (2002), the

1

proximate composition of 100 g of(edible) raw Pacific herring is: protein 16.39 g, total

lipid 13.88 g, ash 2.37 g, and water 71.52 g (as-fed basis).

There are numerous factors influencing the proximate composition ofthis species.

Both season and location ofcatch contribute to the nutrient variation ofPacific herring,

especially in lipid content. Also, the age ofthe fish, sex and/or stage ofthe reproductive

cycle influence nutrient content. Some herring are "wild caught," which means that there

is minimal fishery preparation ofthe fish before freezing. In other fisheries, herring are

corralled, starved (to clear gut contents) and then collected before being frozen. These

different methods offishery processing can also affect nutrient content of the fish.

1.3 MORPHOMETRIC MEASUREMENTS

Because correlations between morphometric characteristics ofherring and its

proximate content have been found in previous studies (Castellini et al., 2001, Anthonyet

al., 2000), there exists a possibility ofutilizing these measurements to predict nutrient

content ofherring. Ifa correlation ofthis kind does exist, a marine mammal facility

would be able to simply measure the length, volume, or weight ofa herring and predict

its nutrient content. The ability to do this would greatly facilitate diet analysis. This

would be the simplest and quickest method ofgaining proximate content ofa herring.

There would be very minimal sample preparation and no laboratory analysis needed.

1.4 TRADITIONAL METHODS OF PROXIMATE ANALYSIS

In the marine mammal husbandry field, diet analysis is an integral part of

maintaining optimal health ofan animal. The amount ofa species offish fed to an

2

animal is based not only upon the age, weight and energy requirement ofthe animal, but

also upon the proximate content of the fish. In order to estimate how much ofa certain

species offish should be fed, accurate nutrient analyses (to determine percentages offat,

protein, moisture and ash and its energy density, kcaVg) must be readily available.

In most facilities, when a new lot of fish comes in, samples ofthat fish are sent to

outside laboratories for proximate analysis prior to diet formulation. In some cases, the

new lot of fish may need to be used quickly as the current lot is running low. In these

instances, accurate proximate analyses are needed quickly.

These laboratories can be far away and the lab analyses are usually quite

expensive and time consuming. Ofthe many chemical analyses that can be run, the most

common for proximate analysis are Goldfisch or soxhlet methods using ethyl ether for

lipid extraction (crude fat content), Kjeldahl procedure for nitrogen determination

(Nitrogen x 6.25 = crude protein content), freeze-dried or oven-dried dry matter (moisture

content) and ash via combustion furnace (mineral content). Before the analyses can be

done the sample needs to be prepared by homogenization, drying, freeze-drying or a

combination oftwo ofthese methods. This sample preparation can be time consuming,

taking up to a day or more to complete depending upon the size ofthe fish or sample and

method used. The optimal sample consistency is entirely homogenous. One ofthe

challenges in achieving this with fish is the grinding ofthe bones, scales and skin in order

to get a completely homogenous sample mixture. From here, the analyses are run. Both

the ethyl ether extraction and the Kjeldahl procedure also include the use ofhazardous

chemicals such as ethyl ether (C.JIlOO), sodium hydroxide (NaOH), and sulfuric acid

(H2S04). They also require large volumes ofwater, mainly to run through the equipment.

3

All analyses rely heavily upon human accuracy in measuring and weighing. Therefore,

there is a greater possibility for human error in determining the proximate content of the

sample. These analyses also rely on the availability ofproper equipment and materials,

and a trained lab technician. These materials are costly, and cleaning them after every

analysis takes time. Another drawback ofthese chemical analyses is that most times the

sample is destroyed during the processing and/or analysis, and therefore unavailable for

possible fatty acid, amino acid or individual mineral element analysis later on.

Due to the length oftime these analyses take, many times a marine mammal diet

is formulated without the needed information for fat, protein, moisture, mineral and gross

energy content. In these cases, the pounds ofthat species offish to be fed are estimated

based on previous proximate data and curator judgment. In some cases, the animals are

fed simply a percentage oftheir bodyweight. This leads to an erroneous diet and possible

nutrition and diet related behavioral problems in the animal being fed (e.g., cessation of

eating or "playing" with food). Because ofthis, a more efficient and less costly

alternative method ofnutrient analysis is needed. Near Infrared Reflectance

Spectroscopy has been suggested as that alternative.

1.5 NEAR INFRARED REFLECTANCE SPECTROSCOPY

Near Infrared Reflectance (NIR) spectroscopy is an expanding technology which

has recently grown in popularity. In the livestock feed and forage industry, it has been

widely used and well accepted for the prediction ofnutrient composition used in ration

(diet) formulation. This technology has the ability to provide both quantitative and

qualitative information on a wide array ofproducts (especially raw materials) in a matter

4

ofminutes (Burns and Ciurczak, 1992). A researcher, for example, is able to

simultaneously obtain the amounts of several organic analytes (e.g., protein, fat, fiber,

etc.) in a sample. NIR spectroscopy is a cost effective and accurate method ofanalysis

where no hazardous chemicals are needed. It is also non-destructive to samples. As

indicated previously, this technology often requires minimal sample preparation and

provides the capability to analyze multiple nutrients simultaneously in a very short period

of time. Most NIR units normally scan samples for approximately 30 seconds and take

an average of20-30 scans in that time period.

NIR spectroscopy can be used to detect virtually all organic compounds. Easiest

to detect are those with functional groups such as hydroxyl, carboxyl, amine and carbon­

hydrogen (Burns and Ciurczak, 1992). NIR spectroscopy works by measuring the

amount of light reflected from a thoroughly homogenized sample. The light reflected is a

function ofthe light absorbed by the chemical components (i.e., functional groups)

making up the sample. The light absorbed by a specific chemical component is related to

the amount of that component present in the sample. Basically, a NIR spectrophotometer

will shine a light source ofvarying wavelengths, normally between 600-11OOnm

(depending upon the type ofsample) onto the sample. This will cause the molecules in

the sample to vibrate. These vibrations are caused by the bending and stretching of

hydrogen bonds with carbon, nitrogen and oxygen. The spectrophotometer detects the

signals reflected and amplifies those signals. These spectra then go to a computer

(containing a software program that usually comes with the NIR spectrophotometer)

where they are correlated, by comparison to an internal reference standard (calibration),

to determine the amount ofthat particular component present in the sample. There is also

5

a form ofspectroscopy that detects the signals that travel through the sample. This is

known as Near Infrared Transmission spectroscopy.

The accuracy ofNIR spectroscopy depends greatly upon an accurate independent

method ofmeasuring the properties within a sample of interest. Accuracy also depends

highly on sample preparation. Samples must be uniform in consistency and particle size

to reduce error and increase both the accuracy and repeatability ofthe analysis. The

results ofthis independent method ofanalysis (such as chemical analysis) will be used to

build a calibration model, which the spectrophotometer will use when analyzing that

particular sample. Although NIR spectroscopy is an extremely rapid method ofanalysis,

the first stage ofdata analysis (i.e.• chemical and statistical analyses) is time-consuming

(Murray and Cowe, 1992) and sometimes quite costly.

1.6 OBJECTIVES

The objectives of this project are: 1) to determine the effects ofsource and sex on

nutrient composition ofherring, 2) to examine the possibility ofusing morphometric

measurements ofPacific herring including length (em), weight (g), volume (ml) and/or

their ratios to predict its proximate composition, and 3) to develop and compare NIRS

calibration equations and nutrient regressions for fat, protein, moisture and mineral

content for whole homogenized Pacific herring (C/upea pa//asi).

6

CHAPTER 2. LITERATURE REVIEW

2.1 MARINE MAMMAL FIELD

Diet analysis is an integral component ofgood marine mammal husbandry.

Marine parks across the United States house and exhibit various species of marine

mammals, some of them endangered. In cases like these, where an endangered or

threatened species, such as the Hawaiian monk seal (Monachus schauinsiandi), is housed,

diet analysis becomes increasingly more important. Having marine mammals in captivity

facilitates needed research on the behavior, physiology and nutrition of different species.

Many times, one or more components ofa study will rely upon accurate diet analysis.

Due to the known variation in the nutrient composition offish, along with their variation

in seasonal availability, routine sampling and proximate analysis is necessary (Bernard et

ai., 1997).

Several marine mammal facilities formulate diet based upon a percentage of the

animal's weight. Others formulate diet based upon the gross energy content (kcal/kg per

day) of the feed. Either way, a nutritionally balanced diet, which takes into consideration

the physiological health and characteristics of the specific animal, is the primary goal.

2.2 PACIFIC HERRING (Ciupeapallasi)

Pacific herring (Ciupea harengus pallasi Valenciennes 1847) is a widely and fully

used commercial species. This schooling specie is distributed from northern Baja

California to Toyama Bay, Japan, continuing to the shores ofKorea and the Yellow Sea

(Miller and Lea, 1972). This species of herring is one of the main components to captive

7

marine mammal diets in most U.S. marine parks located on the west coast, Hawaii and

Alaska. Pacific herring is also a major prey item for marine mammals in the Pacific,

including the endangered North Pacific population ofhumpback whales.

Since it is consumed by humans and used as a component offeed for animals,

knowing the nutrient content ofa herring is important. Several studies have looked at the

proximate composition ofPacific herring. In a study done by Castellini et al. (2001)

Pacific herring was 16.8±2.2% lipid (as-fed basis) and had an average energy density of

9.3±0.8kJ/g (as-fed basis). In another study conducted by Anthony et al. (2000), Pacific

herring was 77.6±0.20% moisture (as-fed basis) and 1O.5±0.80% lipid (dry matter basis).

According to the U.S. Department ofAgriculture's (USDA) National Nutrient Database

for Standard Reference (2002), the proximate composition of 100 g of (edible) raw

Pacific herring is: protein 16.39 g, total lipid 13.88 g, ash 2.37 g, and water 71.52 g (as­

fed basis).

2.3 EFFECTS ON NUTRIENT CONTENT OF PACIFIC HERRING

There are numerous factors influencing the chemical make-up of this species.

Among these factors are harvest location and/or season, sex and/or reproductive status

and methods of processing and storage.

2.3.1 Size and Morphometric Characteristics

Having the ability to predict proximate content ofherring based upon

morphometric characteristics, such as length, weight and/or volume, would be extremely

helpful to the field of marine mammal husbandry. In an emergency case, where a box of

8

herring needs to be fed and its proximate analysis was unavailable, the ability to estimate

(predict) chemical composition from moisture content would prove invaluable.

Lipid content was found to be correlated with the size of a herring. In a study by

Anthony et al. in 2000, large herring (14-30 cm, 38 ± 1.6%) had, on average, twice the

lipid content of medium sized herring (10-14 cm, 25 ± 0.7%) and three times that of the

small sized herring «10 cm, 10 ± 0.5%). Other studies have found that the size ofa

herring is related to age. Lipid content was found to be positively correlated with the age

ofa herring (Payne et al., 1999; Paul et al., 1998; Harris et al., 1986; Stansby, 1976).

Therefore, it can be inferred that the size of a herring is correlated with lipid content.

These size-related differences may result from changes in the allocation ofenergy with

stage ofgrowth and sexual maturity (Calow and Townsend, 1981). In younger fish, more

energy is allotted to somatic growth. This is due to an increase in locomotive efficiency,

predator evasion and food procurement (Calow and Townsend, 1981). During this stage,

growth requires more protein turnover than does weight gain and maintenance in adults.

Therefore, adults store more energy as lipid (Anthony et al., 2000; Harris et al., 1986).

2.3.2 Location ofCatch

A study by Anthony et al. (2000) found that location of catch also affects the

proximate content of herring. Researchers from this study found that there was a great

variation in lipid content (21-29%) in herring between different areas ofPrince William

Sound, Alaska (i.e., northeast versus central and southwest). A significant difference

(P<0.01) in lipid content ofherring was also seen between Prince William Sound (41%)

and the Lower Cook Inlet (32%), Alaska. The differences seen between areas within

9

Prince William Sound were attributed to availability of prey (i.e., zooplankton and

calanoid copepods). Seasonal differences in prey availability have also been cited as a

possible reason for differences in body growth (Ware, 1985) and lipid reserves (Anthony

et al., 2000). Similar regional differences were found in a study done by Paul and Paul

(1999) in Prince William Sound, Alaska.

2.3.3 Sex

Reproductive cycle stage, sex, and fecundity have also been found to influence

nutrient content, especially for lipids (Castellini et al., 2001). Spawning occurs at

different times depending upon the population's geographic location. In Alaska, for

example, herring are spring spawners (ADF&G, 1978). In Pacific herring, when the

ovary weights exceed five percent of the total body weight, sexual maturation has begun

(Hay and McCarter, 1999). Sexual maturity has been reported between the ages of two

(Rumyantsev and Darda, 1970) and three years (Hay and Cater, 1999). Most herring do

not spawn until at least three or four years ofage. By the age offive, approximately 95%

ofthe population has reached sexual maturity (Rumyantsev and Darda, 1970). A study

done by Paul et al. (1996) found that whole ovarian energy content was positively

correlated with body weight (R2=0.76).

In Atlantic herring (CIupea harengus), a species very similar to Pacific herring, it

was found that female gonads were higher in lipid content than male gonads. It could be

assumed then that females may be higher in lipid content than males.

Fecundity in herring has been found to increase with body length and width

(Nagasaki, 1958) and varies depending upon age (Rumyantsev and Darda, 1970).

10

2.3.4 Lot or Season of Catch

When frozen or processed herring are purchased, they are purchased in lots. Each

lot ofherring has a specific date ofcatch; therefore, lot is related to the date or season of

catch. Depending upon the season of catch, lipid content in Pacific herring varies (Paul

et al., 1998; Payne et al., 1999). This is thought to be due to the seasonal variation in

availability of food in different locations. This variation has been directly related to lipid

content (Blaxter and Holiday, 1963; Stansby and Hall, 1967). In Alaskan herring, energy

intake increases during the spring, summer and early fall months in preparation for the

winter when prey availability and feeding conditions are poor (Blaxter and Holiday,

1963; Paul et al., 1996; Paul and Paul, 1998). This has resulted in higher lipid content in

herring caught in fall and early winter (Castellini et al., 2001). Lipid content and energy

density of herring caught in the same season, during different years, will also vary. In a

study done by Anthony et al. (2000), herring of the same size caught in 1995 had a higher

lipid content and energy density than those caught in the same region in 1996. This was

thought to be due to the availability and/or quality oftheir food being better in 1995.

Similar interannual variations in lipid content were found in the study done by Paul and

Paul (1999).

2.3.5 Processing and Storage

Methods used in the harvest (catch), shipping, and handling ofPacific herring,

along with the storage temperature of the fish affect their resulting nutrient content. The

methods used in freezing the fish prior to shipping is also very important. Fish come

both block-frozen, as well as individually quick frozen (IQF). Ofthe two, IQF fish are

11

usually of higher quality due to the shorter time it takes for the fish to freeze. With this

method offreezing fish exposure to oxygen is minimized. This decreases the likelihood

of lipid oxidation, which oxygen exposure is thought to increase (Hultin, 1992).

Factors related to storage, such as temperature, may also affect the likelihood of

lipid oxidation. At higher temperatures, degradation of proteins and oxidation oflipids

occur (Hultin, 1992; Bernard et al., 1997). The suggested optimal storage temperature is

between -18C and -30C (Bernard et al., 1997). This is especially key with high fat fish

such as herring since fattier fish are more likely to undergo lipid oxidation (Hultin, 1992).

2.4 LABORATORY ANALYSES

2.4.1 Methods ofChemical Analyses

Most times, feed samples are sent to laboratories outside of the marine mammal

facility for proximate analysis. These laboratories can be far away and the lab analyses

are usually quite expensive and time consuming. Ofthe many chemical analyses that can

be run, commonly used methods for nutrient analysis are Goldfisch and Soxhlet methods

using ethyl ether extraction (crude lipid content) and Kjeldahl procedure (nitrogen

determination for crude protein content). Common methods for determining moisture

and ash content are freeze-drying and oven-drying (moisture content), and using an

electric muftle furnace (ash or total mineral content).

There exist a variety of laboratories and methods for determining the lipid content

of a fish. This is due to the tremendous variation in the methods used for processing and

preparation of herring for nutrient analysis.

12

2.4.2 Drawbacks ofLaboratory Analyses

Before the analyses can be done the sample needs to be prepared by

homogenization, drying, freeze-drying or a combination of two of these methods. This

sample preparation can be very time consuming, and often taking up to a day or more to

complete depending upon the method used. Once uniform particle size and texture is

gained, the samples are prepared and the nutrient analyses can be run.

The Goldfisch or Soxhlet lipid extractions and the Kjeldahl procedure to

determine nitrogen content both include the use ofhazardous chemicals such as ethyl

ether (CJflOO), sodium hydroxide (NaOH), and sulfuric acid (H2S04). All ofthese

analyses rely heavily upon human accuracy in measuring and weighing. Therefore, there

is a greater possibility for human error in determining the nutrient content of the sample.

These analyses also rely on the availability of proper equipment and materials and a

trained lab technician. These required materials (e.g., equipment, glassware and

reagents) are costly, chemicals are difficult to dispose ofand cleaning them after every

analysis takes time. Other drawbacks of these chemical analyses are that most times the

sample is destroyed during the process and large quantities ofwater are used.

2.5 NEAR INFRARED REFLECTANCE SPECTROSCOPY

2.5.1 Background

Near Infrared (NIR) Reflectance spectroscopy is an expanding technology which

has recently grown in popularity. In the livestock feed and forage industry, it has been

widely used and well accepted for the prediction ofnutrient composition used in diet

analysis (Williams and Norris, 1987). This technology has the ability to provide both

13

quantitative and qualitative information on a wide array ofproducts (especially raw

materials) in a matter ofminutes (Bums and Ciurczak, 1992). This enables a researcher,

for example, to obtain the amount of an organic analyte ofinterest (e.g., protein) in a

sample very quickly. NIR spectroscopy is a cost effective and accurate method of

analysis where no hazardous chemicals are needed. It is also non-destructive to samples.

This technology provides the capability to analyze multiple nutrients simultaneously.

Most NIR units normally scan samples for approximately 30 seconds, take an average of

20-30 scans during that time period and conveniently determines the average spectra and

analyte/nutrient content for that sample.

NIR spectroscopy can be used to detect virtually all organic compounds. Easiest

to detect are those with functional groups such as hydroxyl, carboxyl, amine and carbon­

hydrogen bonds (Bums and Ciurczak, 1992). NIR spectroscopy works by measuring the

amount oflight reflected from a thoroughly homogenized sample. The light reflected is a

function ofthe light absorbed by the chemical components (i.e. functional groups) that

make up the sample. The light absorbed by a specific chemical component is related to

the amount of that component present in the sample (Osborne, 2001). Basically, a NIR

spectrophotometer will shine a light source ofvarying wavelengths onto the sample. This

will cause the molecules in the sample to vibrate. The bending and stretching of

hydrogen bonds with carbon, nitrogen and oxygen cause these vibrations. For NIR

reflectance spectroscopy the spectrophotometer detects the signals reflected. This

spectral data is analyzed using linear regression based statistical models where they are

quantified, by comparison to a series ofreferences with known composition (calibration),

to determine the amount ofthat particular component present in the sample. There is also

14

a form of spectroscopy that detects the signal that travels through, or is absorbed by the

sample. This is known as Near Infrared Transmission (NIT) spectroscopy.

2.5.2 Calibration Model

Once the primary data analyses are completed and the amounts or properties of

the sample are collected, a calibration model is built. In order to build the calibration

model, the NIR spectra ofthe sample are needed. To obtain this, these samples must be

run on an NIR spectrophotometer. Regression equations are then calculated based on the

NIR spectra obtained and the known analyte information. The model is used to predict

future unknowns. Partial Least Squares regression statistics are most commonly used for

linear calibration models. This method takes into account all wavelengths used.

Depending on the make of the NIR unit, or company from which equipment or software

are purchased, there can be considerable variation in models employed. Based upon

these statistics, the model with the lowest standard error ofprediction is used as the

calibration model (Murray and Cowe, 1992). The NIR computer software does much of

the analyses and generally aids developing this calibration model.

The next step is to test the validity and the prediction accuracy of the calibration

model chosen using cross validation. To do this, the samples are divided into two sets: 1)

calibration set and 2) prediction or validation set. Here, the analyte data in the calibration

set is used, with the calibration model chosen, to predict the amount ofan analyte in the

prediction set. More recently, "leave-one-out" cross validation is used. With this

method, one sample is left out while the rest are used to predict the analyte ofinterest

within that sample. An advantage ofcross validation is that the sample is not included in

15

the calibration model and therefore, the model can be tested independently (Murray and

Cowe, 1992).

Once the calibration model is chosen and validated, this calibration can be used to

predict future unknowns. These unknowns, however, must be within similar sample

populations as those used the calibration set. For example, the calibration for fish is

thought to be species specific (Rasco, personal communication).

The accuracy ofany type ofNIR spectroscopy depends greatly on an accurate,

independent method ofmeasuring the properties within a sample of interest. Increasing

the number of samples used to create the calibration is thought to increase the reliability

of the calibration. However, errors can occur and are usually associated with the NIR

prediction accuracy. They arise due to several factors such as, previous errors in the

reference methods (i.e. chemical analysis), instability with the NIR spectrophotometer,

and inappropriate choice ofthe calibration model (Burns and Ciurczak, 1992),

inconsistent sample preparation and particle size. Samples must be uniform in

consistency to reduce error and increase the accuracy ofthe analysis. They should also

be ofthe same constant temperature (Bechmann and Jorgensen, 1998). The results of this

reference analysis (such as laboratory chemical analysis) will be used to build the

calibration models, which the spectrophotometer will use when analyzing that particular

sample. Although NIR spectroscopy is an extremely rapid method ofanalysis, this first

stage ofdata analysis (i.e. chemical and statistical analyses) is time-consuming (Murray

and Cowe, 1992).

16

2.5.1 NIR Spectroscopy and Fish

The use ofNIR spectroscopy in the fish industry has grown in recent years. This

technology is used mainly for quality control purposes (i.e. in aquacultured fish) leading

to a higher quality offish in the market. Recent studies looked at minimizing sample

preparation time by scanning whole, intact fish. In a study done by Downey (1996) NIR

spectroscopy (700-1100nm) was used to predict the moisture and oil content offarmed

salmon. Scanning was done through the skin on intact fish. R2 values in this study

ranged from 0.69-0.77 for moisture and 0.70-0.74 for lipid content. Intact fish were also

scanned in studies done by Lee et al. (1992), Isaksson et al. (1995) and Wold and

Isaksson (1997). In the study by Lee et at. (1992), whole rainbow trout were scanned

intact and calibration models were created for lipid content. Isaksson et at. (1995),

scanned intact salmon fillets for fat, protein and moisture content. Wold and Isaksson

(1997), used a fiber optic probe to scan 49 intact salmon for fat and moisture content. In

some studies, sample preparation was minimized, however not removed completely. In

one study by Rasco et al. (1991), cross sections offrozen plugs taken from cultured

rainbow trout were scanned. This method was able to predict, with fairly good accuracy,

fat, moisture and protein content. In another study done by Wold et al. (1996), cylinders

of intact tissue were taken from various sites along and within ten farmed salmon. The

R2 results for fat prediction were greater than 0.90 for all sites except one (0.44 for the

plug containing skin and dark muscle). Other studies went further in preparing samples

by homogenizing or grinding them prior to spectra collection. One such study was done

by Sollid and Solberg (1992) where they ground cultured Atlantic salmon fillets before

scanning. This study used NIR spectroscopy (850-1050nm) to predict the fat content of

17

the ground salmon. Prediction results were exceptional with an R2 value of0.98. Ground

fish were also scanned in studies done by Isaksson et al. (1995), Valdes et al. (1997), and

Zhang and Lee (1997). In a study done by Darwish et al. (1989), fish were processed and

ground to milk-like consistency in order to be analyzed via mid-infrared transmittance

spectroscopy. The method of sample preparation used in this study was extensive and

time consuming. There is a range in sample preparation techniques when utilizing this

technology, however current research tends to focus on minimizing sample preparation as

much as possible.

18

CHAPTER 3. UTILIZATION OF MORPHOMETRIC MEASUREMENTS,SEX AND/OR LOCATION OF CATCH TO PREDICT THE NUTRIENTCONTENT OF WHOLE, HOMOGENIZED PACIFIC HERRING (Clupea

pallasi)

3.1 INTRODUCTION

Numerous marine parks in the United States house and exhibit marine mammals

such as Steller sea lions, Hawaiian monk seals and Harbor seals. One of the main

components to these animals' diets is herring. The main species of herring fed in marine

parks located on the west coast (including Hawaii and Alaska) is Pacific herring (Clupea

pallasi).

In most marine mammal facilities, diet assessment is a key part of daily

husbandry. In a facility that houses an endangered or threatened species ofmarine

mammal, diet analysis is more critical. Diet analysis becomes increasingly more

important ifvital research is being performed using the animals at the facility. Having

marine mammals in captivity facilitates research on the behavior, physiology and

nutrition of marine mammals. Many times, one or more components ofa study will rely

upon accurate diet analysis.

3.1.1 Morphometric Measurements ofPacific Herring

Lipid content in many species offish depends upon the size of the fish. This

relationship has been reported in Pacific herring (paul et al., 1998; Payne et al., 1999).

Since lipid is related to the size ofa fish and size was found to be closely related to age, it

could be stated that the lipid content ofa fish is related to size. Studies have found a

positive correlation has been found between standard size and lipid content in herring

19

(Castellini et al., 2001). However, another study, done by Paul and Paul (1998), found

no predictable association between standard length and whole body energy content,

which is made up primarily of lipid. Their linear regression resulted in an R2 value of

0.008. This could be due to the fact that growth in length and mass are both influenced

by different factors so there is notable variability in mass-at-age (Tanasichuk, 1997).

There seems to be a general tendency for a correlation between lipid content and size,

however other factors such as location and season ofcatch, and reproductive status may

also affect this correlation.

Since some correlations have been found between morphometric characteristics of

herring and its nutrient content in previous studies, there exists a possibility ofutilizing

these measurements to predict nutrient content ofherring. If a correlation does exist

between these parameters, a marine mammal facility would be able to simply measure the

length, volume, or weight of a herring, then calculate its volume per unit length, or

weight per unit length, and estimate its nutrient content. The ability to do this would

greatly facilitate diet analysis because there would be minimal sample preparation and no

laboratory analysis would be needed.

3.1.2 Sex and Location Effects on Nutrient Content

Reproductive cycle stage, sex, and fecundity have also been found to influence

nutrient content, especially lipids (Castellini et al., 2001). Location also affects the

nutrient content ofherring as was found in a study by Anthony et al. (2000) where a great

variation was observed in lipid content of herring within Prince William Sound, Alaska.

20

They also reported a significant difference (P<0.01) in lipid content ofherring between

Prince William Sound and the Lower Cook Inlet, Alaska.

Studies have also found that the quantities of several other sample constituents are

highly correlated. This method of predicting the proximate content of a herring was

however, mentioned in a study done by Anthony et al. (2000), where they suggested the

use of the linear regression formula for predicting gross energy based on moisture

content. The use of this method of calculating analyte concentrations was not found in

previous literature.

Therefore, the objectives of this study were to determine the impact of source

(location ofcatch) and sex ofPacific herring on nutrient composition, and to assess the

possibility ofusing morphometric measurements ofherring including length (cm), weight

(g), volume (ml) and/or their ratios to predict its nutrient (fat, protein, and carbohydrate),

total mineral or ash and gross energy (GE) content.

3.2 MATERIALS AND METHODS

3.2.1 Sample Collection

The Pacific herring samples (n = 99) used were from two fisheries - North Bay

Meat Company which sells herring caught in British Columbia, Canada (lots A, B, and

C) and Petersburg Fisheries in Alaska (lot D) (Figures 3.1 and 3.2). The Marine

Mammal Research Program ofthe University ofHawaii donated the North Bay herring.

Samples for those lots were prepared at their fish preparation facility at Kaneohe Marine

Base Headquarters, Hawaii. The Petersburg herring was donated by the Alaska SeaLife

Center in Seward, Alaska. Each lot (A, B, C, and D) was made up of herring caught on a

21

specific date. Catch dates for these lots were not known since dates were not recorded.

Herring samples within these lots were individually quick-frozen and randomly selected

using the methods described below. Samples were then shipped, packed in blue ice and

still in the frozen state, to Hawaii via overnight FedEx.

Herring samples were collected from several boxes from each lot, and stored at

-80°C. Four to five fish were collected once a week from a 13.6 kg box ofherring.

These herring were being sampled as boxes were being opened and used to feed

bottlenose dolphins, a false killer whale, Harbor seals and/or Steller sea lions. Due to

sample collection within such a dynamic setting, the number ofboxes sampled from each

lot could not be controlled. Approximately 30 fish were collected per lot. Herring were

randomly selected based upon estimated size categories within each box - small, medium

and large. Sizes were determined by comparison to other herring in the box that could be

seen at the time ofcollection. Three size categories were collected not only to randomize

sample collection based upon the size distribution within each lot, but also to randomize

possible nutrient range in each lot. Ten fish of each size were collected per lot. Handling

ofthe fish was kept to a minimum for quality control purposes, especially because the

unsampled herring would later be fed.

3.2.1 Morphometric Measurement and Sample Preparation

To prepare tissue samples for nutrient analysis, the herring were first thawed in a

cool, fresh water bath. Fish were then put into a clean stainless steel pan. Standard

length was measured, with a ruler, in centimeters (to the nearest 0.25 em) from the tip of

the mouth to the end of the caudal peduncle. The herring was then weighed to the nearest

22

tenth gram, using an Ohaus model DS10 digital scale. Volume was measured by

displacement ofwater in a 1000 ml cylindrical glass graduated cylinder with 10 ml

demarcations. The beaker was filled with 500-600 ml offresh water. The fish was then

dropped in gently and the water displacement was measured to the nearest 5 ml and

recorded. Prior to homogenization, the herring were cut sagittally into 4 pieces and the

sex (male, female or female with eggs) was determined and noted. Herring were

homogenized in a glass container with pouring lip, using a 12-speed Oster blender with a

450-watt motor. The blending process for each fish took approximately 10min. since

time was spent scrapping down the sides of the container as blender speed was increased

from "chop," to blend and finally to "mix." The thoroughly mixed samples were then

scooped out with a stainless steel spoon and distributed into two 50 m1 disposable sterile

centrifuge tubes. Whole herring were homogenized because the nutrient make-up of the

entire fish was needed since marine mammals are fed the entire fish. The sample tubes

were then stored at -80°C until analysis.

3.2.2 Proximate Analysis

Chemical proximate analysis was conducted at the University ofHawaii Animal

Sciences nutrition laboratory. Standard methods of the Association of Official Analytical

Chemists (1990) for meat (Ellis, 1984) were used. Duplicate analyses were run for each

component. To determine dry matter content, 3-5 g ofwet sample were placed in

ceramic crucibles and dried for 24 hours in a 100°C drying oven. Moisture was

determined by calculating the difference between the initial wet weight and final dry

weight. Ash content was determined on dried samples by combustion in an electric

23

muflle furnace for 8 hours at 650°C. Crude protein content was determined using a

modified Kjeldahl nitrogen analysis. Approximately 1-3 g ofwet sample was first

digested in a Tecatur 1015 block digester programmed with a 4.5 hour ramp up and

digestion time. Samples were then transferred into Kjeldahl flasks for distillation and

titration using normal Kjeldahl units, and nitrogen content was determined by titration.

Crude fat content was determined using the Goldfisch ether extract procedure.

Approximately 4-9 g of fresh wet sample were placed in thimbles and dried at 50°C for

48 hours prior to analysis. The lower temperature ensured that fat from the herring did

not seep through the thimble during the drying process. The dried samples in the

thimbles were then analyzed. For all data, when errors between duplicates were greater

than 5%, they were rerun, with the exception ofash. Due to a lack of sufficient sample

material to rerun analyses, the means of the ash duplicates were used. Ash results with

errors greater than 15%, if it was determined to be a definite outlier, were removed.

Considerable diversity in ash weights has been found and may be due to individual

differences in robustness of the skeleton (Paul et ai., 2001). Also, due to the high fat

content of herring, there is the possibility of some splattering as samples are heated

quickly during the ashing process, thus there is a potential for sample loss.

To calculate crude protein and fat content of the herring samples on dry matter

basis, the dry matter percentage was used to estimate dry sample weight. Carbohydrate

(CRO) content was determined by difference using the equation: CRO (dry matter basis)

= 100 - (%Ash + % Crude Protein + % Crude Fat). Gross energy was calculated using

the protein, fat and CRO energy values ofPond et al (1995) in the following equation:

24

GE (kcaVg) = «%Crude Protein/lOO) * 5.7) + «%Crude Fat/100) * 9.4) +

«%CHOIlOO) * 4.1)

3.2.3 Statistical Analysis

Resulting morphometric data were compiled into a Microsoft Excel spreadsheet

along with proximate analysis results. Data were statistically analyzed using Minitab

Statistical Software Release 13.31. General linear and quadratic regressions, and one­

way ANOVAs were run, and Tukey's pairwise comparisons were used to separate out

significant means.

3.3 RESULTS

3.3.1 Morphometric Measurements

Morphometric measurements oflength (cm), volume (ml), and weight (g) were

recorded and the volume per unit length (mVcm), weight per unit length (g/cm) and

weight per unit volume (g/ml; density) were calculated (Table 3.1). No significant

differences were seen either among lots or between locations (P>0.05). Length (cm)

ranged from 14.0 to 23.5 cm with a range in volume and weight of30-170 ml and 27.22­

154.22 g, respectively. Overall mean herring length, volume and weight was 18.4 cm,

84.6 ml and 85.4 g, respectively.

Morphometric measurements were linearly regressed against each other. Both

lots and locations were separated out. Linear regression equations and resulting adjusted

R2 values for each variable were fairly similar (Table 3.2). Only adjusted R2 values are

cited herein. These values represent R2 values adjusted for degrees offreedom. Lowest

25

RZ values were seen in lot C (Rz=O.76-0.87) and highest RZ values were seen in lot A

(Rz=O.93-0.97). Due to this similarity it was proposed that an overall regression equation

could be used, regardless oflot and location, to estimate morphometric measurements.

Linear regressions were run on all data. RZ values were highest between length and

volume (Rz=O.91), length and weight (Rz=O.93), and volume and weight (Rz=O.96).

Linear regressions between morphometric ratios also had fairly high RZ values, except for

length versus density (weight/volume; RZ=O.08). Knowing that growth follows a sigmoid

curve, quadratic regressions were then run on overall data to see ifRz values would

increase (Figures 3.3-3.8). Resulting RZ values did increase between length and volume

(Rz=O.93), and length and weight (Rz=O.94) and slightly between volume and weight

(Rz=O.96). Based on the results, the overall quadratic equations for all morphometric

measurements and ratios (except density) could be used to predict other morphometric

measurements.

3.3.2 The Effects ofLot. Location, Sex and Size on Morphometric Measurements

Morphometric measurements were then compared to the main variables oflot (A

through D), location (Alaska and Canada), sex (males, females, and females without

eggs) and size (small, medium and large) to determine if significant differences were

present. One-way ANOVAs were run along with Tukey's pairwise comparison.

When morphometric measurements within lots A through D were analyzed, no

significant differences resulted. The same was true for morphometries between the

Alaska and Canada locations.

26

Morphometric measurements were then analyzed with three sex categories -

male, female without eggs, and female with eggs. Significant morphometric differences

were found between the male and female sex categories in volume (P=0.036), weight

(P=0.043), volumellength (p=0.020), and weightllength (P=0.037) (Figures 3.9 and 3.10).

When significant means were separated, significance differences in volume,

volumellength and weightllength were due to a difference between males and females.

There was overlap in all three sex categories for weight.

Morphometric measurements were also run against size. This refers to the small,

medium and large size categories that were used to randomly select samples from each

box. Because the actual range of length for each size category was estimated at the time

of sampling, statistics were run to make certain each category was significantly different

from the other. One-way ANDVAs were run and all size categories were found to be

significantly different with a P-value less than 0.001 (Figures 3.11 and 3.12). For each

morphometric measurement, the significance was due to a difference between all three

size categories (Tukey's pairwise comparison).

3.3.3 Morphometric Measurements and Proximate Content

Morphometric measurements were then correlated with the nutrient components

to determine whether significant relationships existed. Statistical analyses were run to

determine whether there exists a relationship between the morphometric measurements of

herring and their proximate content. No significant correlations were found between

morphometric measurements and sample analytes.

27

3.3.4 Main Variables

All main variables, including lot, location, sex and size were compared to

determine if there existed any significant differences. All were compared using one-way

ANOVAs and no significant differences were found.

3.3.5 The Effects ofLot. Location, Sex and Size on Proximate Content

Statistical analyses were run to determine if there were any effect ofmain

variables on sample composition. Nutrient components analyzed included: crude protein,

crude fat, ash, moisture, carbohydrate (CHO) and calculated gross energy (GE) content.

When analyzed, there were significant differences between lots A, B, C and D for

each nutrient analyzed (P<O.OOl). When statistically significant means were separated,

no trend or pattern existed in differences between lots. The same statistical analyses were

then run on locations. The amounts ofall nutrients, except CHO, were significantly

different by location (P<O.OOI). Again, no discernable trend or pattern existed whereby

one location would have a greater level of all components. Table 3.3 contains the results

of these analyses.

The main variables of sex and size were also analyzed with proximate content.

Neither was significantly different in respect to proximate content (Table 3.4). No

discernable trends were apparent when results were graphed.

3.3.6 Proximate Content

Linear regressions were run on all analytes to determine whether or not

correlations existed between them (Table 3.5). To begin, regressions were run with the

28

proximate data on a dry matter basis. These regressions were then run with the data on

an as-fed basis to determine ifR2 values would increase. This was the case in a few of

the analyses. A negative correlation was found between moisture and gross energy on a

dry matter basis (R2=0.69). When the as-fed data were used, the resulting R2 value

increased to 0.96 (Figure 3.13). There was also a highly significant positive correlation

(R2=0.97) between lipid and gross energy content on a dry matter basis. When the

regression was run using the data on an as-fed basis, the R2 value remained the same

(Figure 3.14). On the dry matter basis, ash was negatively correlated to gross energy,

with an R2 value of 0.72 (Figure 3.15). Based on an R2 value of0.68, protein was best

correlated with lipid on a dry matter basis (Figure 3.16).

The nutrient correlations cited herein were highlighted because they had the

highest R2 value for the prediction ofa certain component. The equations from these

regressions could be used to later calculate gross energy (GE (kcal/g», lipid, ash, and

protein content based on a fairly easy to obtain moisture content (Table 3.6). The

predictability of these equations was tested by using them to predict the proximate

composition ofa known group of herring from a previous study. In Table 3.7, calculated

and actual chemical results were compared for each species ofherring, as well as overall.

Linear regressions were then run comparing the calculated (equation predicted) and

known chemical results (Table 3.8). R2 values for protein, lipid, ash and gross energy

(kcal/g) were high when all data (both Pacific and Atlantic herring) was used (R2=0.98,

0.97,0.65,0.94, respectively). R2 values decreased when fish were separated by species,

especially for ash content.

29

3.4 DISCUSSION

3.4.1 Morphometric Measurements

When the morphometric measurements oflength (cm), volume (ml), weight (g),

volume per unit length (ml/cm), weight per unit length (glcm) and weight per unit

volume (glml; density) were analyzed, highly significant correlations were seen between

several measurements. Quadratic regressions (R2=0.93-0.96) for all morphometric

correlations were, on average, a better fit than the linear regressions (R2=O.91-0.96).

Regression equations (linear and quadratic) between lots and locations were similar, and

thus overall regression equations could be used to depict correlations.

Highest R2 values were seen in linear and quadratic correlations between volume

(ml) and weight (g) (R2=0.96). Both volume and weight are more accurate estimators of

the actual "size" ofthe herring than length is. For example, several herring were fairly

long, however thin, while others were short, but plump (i.e. female with eggs). Herring

lengths in this study were in the mid-range (14-23.5 em) compared to those used in other

studies. For example, Castellini et al. (2001) used a size range of 11 to 27cm. No

literature could be found currently, citing the weight or volume ranges ofherring.

3.4.2 The Effects ofLot. Location, Sex and Size on Morphometric Measurements

Ofthe main variables, significant differences between morphometric

measurements were found in sex and size. When morphometric measurements were

analyzed by sex category, all measurements except length had a P-value less than 0.05.

This was due to the difference in volume, vo1.llength and wt.llength between males and

females. On average, males tended to be larger than females. There was a slight overlap

30

with males and females with eggs. This overlap may be due to the increased volume and

weight ofa female due to the eggs. Previous studies have found a similar correlation

between fecundity and weight ofa female herring (paul et al., 1996).

The significant differences found between size classes confirmed that the visual

size categories assigned during the sample collection process were indeed different.

3.4.3 Morphometric Measurements and Proximate Content

No correlations between the morphometric measurements and nutrient

components were found. In previous studies, standard length was found to be positively

correlated with lipid content (Anthony et aI., 2000; Castellini et al., 2001). This

correlation is thought to be due to the fact that standard length is indicative ofage class

and since lipid content increases with the age of the herring, it should therefore be

correlated to length as well. Rapid weight gain is needed early in a herring's lifecycle.

This requires more protein turnover than does normal weight gain and maintenance in

adults. Therefore, there is thought to be an increase in the storage ofenergy as lipid in

older, adult herring (Harris et al., 1986). This standard length to lipid correlation was not

found in either dry matter or as-fed basis with this set of data (P-values>0.5; R2

values<O.OI). This data set represented a reasonable range in standard lengths from 14.0­

23.5 cm, but did not have either abnormally large or small herring. The lack of a

correlation may have been due to a smaller range in size used for this study. In a study

done by Castellini et al. in 2001, a size range of 11-27 cm was used. In this study,

significant correlations were found when all samples were used. However, the

31

correlation disappeared when samples were separated into two size categories - large

(15-27 em) and small (11-14 em) - and statistical analyses run.

3.4.4 Main Variables and Proximate Content

When comparing the main variables of lot, location, sex, and size to proximate

content, significant differences were found with lot and location (p<O.OOl). For lots,

differences were seen mainly between A and B, and C and D. Lots A, Band C were

caught in Canada and Lot D was caught in Alaska. Lots A, Band C were caught in

different years. This may be a reason for the significant differences seen between the

lots. It has been found in previous studies that herring from the same location, but

different year ofcatch had significantly different lipid content (Anthony et al., 2000).

For locations, significant differences were seen with all components except CHO.

Similar results were seen in a study done by Anthony et al., in 2000. In their study they

found a variation in lipid content between locations within Prince William Sound,

Alaska, where the herring in the northeast had a higher lipid content than those caught in

the southwest. They also saw a difference in lipid content between Prince William Sound

and the Lower Cook Inlet, Alaska. These yearly and regional differences could be due in

part to differences in prey availability (Blaxter and Holiday, 1963).

3.4.5 Proximate Content

As found in previous studies, certain components are correlated to others. One

that has been noted is the positive correlation between lipid content and gross energy

(kcallg). In this study, the relationship exists on both the dry matter and as-fed basis

32

(R2==0.97 for both). This is most likely due to the fact that gross energy is made up

mainly by energy from lipid (lipid content is multiplied by 9.4 in the GE equation). This

type ofcorrelation exists, to lesser degrees, between other nutrients. All components

were regressed on both the dry matter and as-fed basis. A couple of those run on the dry

matter basis resulted in higher R2values when run on the as-fed basis. These were lipid

and moisture (R2==0.88) and moisture and gross energy (R2==0.96).

Due to the fact that the regression equations and R2values were greater than 0.60,

it was proposed that these equations could be used to calculate the proximate content of a

herring without the need for a laboratory, chemicals or even morphometric

measurements. This method has been mentioned in other studies as a plausible way to

calculate, for example, gross energy from the moisture content ofherring (Anthony et al.,

2000).

The linear regression equations with the highest R2value for each component

were determined. Moisture content was chosen as the starting point due to the ease of

obtaining this data. Once moisture content is obtained (i.e. with a drying or microwave

oven) it is entered into the following regression formula (R2==O.96):

GE (kcal/g) on an as-Jed basis = 8.59 - 0.09 *Moisture

Once gross energy is obtained, it can be used to calculate both lipid (as-fed basis) and ash

(dry matter basis). For lipid content, the following equation (R2==0.97) is used:

Lipid content (% - as-Jed basis) = -9.82 + 10.76 x GE (kcal/g - as-Jed basis)

For ash, the gross energy content can be converted to dry matter basis using the

calculated dry matter percentage (dry matter == 100%-moisture %). The following

33

equation would then be used to calculate ash on the dry matter basis (R2=O.72) (Figure

3.12):

Ash content (% - DM basis) = 25.55 - 2.73 x GE (kcal/g - DM basis)

Using the previously calculated lipid content (as-fed basis), protein can be calculated.

The lipid content first needs to be converted to the dry matter basis using the same

method as described above for gross energy. Once this is done, the following equation

(~=O.68) can be used to calculate protein content (dry matter basis) (Figure 3.13):

Protein content (% - DM basis) = 75.939 - 0.70 x lipid (% - DM basis)

This proposed use of the above linear regression equations may not be the most

accurate way of obtaining the nutrient content ofa herring. These equations were built

on a sample size of99 herring. The samples used herein were as representative as

possible of the range in lipid content ofPacific herring. By no means does the data in

this study make up a comprehensive database of herring nutrient content. Further data

must be included in this database and the regressions run again to hopefully obtain an

even higher R2 value, and therefore better prediction equations. The predictability of

these equations was tested on both Pacific herring, as well as Atlantic herring (Clupea

harengus). Equations were used to predict the proximate composition of a known group

of herring from a previous study. Results indicate a fairly good level of predictability for

both Pacific and Atlantic herring.

3.5 CONCLUSION

A main objective of this study was to determine the impact of source and sex on

the nutrient composition ofherring. From the results of the analyses done herein, there

34

exists a definite impact of source (or location ofcatch) on the proximate content of

Pacific herring. All components (protein, lipid, ash, moisture, and gross energy) showed

significant differences between the Canada and Alaska locations, except for CHO. These

results were similar to those found in other studies. However, there was no significant

effect of sex on the proximate content ofthese herring. This may be due to the lack of

sufficient sample representation ofeach sex category.

The second objective of this study was to determine if morphometric

measurements could be used to predict nutrient content ofherring. There were no

significant correlations between morphometries and nutrient content, therefore these

measurements, according to the results found herein, would not yield an accurate

prediction ofproximate content. However, when regressions were run comparing

components, significant correlations were found. It is proposed that these equations (R2

values=O.68-0.97) could be used, given the percentage ofmoisture, to predict the protein,

lipid, ash and gross energy content ofherring.

35

Table 3.1 Average, standard deviation and minimum and maximum values formorphometric measurement results by lot and location for Pacific herring.

Length(em)

Volume. VolIL(ml) Weight (g) (mllem)

WtlL(g/cm)

WN (g/ml)

Lot A Avg 18.34 86.90 86.03 4.55 4.49 0.98

StDev 2.64 36.44 38.46 1.39 1.51 0.10

Min 14.00 30.00 27.22 2.07 1.81 0.68

Max 23.50 145.00 145.15 6.36 6.60 1.32LotS Avg 18.80 93.04 93.31 4.74 4.73 0.99

StDev 2.76 40.90 42.68 1.57 1.69 0.10

Min 14.00 30.00 27.22 2.14 1.88 0.78

Max 22.50 170.00 154.22 7.56 7.01 1.21LotC Avg 18.52 81.36 85.15 4.37 4.57 1.05

StDev 1.15 14.97 16.56 0.58 0.66 0.07

Min 17.00 55.00 54.43 3.14 3.20 0.91

Max 20.50 110.00 113.40 5.50 5.82 1.17Lot D Avg 17.66 72.75 73.77 4.05 4.10 1.01

(Alaska) StDev 1.68 21.01 21.12 0.86 0.88 0.09

Min 14.00 35.00 31.28 2.50 2.23 0.85

Max 20.00 105.00 98.95 5.41 5.21 1.22Lots ASC Avg 18.56 87.53 88.36 4.57 4.60 1.00(Canada) StDev 2.35 33.73 35.34 1.29 1.39 0.10

Min 14.00 30.00 27.22 2.07 1.81 0.68

Max 23.50 170.00 154.22 7.56 7.01 1.32OVERALL Avg 18.38 84.55 85.42 4.46 4.50 0.68

StDev 2.25 32.04 33.40 1.23 1.31 1.32

Min 14.00 30.00 27.22 2.07 1.81 1.00

Max 23.50 170.00 154.22 7.56 7.01 0.10

36

Table 3.2 Linear regression relationships between morphometric measurements forPacific herring, including the R2 value and Root Mean Square Error (RMSE).

Morphometric measurement n Regression equation R2 RMSE

Lot A Length (em) vs. Vol (ml) L=12.22+0.07V 0.95 0.62Length (em) vs. Wt (g) L=12.63+0.07W 0.93 0.68Vol (ml) vs. Wt (g) 29 V=6.37+0.94W 0.98 5.71Wt (g) vs. VlL (ml/em) W=-36.03+26.80(V/L) 0.94 8.65Vol (ml) vs. W/L (g/em) V=-18.70+23.50(W/L) 0.94 9.49Length (em) vs. WN (g/ml) L=11.54+6.94(W1V) 0.04 2.59

Lot B Length (em) vs. Vol (ml) L=12.75+0.07V 0.93 0.74Length (em) vs. Wt (g) L=12.95+0.06W 0.94 0.68Vol (ml) vs. Wt (g)

28V=4.83+0.95W 0.97 6.83

Wt (g) vs. V/L (ml/em) W=-33.46+26.77(V/L) 0.96 9.20Vol (ml) vs. W/L (g/em) V=-18.93+23.66(W/L) 0.95 8.31Length (em) vs. WN (g/ml) L=6.25+12.69(W1V) 0.17 2.51

LotC Length (em) vs. Vol (ml) L=13.05+0.07V 0.76 0.57Length (em) vs. Wt (g) L=13.32+0.06W 0.77 0.56Vol (ml) vs. Wt (g)

22V=9.13+0.85W 0.87 5.32

Wt (g) vs. VlL (mllem) W=-25.12+25.25(V/L) 0.78 6.92Vol (ml) vs. W/L (g/em) V=-11.01 +20.22(W/L) 0.79 7.74Length (em) vs. WN (g/ml) L=12.43+1.33(W1V) 0.57 0.76

Lot 0 Length (em) vs. Vol (m1) L=12.24+0.07V 0.86 0.64(Alaska) Length (em) vs. Wt (g) L=11.98+0.08W 0.93 0.44

Vol (ml) vs. Wt (g)20

V=2.31+0.95W 0.92 6.03Wt (g) vs. VlL (ml/em) W=-19.15+22.94(V/L) 0.87 7.26Vol (ml) vs. W/L (g/em) V=-19.73+22.55(W/L) 0.88 7.77Length (em) vs. WN (g/ml) L=12.51 +5.09(W1V) 0.03 1.66

Lots ABC Length (em) vs. Vol (ml) L=12.71+0.07V 0.92 0.68(Canada) Length (em) vs. Wt (g) L=12.90+0.06W 0.93 0.65

Vol (ml) vs. Wt (g)79

V=4.65+0.94W 0.97 6.26Wt (g) vs. VlL (ml/em) W=-33.30+26.64(V/L) 0.94 8.96Vol (ml) vs. W/L (g/em) V=-20.21 +23.42(W/L) 0.93 8.58Length (em) vs. WN (g/ml) L=1 0.87+7.67(W1V) 0.09 2.24

Overall Length (em) vs. Vol (ml) L=12.70+0.07V 0.91 0.67Length (em) vs. Wt (g) L=12.82+0.06W 0.93 0.62Vol (ml) vs. Wt (g)

99V=4.12+0.94W 0.96 0.17

Wt (g) vs. VlL (ml/em) W=-32.14+26.34(V/L) 0.94 8.45Vol (ml) vs. W/L (g/em) V=-21.20+23.51 (W/L) 0.93 8.67Length (em) vs. WN (g/ml) L=11.29+7.04(W1V) 8.10 2.16

37

Table 3.3 Average, standard deviation and minimum and maximum values for nutrientcomponent results ofPacific herring - overall and by lots and locations.

LOT

Protein % EE % Ash % CHO% Moisture % GE(kcal/g)

A Avg 52.33 a 31.23 c 8.44 a 7.18 a 72.40 a 6.25 c

(n =29) StDev 4.99 5.51 1.13 2.55 2.84 0.28

Min 39.01 20.66 6.05 2.71 67.94 5.48

Max 64.08 40.32 10.88 11.71 80.26 6.71

B Avg 54.52 a 35.53 b 7.71 b 2.49 c 71.03 a 6.55 b

(n =28) StDev 4.64 4.54 0.99 2.38 1.95 0.22

Min 45.89 24.10 5.92 0.00 68.32 5.98

Max 63.83 42.56 10.02 8.96 75.72 6.93

C Avg 44.43 b 43.39 a 6.92 c 5.26 b 68.24 b 6.83 a

(n =22) StDev 5.21 6.36 1.05 1.29 2.67 0.30

Min 37.02 27.50 5.47 3.06 63.29 6.08

Max 59.11 51.85 9.18 8.56 74.60 7.220 Avg 42.80 b 45.73 a 6.57 c 5.11 cfb 66.26 b 6.95 a

(n =20) StDev 3.38 5.57 0.70 3.37 3.26 0.30

Min 35.90 35.55 5.40 0.00 59.88 6.43

Max 48.59 56.81 8.08 11.55 71.93 7.60

LOCATION

Alaska Avg 42.80 b 45.73 a 6.57 b 5.11 66.26 b 6.95 a

(n =20) StDev 3.38 5.57 0.70 3.37 3.26 0.30Min 35.90 35.55 5.40 0.00 59.88 6.43Max 48.59 56.81 8.08 11.55 71.93 7.60

Canada Avg 50.91 a 36.14 b 7.76 a 4.95 70.76 a 6.52 b

(n =79) StDev 6.40 7.27 1.21 2.96 2.99 0.35Min 37.02 20.66 5.47 0.00 63.29 5.48

Max 64.08 51.85 10.88 11.71 80.26 7.22

OVERALL

(n =99) Avg 49.27 38.08 7.52 4.99 69.85 6.60StDev 6.75 7.94 1.22 3.03 3.53 0.38

Min 35.90 20.66 5.40 0.00 59.88 5.48

Max 64.08 56.81 10.88 11.71 80.26 7.52a, , C indicate significant differences (P<O.001) between means within lots and within locations for eachnutrient.

38

Table 3.4 Average, standard deviation and minimum and maximum values for nutrientcomponents ofPacific herring by size and sex (P >0.05 for all).

SIZE

Protein % EE% Ash % CHO%Moisture GE

% (kcallg)

Small Avg 48.58 38.23 7.58 5.70 70.53 6.60

StDev 6.91 7.46 1.29 3.24 3.43 0.35

Min 38.43 20.66 5.92 0.00 65.56 5.77

Max 64.08 49.63 10.88 11.71 80.26 7.11Medium Avg 49.88 36.59 7.76 5.79 69.99 6.52

StDev 5.43 6.95 1.03 2.57 2.94 0.33

Min 38.29 23.31 5.69 0.00 63.33 5.92

Max 58.46 50.68 9.46 9.70 74.60 7.15Large Avg 49.49 38.82 7.32 4.56 69.20 6.66

StDev 7.38 8.88 1.26 5.11 3.88 0.44

Min 35.90 21.00 5.40 0.00 59.88 5.48

Max 63.83 56.81 10.07 31.30 77.42 7.60SEX

Male Avg 45.23 43.80 6.92 4.16 67.84 6.87

StDev 5.72 7.04 0.99 2.31 3.36 0.34

Min 35.90 27.50 5.40 0.00 59.88 6.08

Max 59.11 56.81 9.18 8.56 74.60 7.60Female Avg 43.35 43.87 6.81 6.10 67.02 6.84

w/out eggs StDev 3.86 4.99 0.71 3.05 2.95 0.26

Min 38.29 35.18 5.69 0.00 61.30 6.45

Max 51.72 54.38 8.27 11.55 71.93 7.48Female Avg 46.29 41.19 7.04 5.49 68.86 6.74wI eggs StDev 4.20 5.25 0.98 1.68 2.21 0.25

Min 37.02 34.19 5.47 1.88 63.29 6.39

Max 51.75 51.85 8.95 8.96 71.65 7.22

39

Table 3.5 Linear regression equations and R2 values (>6QO.Io) nutrient componentrelationships in Pacific herring analyte correlations (n=99, P<O.OOI).

Nutrient content Regression equation R2

Dry Matter Basis

Ash vs GE (k Ash =25.55 - 2.73 * GE 0.72

P ein (EE) Prot =75.93 - 0.70 * EE 0.68

Protein vs. Moisture Prot =-58.42 + 1.54 * Moist 0.65

lipid (EE) VS. Ash EE =78.32- 5.35 * Ash 0.68

lipid (EE) vs. Moisture EE =173.29 - 1.94 * Moist 0.74

lipid (EE) VS. GE (kcaVg) EE =-98.11 + 20.64 * GE 0.97

Ash vs. Moisture Ash =-11.51 + 0.27 * Moist 0.62

Moisture vs. GE (kcaVg) Moist =120.93 -7.74 * GE 0.69

As-Fed Basis

Moisture ) Moist = 90.23 -10.18 *GE 0.96

lipid (EE) g} EE = - 9.82 + 10.76 * GE 0.97

lipid (EE) ¥s. Moisture EE = 80.69 - 0.99 * Moist 0.88Shaded areas indicate "best fit" regression equation to use for predicting nutrient content,based on r-squared value.

Table 3.6 Sequence oflinear regression equations used to predict energy density (GE(kcallg», lipid (%), ash (%), and protein (%) content ofPacific herring starting frommoisture content.

Nutrient contents Basis Fonnula

to calc. GE

to calc. lipid

convert to lipid (Dftf)

convert to GE (Dftf)

moisture

GE

lipid (as fed)

GE(asfed)

GE

lipid

to calc.

to calc.

ash

protein

8S fed GE =8.59 - 0.09 * Moist

as fed EE =-9.82 + 10.76 * GE

OM lipid (OM) = Hpid (as fed) / ((1~Moist)/1()())

OM GE (OM) =GE (as fed) / ((1~st)/1()())

OM Ash =25.55 - 2.73 * GE

OM Prot =75.93 - 0.70 * EE

40

Table 3.7 Calculated proximate composition of samples from a previous study (usinglinear regression equations to predict analyte content) compared to known chemicalreference values.

PacificHerring

AtlanticHerring

AVG Overall

SO +\-

AVG Pacific

SO +\-

AVG Atlantic

SO+\-

% Prot %EE %

41

% Ash GE(kalllg) (kcaUg)

(Chem)

6.65

6.66

6.63

6.66

6.66

6.46

6.99

6.90

7.12

5.59

5.59

5.42

5.99

5.81

5.89

5.56

5.61

5.92

6.23

0.57

6.75

0.21

5.71

0.20

Table 3.8 Linear regression R2 values, means and standard deviations (SD) forcalculated (using linear regression equations to predict analyte content) and knownchemical reference results.

Calculated ChemicalR2 Mean +1-50 Mean +1-50

OVERALL - Pacific and Atlantic combinedProtein 0.98 56.06 +/- 8.80 61.67 +/- 11.72Lipid 0.97 28.38 +/- 12.57 28.14 +/- 12.60Ash 0.65 8.77 +/- 1.64 8.48 +/- 2.29Gross energy (kcal/g) 0.94 6.14 +/- 0.60 6.23 +/- 0.57Pacific herring

Protein 0.93 47.77 +/- 2.78 50.71 +/- 3.07Lipid 0.89 40.21 +1- 3.98 36.69 +1- 4.89

Ash 0.04 7.23 +/- 0.52 6.52 +/- 0.31Gross energy (kcal/g) 0.85 6.70 +1- 0.19 6.75 +/- 0.21

Atlantic herring

Protein 0.64 64.34 +/- 1.60 72.62 +/- 3.49Lipid 0.47 16.56 +/- 2.28 16.59 +/- 3.66Ash 0.02 10.31 +1- 0.30 10.44 +1-1.56

Gross energy (kcal/g) 0.11 5.58 +/- 0.11 5.71 +/- 0.20

42

Figure 3.2 Map ofAlaska, United States. Pacific herring harvest area is circled.

43

Regression PlotlBG1H. 10.61'lI2 + 0.119262 \IQ.l.aE

-0.CXXJ2&23~5 • 0.582332 R&r" 93.5 .. R-Sq(act) .. 93.3 ..

24

23

22- 215- 20

.r:.- 190)C

18Q)-J

17

16

15

14

20 70 120

Volume (ml)170

Figure 3.3 Quadratic regression fitted line plot for volume (ml) versus length (em) ofPacific herring.

Regression PlotI.EN31H .. 11.4041 + 0.102fD1~GfT

• 0.lDl2153~Gfl"*2

5 .. 0.5l57016 R&r. 94.0" R-Sq(att .. 93.9 '"

24

23

22- 215- 20

.r:.- 190)CQ) 18-J

17

16

15

14

WeiQht(Q)160

•

Figure 3.4 Quadratic regression fitted line plot for weight (g) versus length (em) ofPacific herring.

44

•

170

Regression PlotYa..LM: .. 10.32516 +0.77f1!IJ7 WEJG{T

+ O.<XXB3lI9 VIt'ElGir"25 .. 6.005151 R-Sq .. 96.5 ... R-Sq(.q) .. 96.4 ...

•

=-E-Q)

E:::J

~

12:)

70

2:) --. ---,. ----.-----'

50 100Weight (g)

150

Figure 3.5 Quadratic regression fitted line plot for weight (g) versus volume (ml) ofPacific herring

Regression PlotVCLLM: .. 10.3227 + 7.84236 WWI...erVh

+ 1.775>11N111sVh"25 .. 7.94712 R-Sq .. 94.0'" R-Sq(~ .. 93.8 ...

170

=-E 12:)-Q)

E:::J"6> 70

•

2:)-....--r---,------,----,----.----_.---'

2 3 458'MILength (glan)

7

Figure 3.6 Quadratic regression fitted line plot for weight per unit length (g/cm) versusvolume (ml) ofPacific herring.

45

Regression PlotW8<JiT= -27.3lII58 + 24.03ZS~

+Cl.2558!l3V~S .. 8.47915 ~ .. 93.7 'lI. R-S«-. =93.6 'lI.

70

170

§ 120

......c0)

~

2O--,,__-.--_---,__-.--_--.__-.--_---y-I

2 3 .. 5 6 7VoIILength (milan)

6

Figure 3.7 Quadratic regression fitted line plot for volume per unit length (mlIcm) versusweight (g) ofPacific herring.

Regression PlotLngIl (em) .. -41.4845 + 113.lB> WN (~rrf)

• 52.7464 WN ~rrf)"2

S = 1.978» R-Sq .. 24.5~ R-S«acI" 23.0 ..

••

•

• •••• •

I

•

••• •••• ••. .. ... -. .-----.....•

24

22

- 20

5-- 16.c.....0)C 16CD •....J

14

12

10

07

WN(glml)

Figure 3.8 Quadratic regression fitted line plot for weight per unit volume (g1ml) versuslength (cm) ofPacific herring.

46

Morphometric A\lerages by Sex

90.00 +-----------~-------.__ F---l

80.00 1-----------1

70.00 f------------.--+-;

• 60.00 +----------1­a­I! 50.00 f------------I~__l•~ 40.00 f------------I~_,

30.00+---------~9

20.00 t---:=;;;;;::::::;:::=-O=:;:::;;;;;;;;;""1--I~~

10.00

0.00L.erVh (em) Vok.me(rri)

Morphometric measurement

~tt(g)

Iii! Female o Male ID Female wi eggs I• Denotes significant difference ofP<O.05.

Figure 3.9 Bar graph showing mean length, volume and weight for Pacific herring sexcategories of female, male and female with eggs.

Morphometric Averages by Sex

6.00

5.00

*4.00

•iI 3.00or:(

2.00

1.00

0.00VII.. (mlIcm) Wll.~)

Morphometric rneuurement

WN(~

III Female o Male IIFemale wi eggs I• Denotes significant difference ofP<O.05.

Figure 3.10 Bar graph showing mean volume per unit length (mVcm), weight per unitlength (glcm) and density (glml) for Pacific herring sex categories -offemale, male andfemale with eggs.

47

Morphometric Averages by Size

•120 t---------------"-.-------.......----.--1

100 +--------------t•

t 80

~ 60-f---------...........-f

40+----------

20

Length (an) VoIl.me (mI)

MorphometrIc measurement

II Small 0 Medium

• Denotes significant difference ofP<O.05.

Weight (g)

IDLarge

Figure 3.11 Bar graph showing mean length, volume and weight for Pacific herring sizecategories ofsmall, medium and large.

Morphometric Averages by Size

7

6

5IIal4l!•~ 3

2

o

......-:E- r--W-. .

~ ---:-.. ..

r----~ ~-,--.",

~

== - -'-, ~

-----;;;;;;;

r---- := ~

~ I r= -~....z;

VIL (milan) WIL~) WN~

Morphometrtc meuurwnent

B Small 0 Medium II Large

• Denotes significant difference ofP<O.05.

Figure 3.12 Bar graph showing volume per unit length (mVcm), weight per unit length(g1cm) and density (g1ml) for Pacific herring size categories ofsmall, medium and large.

48

Regression PlotGE (lara) • 8.58B74- o.lBOD4ati*n ..

S .. 0.al82382 R-Sq .. 96.0.. R-Sq(" =96.0 ..

3 ••

-.P}

~ 2-w(!)

Moisture %

Figure 3.13 Linear regression plot ofmoisture versus gross energy (GE) content on an asfed basis (R2=O.96) for Pacific herring.

Regression PlotEE .... -9.82{5l + 10.7!i94 GE (kca'g)

S .. 0.637754 R-Sq" 97.1 .. R-Sq(8lI .. 97.0 ..

25

"*' 15WW

5

2

GE (kcallg)3

Figure 3.14 Linear regression plot ofgross energy (GE) versus crude lipid (EE%)content on an as fed basis (R2=0.97) for Pacific herring.

49

Regression Plot/WI ..=25.5511 - 2 nr:o (£ (lafg)

S =QeB2303 R-Sq =71.8" R-Sltact =71.5"

11

10

9

;j.~

8enc(

7

6

5

55

••. .

• •.. .••• ••. .. , . .. .

6.5

GE (kcallg)75

Figure 3.15 Linear regression plot ofgross energy (GE) versus ash content on a drymatter basis (R2=O.72) for Pacific herring.

Regression PlotProBn"" • 75.9329· 0.7OO28ll EE ..

S:z: 3.84511) RSq .. 67.9 "" R-S«-e .. 67.5 ""

1lO40

EE%3020

35 ~---_,~--_,._---~---r'

65 -r----------------,

55'#.c:jeQ.. 45

Figure 3.16 Linear regression plot ofcrude lipid (EE) versus protein content on a drymatter basis (R2=O.68) for Pacific herring.

50

CHAPTER 4. UTILIZATION OF VISIBLE AND SHORT-WAVELENGTH NEAR­INFRARED REFLECTANCE SPECTROSCOPY TO PREDICT THE PROXIMATECONTENT OF WHOLE, HOMOGENIZED PACIFIC HERRING (Clupea pallasi)

4.1 INTRODUCTION

4.1.1 Diet Assessment in the Marine Mammal Field

Numerous marine parks in the United States house and exhibit marine mammals

such as Steller sea lions, Hawaiian monk seals and Harbor seals. One of the main

components of the diets of these animals is herring. The quantity ofherring fed at any

given time depends upon the proximate analysis results ofthe herring being fed. Other

commonly fed species offish include capelin (Mallotus villosus) and pollock (Theragra

chalcogramma). The main species ofherring fed in marine parks located on the West

Coast (including Hawaii and Alaska) is Pacific herring (Clupea pallasi).

In most marine mammal facilities, diet assessment is a key part of daily

husbandry. In a facility that houses an endangered or threatened species of marine

mammal, diet analysis is more critical. Diet analysis becomes increasingly more

important ifvital research is being performed using the animals at the facility. Having

marine mammals in captivity facilitates needed research on the behavior, physiology and

nutrition of marine mammals. Many times, one or more components of a study will rely

upon accurate diet analysis.

For this analysis, samples are usually sent to an outside laboratory. Nutrient

analyses are run to determine crude protein, crude lipid, total mineral and moisture

content. These analyses can be costly and results can take a week or more to obtain.

51

4.1.2 Near Infrared Reflectance Spectroscopy

Near Infrared (NIR) Reflectance spectroscopy is an expanding technology which

has recently grown in popularity. In the livestock feed and forage industry, it has been

widely used and well accepted for the prediction ofnutrient composition used in diet

analysis (Williams and Norris, 1987). This technology has the ability to provide both

quantitative and qualitative information on a wide array of products (especially raw

materials) in a matter of minutes (Burns and Ciurczak, 1992).

In this study visible and short-wavelength near-infrared (SW-NIR) reflectance

was used. This spectroscopy uses wavelengths from 600 to 1100nm. SW-NIR

spectroscopy yields good quantitative results, especially for higWy scattering samples

(Phelan et ai., 1989; Cavinato et al., 1990). This technology also permits the remote

collection ofa full spectrum with a fiber optic probe (Lee et ai., 1992; Huang et ai.,

2000). There are, however, some limitations ofSW-NIR. SW-NIR should be used to

predict analytes that are at concentrations greater than one percent, due to lower

sensitivity. This type of spectroscopy also has a limited scope of functional groups as

well as a tendency for absorption bands to overlap. When this happens, one or more

interfering peaks may overlap a peak of interest. Because of this, complicated calibration

models are sometimes required (Lee et ai., 1992; Huang et ai., 2000).

4.1.3 NIR Spectroscopy and the Fish Industry

The use of NIR spectroscopy in the fish industry has grown in recent years. This

technology is used mainly for quality control purposes (i.e., in cultured salmonids)

leading to a higher quality of fish in the market. Recent studies looked at minimizing

52

sample preparation time by scanning whole, intact fish. In a study done by Downey

(1996), NIR spectroscopy (700-11 OOnm) was used to predict the moisture and oil content

offarmed salmon. Scanning was done through the skin on intact fish. R2 values in this

study were fairly similar to those seen herein. They ranged from 0.69-0.77 for moisture

and 0.70-0.74 for lipid content. Intact fish were also scanned in studies done by Lee et

al. (1992), Isaksson et al. (1995) and Wold and Isaksson (1997). In some studies, sample

preparation was minimized, but not removed completely. In one study by Rasco et al.

(1991), cross sections of frozen plugs taken from cultured rainbow trout were scanned.

This method was able to predict, with fairly good accuracy, fat, moisture and protein

content. In another study done by Wold et al. (1996), cylinders of intact tissue were

taken from various sites on the bodies of ten farmed salmon. The R2 results for fat

prediction were greater than 0.90 for all sites except one (0.44 for the plug containing

skin and dark muscle). Other studies went further in preparing samples, homogenizing or

grinding them prior to spectra collection. One such study was done by Sollid and Solberg

(1992), who ground cultured Atlantic salmon fillets before scanning. This study used

NIR spectroscopy (850-1050 nm) to predict the fat content ofground salmon. Prediction

results were exceptional with an R2 value of0.98. Ground fish were also scanned in

studies done by Isaksson et al. (1995), Valdes et al. (1997), and Zhang and Lee (1997).

In a study done by Darwish et al. (1989), fish were processed and ground to milk-like

consistency prior to analysis via mid-infrared transmittance spectroscopy. The method of

sample preparation used in this study was extensive and time consuming.

The objective of this project was to use visible and short-wavelength near-infrared

(SW-NIR) reflectance spectroscopy to develop chemometric calibration models for the

53

prediction of fat, protein, moisture and mineral content ofwhole homogenized Pacific

herring (Clupea pallasi).

4.2 MATERIALS AND METHODS

4.2.1 Sample Preparation

The Pacific herring samples (n = 86) used were from two fisheries - North Bay

Meat Company which sells herring caught in British Columbia, Canada (lots A, B, and

C), and Petersburg Fisheries in Alaska (lot D). Lots represent separate seasonal catch

dates within a three-year span (1999-2001). Specific year and/or season data were not

available. The Marine Mammal Research Program ofthe University ofHawaii donated

the North Bay herring. Samples for those lots were prepared at their fish preparation

facility at Kaneohe Marine Base Headquarters, Hawaii. The Alaska herring was donated

by the Alaska SeaLife Center in Seward, Alaska. All samples from the fisheries came

individually quick-frozen. Samples were randomly selected using the methods described

in the previous chapter, and shipped, still in the frozen state, to Hawaii via overnight

FedEx. Lots were stored at -80°C. Samples were prepared using the methodology

described in chapter three.

To prepare samples, the herring were first thawed in a cool fresh water bath, then

put into a clean stainless steel pan. Herring were homogenized in a glass container with a

pouring lip, using a 12-speed Oster blender with a 450-watt motor. The blending process

for each fish took approximately 10min. since time was spent scrapping down the sides

of the container as blender speed was increased from "chop," to blend and finally to

"mix." The thorougWy mixed samples were then scooped out with a stainless steel spoon

54

and placed into 50 ml disposable sterile centrifuge tubes. Whole herring were

homogenized because the nutrient make-up of the entire fish was needed. Since marine

mammals are fed the entire fish. The sample tubes were stored at -80°C.

4.2.2 Reference Analysis

Chemical proximate analysis was conducted at the University ofHawaii Animal

Science nutrition laboratory. Tests performed were those to determine moisture, ash,

crude protein and crude fat content of the herring. Standard methods of the Association

ofOfficial Analytical Chemists (1990) for meat were used (Ellis, 1984). Duplicate

analyses were run for each component. To determine moisture content, samples were

placed in ceramic crucibles and dried for 24 hours in a 100°C drying oven. Ash content

was determined on dried samples by combustion in an electric muffle furnace for 8 hours

at 650°C. Crude protein content was determined using a modified Kjeldahl nitrogen

analysis (see chapter three for more specifics). Crude fat content was determined using

samples dried at 50°C for 48 hours prior to analysis using the Goldfisch ether extract

analysis method.

4.2.3 Spectra Collection

All homogenized herring samples were thawed to room temperature on a

laboratory bench with an ambient temperature ofapproximately 21°C. The temperature

ofall samples was kept constant allowing for optimal NIR scanning. Samples were

thorougWy mixed, and approximately 5 g sub-samples were taken from each sample tube.

This sub-sample was then placed in direct contact with the detector on the NIR probe.

55

This amount of sample covered the entire detection window ofthe detector. Once placed

on the detector, the top of each sample was smoothed over to ensure that no air pockets

existed between the detector and sample. Visible and short-wavelength near-infrared

reflectance (SW-NIR) spectra were recorded in diffuse reflectance mode with a GMS

spectrophotometer (Textron Systems, Wilmington, MA). Spectra were collected from

600 to 1100 nm at 0.5 nm intervals. Each spectrum was the average of20 scans with

30ms exposure time for each scan. The detector was wiped and cleaned thoroughly with

distilled water between samples. Three replicate scans were carried out for each sample.

4.2.4 Statistical Analysis

Spectra were scanned using the D2TSC 3.2 software (Figure 4.1). The spectral

data was analyzed using DeLight 3.2 software (DSquared Development, Inc., La Grande,

OR). This software is designed for chemometric analyses. The spectra were transformed

using the pre-processing algorithms ofbinning, smoothing, and second derivative

transformation (Figure 4.2). Binning is the averaging of a number of points into one.

This increases the signal to noise ratio by a factor of~(n). Smoothing takes the value of

each point and replaces it by the mean of the values in the interval surrounding it.

Smoothing enhances the signal to noise ratio without decreasing the spectral resolution.

Second derivative transformation removes baseline shifts and is able to separate out

overlapping absorption bands. It also minimizes scatter effects due to differences in

particle size (Valdes et al., 1997). First, spectral data were binned by 2 nm and smoothed

with a Gaussian function over 12 nm. Then a second derivative transformation with a 12

nm gap was calculated (Lin et al., 2003 a, b).

56

Regression equations (calibration models) are then calculated based on the NIR

spectra obtained and the known analyte information. The model was used to predict

nutrient composition ofunknown samples. Multiple Linear Regression (MLR) and

Partial Least Squares (PLS) statistics are most commonly used for linear calibration

models. In this study, calibrations were developed using the full NIR spectrum by

applying a PLS regression method to statistically analyze the data. The PLS method uses

full spectrum analysis (Haaland and Thomas, 1988). Initially, the calibration model was

built using one to ten latent variables for each sample component. This was done for

each lot (A-D), and then by location (Canada and Alaska). The software calculated the

R2 value and the standard error ofprediction (SEP) for each regression equation. The

standard error of prediction is indicative of the predictive performance of calibration

models (Lin et at., 2003). The best-fit calibration model was then determined by

graphing the number of latent variables by the SEP using Microsoft Excel 2000. The

next step was to test the validity and the prediction accuracy of the calibration models

using cross validation. In this study, leave-one-out cross-validation was run, with the

software, using the best-fit calibration model to predict each nutrient. In this type of

cross validation, one sample is left out while the rest are used to predict the analyte of

interest within that sample. An advantage ofcross validation is that the sample is not

included in the calibration model and therefore, the model can be tested independently

(Murray and Cowe, 1992). This method ofvalidation also allows for the maximum use

ofall samples. Instead ofdividing the available samples into calibration and validation

sets, all samples are used in both sets with leave-one-out cross validation. This is

especially helpful when sample numbers are limited.

57

4.3 RESULTS

4.3. 1 Reference Results

Calibration models were created for each nutrient or chemical component in five

categories: lots A through D separately and lots A, B, and C combined. The combination

oflots A through C represent fish from the North Bay Meat Company (Canada) and lot D

represents fish from Petersburg Fisheries (Alaska).

First, location was analyzed to determine whether sample composition between

locations were different enough to warrant separate calibration models. Mean values

were compared between the Canada and Alaska herring. Values for each location were

found to be statistically different with p-values less than or equal to 0.001 (Figure 4.3).

For example, lipid content between locations showed a variation in range with herring

from Canada with 23.3-51.9% lipid and those from Alaska, 35.6-56.8% lipid.

Lots were also analyzed to determine whether nutrient contents between them

were statistically significant. Mean nutrient values between lots A through D were

significantly different with p-values less than 0.001 (Figure 4.4). Differences between

lots A through C and lot D were not seen. Therefore, it can be inferred that although lots

are somewhat nested within location, the effects of each on sample composition are

separate. For calibration modeling purposes, the more feasible of the two to use would

be location, a category with a broader range than lot.

Linear regressions were run on all the chemical constituents to determine whether

or not correlations existed between them (Table 4.1). Only adjusted R2 values are cited

herein. These values represent R2 values adjusted for degrees of freedom. There was a

negative correlation found between moisture and gross energy (GE) on a dry matter basis

58

(R2=0.69). When the as-fed data were used, the resulting R2 value increased to 0.96

(Figure 4.5). There was a highly significant positive correlation (R2=0.97) between lipid

and gross energy content on a dry matter basis. When the regression was run using an as­

fed basis, the correlation remained the same with an R2 value of 0.97 (Figure 4.6). On

the dry matter basis, ash was negatively correlated with gross energy, with an R2 value of

0.72 (Figure 4.7). Based on an R2 value of0.68, protein was best correlated with lipid on

a dry matter basis (Figure 4.8). These linear regression equations could be used to later

calculate gross energy (GE (kcal/g)), lipid, ash, and protein content based on a fairly easy

to obtain moisture content (Table 4.2).

4.3.2 SW-NIR Spectral Results

Calibration models were established and selected with the optimal number of

latent variables. The latent variables (x-axis) were graphed against the Standard Error of

Prediction (SEP) (y-axis) for each component. Graphs for protein and lipid can be seen

in Figures 4.9-4.14. These graphs were used to select the best-fit calibration model for

each component. Usually, the lowest point on the line was chosen as the best-fit

calibration model. This point indicated the optimal number of latent variables included

in the calibration equation, as well as the lowest SEP (in most cases). The number of

latent variables chosen for sample constituents in this study ranged from five to eight.

Table 4.3 shows the number oflatent variables and the SEP for the calibration models

chosen for each chemical component for alilotsllocations.

The mean nutrient content predicted using the SW-NIR calibration models chosen

(predicted) can be seen in Table 4.4 along with the mean results obtained with the

59

reference method (chemical/analytical). When linear regression analyses were done for

all components comparing the predicted and analytical results by both lot and location, R2

values ranged from 0.24-0.93. When looking at lots A through D, lot A had, on average,

the lowest R2 values for protein, lipid, ash, dry matter and moisture. However, lot Chad,

overall, the highest R2 values for components. The best positive correlation was found in

lipid content in lot C with an R2 value of0.93 (Figure 4.15). Similarly, the correlation in

ash content between the chemical and NIR predicted data in lot D, or Petersburg (Alaska)

herring, was fairly high, with an R2 value of0.91 (Figure 4.16).

When the R2 values for locations were compared, the calibration for Alaska

herring was, on average, better than that ofthe British Columbia herring. For Alaska

herring, R2 values ranged from 0.66-0.91, for British Columbia, from 0.47-0.81. These

low R2 values were most likely due to the inclusion ofthe highly variable data results

from lot A.

4.3.3 Calculated versus SW-NIR Predicted Results

Nutrient content was also calculated using the linear regression equations listed in

Table 4.2. These calculated values for various chemical components were compared to

the results predicted using the SW-NIR calibration equations. Gross energy content

(kcallg) for those NIR predicted results was calculated using the equation (Pond, et ai.,

1995):

GE (kcallg - dry matter basis) = «%Crude Protein/lOO) * 5.7) + «%Crude

Fat/lOO) * 9.4) + «%CHO/lOO) * 4.1)

60

Linear regressions were run comparing both the calculated and SW-NIR predicted

results to the original chemical analysis results. This was done for all components

(protein, lipid, ash, dry matter, moisture and gross energy) for both locations, as well as

overall. Table 4.5 shows the results of these linear regressions. All comparisons were

significantly different with p-values less than 0.001. When looking at all ofthe overall

data, R2 values for SW-NIR predictions ofprotein (R2=0.75), lipid (R2=0.86) and ash

(R2=0.81) were higher than those of the calculated results (R2=0.64, 0.79, and 0.68,

respectively). SW-NIR calibration models predicted protein content consistently better

(overall R2=0.75; Canada R2=0.67; Alaska R2=0.70) than did linear regression

calculations. However, the calculation method seemed to predict ash content better than

SW-NIR for the overall data (R2=0.69), as well as by location (Canada R2=0.66; Alaska

R2=0.70). When R2 values were compared, SW-NIR yielded higher R2 values overall.

SW-NIR calibration models, on average, predicted content for herring with a slightly

higher degree of accuracy than did the calculation method.

4.4 DISCUSSION

4.4. 1 Reference Results

Reference results for both locations and lots were similar to those seen in the

previous chapters. Virtually the same data were used for both studies, however several

data points were left out for the NIR calibrations. This was because there was not enough

sample volume left to scan on the SW-NIR measurement. Calibrations for chemical

components varied by location and were significantly different for all components tested.

61

When reference results for nutrients were compared to one another, several

interesting correlations were found. The highly significant positive correlation (R2=0.97)

between lipid content and gross energy was similar to that found in other studies

(Castellini et at., 200 I). A significant negative correlation on both dry matter and as-fed

basis (R2=0.74 and 0.88, respectively) was found between moisture and lipid content.

The same type of correlation was also found between gross energy and moisture content

on a dry matter and as-fed basis (R2=0.69 and 0.96, respectively). The same two

correlations were found in a study done by Anthony et at. (2000). This study also found

slightly significant correlations between ash content and gross energy, and between

protein and lipid content (R2= 0.72 and 0.68, respectively).

4.4.2 Spectral Analysis

Partial least squares (PLS) regression method was used to establish the SW-NIR

calibration models. With PLS, the entire spectrum is used (600-1100 nm); therefore,

variations between the samples will be built into the calibration model (SoUid and

Solberg, 1992). The establishment of calibration model depends on the selection of

optimal number oflatent variables. To avoid over-fitting, 5-8 latent variables were

selected for prediction for different models. This resulted in an average standard error of

prediction (SEP) of2.27 for nutrient analysis. Over-fitting is counter-productive fitting

by the regression to the noise in the data (Murray and Cowe, 1992).

62

4.4.3 SW-NIR Calibration Models

The calibration models that would be applicable to the marine mammal field are

those for each location. Lots are specific to not only a location, but also a catch date.

Because a separate calibration model cannot feasibly be created for each lot for each

location, the closest calibration would therefore be location. Also, this study, as well as

previous studies, has found significant differences in nutrient content in feeds between

locations. One study done by Anthony et al., (2000) showed that Pacific herring caught

in Prince William Sound (PWS) and Lower Cook Inlet, Alaska were significantly

different in lipid content (41±1.9% vs. 32±2.7%). Their study also showed that herring

within PWS were significantly different in their lipid contents (29±0.1% vs. 21±1.3%),

depending upon where in PWS they were located (e.g., northeast versus southwest). The

calibration model for combined lots A, B, and C (Canada herring) can be used to analyze

Pacific herring caught in the area ofBritish Columbia, Canada. The model created for lot

D (Alaska herring) could be used to analyze Pacific herring caught in southeast Alaska,

where Petersburg Fisheries catch their fish.

Using the R2 values from the linear regressions, the calibration for southeast

Alaskan Pacific herring would better predict the nutrient content of herring than the

calibration for British Columbian Pacific herring. The Alaskan calibration model could

best predict protein (R2=0.86) and ash (R2=0.91). However, the levels oflipid and

moisture could also be predicted fairly accurately (R2=0.66 and 0.70, respectively). This

model could predict the nutrient content ofPacific herring caught in southeast Alaska

with a fairly high degree ofaccuracy. Overall, the calibration model for the Canada

herring is fairly good. The best prediction with this model would be oflipid content

63

(R2=0.81), then moisture and ash (R2=0.63 and 0.72 respectively). The lowest accuracy

in prediction would be for protein, with an R2 value of0.47. This model could be used to

predict the nutrient content of fish if approximations were acceptable. These correlations

are similar to those found in a study done by Lee et al., (1992). In this study, SW-NIR

was used to predict crude lipid content in intact rainbow trout. Using three latent

variables in the PLS method, the resulting R2 value was 0.81.

There are only a handful of studies involving the homogenization of fish prior to

NIR scanning. In one study done by SoUid and Solberg in 1992, homogenized Atlantic

salmon fillets were used. Samples in this study were scanned with near infrared

transmission spectroscopy in diffuse mode, and fat was measured. Prediction accuracy

was good with an R2 value of 0.99. Valdes et al. (1997) conducted another study using

homogenized samples. In their study they were looking at the utilization ofNiR

spectroscopy for the rapid analysis of fish being used as feed, however individual

calibrations for each species of fish were not created; instead, nine species offish and

cephalopod were combined and homogenized. Herring was one ofthose species and

made up 24% ofthe samples that went into the final homogenous mixture. The final

calibration models for fat and protein in this study were good with R2 values greater than

0.90. Researchers from this study suggested that NIR should be investigated further for

analysis of nutrient contents ofzoo feeds.

With the methodology used herein, whole herring could be sampled, processed

(homogenized) and scanned quickly yielding a fairly accurate result. Errors in

calibration, and the resulting lower R2 values may be the result ofnon-homogeneity

within the samples. Because of the use ofNIR spectroscopy (as opposed to NIT (near

64

infrared transmittance) spectroscopy), samples need to be uniformly homogenized (Sollid

and Solberg, 1992). Whole fish were ground and blended to a fairly homogenous

consistency. Homogenization was not easy as fish have high moisture content, and too

much homogenizing could result in fat and water separation (Sollid and Solberg, 1992).

Once samples were homogenized however, tiny pieces of skin, bones and scales were

still evident. Error in these models could also be due to the small number of samples

making up each calibration model, especially for lots (n=20-24). Outliers were apparent

in all calibrations. These outliers indicate that further research should be performed on

these samples. Additionally, due to slightly high SEP's, more samples should be

included in the calibration sets to account for greater variability in chemical composition

of the herring samples and to improve the precision of this method (Lee et al., 1992).

4.4.4 Calculated Nutrient Content

Although the R2 values for the linear regression equations for component-to­

component prediction were fairly high (0.68-0.97), this method was not as accurate as

SW-NIR spectroscopy. Linear regressions were run to compare NIR predicted and

calculated results with the chemical reference results. All R2 values were compared.

There were instances where calculated nutrient contents were better predicted than those

by NIR. Overall, however, NIR spectroscopy was better at predicting the nutrient content

ofwhole, homogenized Pacific herring.

65

4.5 CONCLUSION

Based on the results of this study, SW-NIR spectroscopy could be used very

effectively to predict the nutrient content ofwhole, homogenized Pacific herring.

Resulting calibration models for both Alaska and Canada herring could be used to predict

nutrient content of southeast Alaskan and British Columbian herring. R2 values were not

as high as in other studies; however, this method could still be used where approximate

results are needed. It should be noted that the strengths of SW-NIR spectroscopy lie in

its ability to predict nutrient content quickly and easily. With the repeated scannings

during analysis and the fact that there are 20-30 scans per sample, the accuracy and

repeatability of analysis would be much greater.

66

Table 4.1 Linear regression equations and R2 values (>600./0) nutrient componentrelationships in Pacific herring analyte correlations (n=99, P<O.OOl).

Nutrient content Regression equation R2

Dry Matter Basis

h vs. GE (kcaU ) Ash =25.55 - 2.73 * GE 0.72

Prot n . Lipid (EE) Prot =75.93 - 0.70 * EE 0.68

Protein vs. Moisture Prot =-58.42 + 1.54 * Moist 0.65

Lipid (EE) vs. Ash EE =78.32- 5.35 * Ash 0.68

Lipid (EE) vs. Moisture EE =173.29 - 1.94 * Moist 0.74

Lipid (EE) vs. GE (kcallg) EE =-98.11 + 20.64 * GE 0.97

Ash vs. Moisture Ash =-11.51+ 0.27 * Moist 0.62

Moisture vs. GE (kcallg) Moist =120.93 - 7.74 * GE 0.69

As-Fed Basis

M Moist = 90.23 -10.18 *GE 0.96

Lipid (EE) EE =-9.82 + 10.76 * GE 0.97

Lipid (EE) vs. Moisture EE =80.69 - 0.99 * Moist 0.88Shaded areas indicate "best fit" regression equation to use for predicting nutrient content, based onr-squared value.

Table 4.2 Sequence of linear regression equations used to predict energy density (GE(kcal/g», lipid (%), ash (%), and protein (%) content ofPacific herring starting frommoisture content.

Nutrient contents Basis Fonnula

moisture

GE

lipid (as fed)

GE(asfed)

GE

lipid

to calc.

to calc.

converlto

convert to

to calc.

to calc.

GE as fed GE =8.59 - 0.09 * Moist

lipid as fed EE = - 9.82 + 10.76 * GE

lipid (011) DM IipKJ (DM) =lipid (as fed) / ((100-Moist)/100)

GE (DAf) OM GE (OM) =GE (as fed) / ((1QO-Moist)/100)

ash OM Ash =25.55 - 2.73 * GE

protein OM Prot = 75.93 - 0.70 * EE

67

Table 4.3. Number oflatent variables and standard error of prediction (SEP) for each1"1 • .. 1 1 1 • t'" .. ... 1 .. • .. t'" T" .. ~ 1 ..

calloranon moael cnosen per component ror eacn lOt ana IOcanon Ot yacmc nemng.

Latent Variables SEP R2

Lot A (n =20)Protein 5 9.91 0.48

Lipid (EE) 8 3.07 0.74Ash 8 0.84 0.24

Moisture 8 1.67 0.27Dry Matter (OM) 8 1.67 0.27

Lot B (n =24)Protein 7 3.08 0.79

Lipid (EE) 6 3.22 0.71Ash 8 0.72 0.64

Moisture 8 1.08 0.82Dry Matter (OM) 8 1.08 0.82

Lot C (0 =22)Protein 6 3.63 0.87

Lipid (EE) 6 2.79 0.93Ash 6 0.63 0.72

Moisture 6 1.3 0.88Dry Matter (OM) 6 1.3 0.88

Lot D or ALASKA (n =20)Protein 5 2.17 0.86

Lipid (EE) 5 2.85 0.66Ash 5 0.5 0.91

Moisture 5 1.57 0.7Dry Matter (OM) 5 1.57 0.7

Lots A-C or CANADA (0 = 66)Protein 6 6.8 0.47

Lipid (EE) 7 2.89 0.81Ash 7 0.7 0.63

Moisture 7 1.39 0.72Dry Matter (OM) 7 1.39 0.72

68

Table 4.4. Actual chemical and SW-NIR predicted results for feed components ofPacific herring.

LatA Avg 52.13

n=20 StDev 3.81

Min 45.03

Max 58.46

Lat B Avg 54.08

n=24 StDev 4.79

Min 45.89

Max 63.83

LatC Avg 44.43

n=22 StDev 5.21

Min 37.0

Max 59.11

LatD Avg 42.80

Alaska StDev 3.38

n=2O Min 35.90

Max 48.59

ABC Avg 49.5

Canada StDev 8.73

n=66 Min 37.03

69

Table 4.5. R2 value comparing SW-NIR predicted and calculated (using regressionequations) values with those from chemical analyses ofPacific herring.

LOTS A, B, & C - Canada (n =66)ProteinLipid (EE)AshMoistureDry Matter (OM)

Protein

LOT 0 - Alaska (0 =20)ProteinLipid (EE)AshMoisture

Dry Matter (OM)

Protein

OVERALL (n =86)ProteinLipid (EE)Ash

MoistureDry Matter (OM)

Protein

NIR PredictedR2

67.4081.6062.7071.9071.9084.60

69.6085.9061.5091.4091.4080.40

74.8086.2067.8083.7083.7081.00

70

CalculatedR2

58.9070.3065.60

77.10

42.6091.2069.70

87.60

64.4079.4070.10

67.60

02

015

0.1

• o £15uIii-e0... 0~

-005

·0.1

-015

600 700 eoo 900Wav length (ron)

1000 1100

Figure 4.1. Short-wavelength Near Infrared (SW-NIR) spectra for lot A ofPacificherring.

Lipid

.········•

. .·••··•···· .... ...-

Water

Protein

-00001

n rTfll

0.00014

Ot.U.1J6

0.0000El

O.CKXlO2

-000012

600 700 1llOO 1100

Figure 4.2. Second derivative transformation ofSW-NIR spectra for lot A ofPacificherring. Boxed areas indicate most common regions for analyte measurements (analytesas indicated).

71

Average Proxi..... Results by LoeBtion

80

70

60

50

)40(J3O

20

10

o

*l1li

*I- f.I- •~I- ,.----

~ -I- ~ * *

I ~ I ~I ~}J1

Ash %

Analyte

GE(kcallg)

o Canada mAlaska

* indicates significant differences (P~ 0.(01) between locations for each nutrient

Figure 4.3. Average chemical reference results for components in Pacific herring byharvest location.

Average Proximate Results by Lot

80

70

60

50

J~30

20

10

o

.!.~b

'a~~

a a ~,-r-- ~-

'i~ ~~ I- 'g

I c..:..i- - -'"

- ~ -

f--.:I b ;a-I I [fl n:.rn ..... ~-'"-

[I ~ IAsh %

Analyte

Mlisue% GE(kcaIIg)

o Lot A o Lot B elate .LotD

.. b. C indicate significant differences between lots for each nutrient

Figure 4.4. Average chemical reference results for components in Pacific herring by lot.

72

Regression PlotGE (larg) • 8.5lIB74· o.lBCDM MlilIIan '"

S" 0.C&12'362 R-Sq. 96.0'" R-Sq(aet> .. 96.0 '"

3 ••

-S'}

~ 2-w(!)

60 70

Moisture %

Figure 4.5. Linear regression plot ofmoisture versus gross energy (GE) content on an asfed basis (R2=O.96) ofPacific hening.

Regression PlotEE 'lIl .. -9.82I!li8 + 10.7S94 GE (kcarg)

S .. 0.637154 R-Sq" W.1 '" R-Sq(aet) .. 97.0'"

25--'------------------,

?fl. 15

WW

5

2

GE (kcaJg)3

Figure 4.6. Linear regression plot ofgross energy (GE) versus lipid (EE) content on anas fed basis (R2=O.97) ofPacific herring.

73

Regression Plot--. ", .. 25.5511. 2.n:ro (£ (lafg)

S .. O.lII5Z3ln R-Sq .. 71.8 '" R-Sq(" .. 71.5 '"

..

11

10

9

~0

..c 8(/)

<7

8

5

55

•... ....-.. .­..- ...••• • ••• •. , ... .. . .

65

GE (kcaVg)75

Figure 4.7. Linear regression plot ofgross energy (GE) versus ash content on an as fedbasis (R2=O.72) ofPacific herring.

Regression PlotP1dein '" .. 75.9329- 0.7t1J288 EE '"

S .. 3.845l!IO RoSq .. 61.9 '" ~ .. 61.5 '"

65

55

'#.c:!e0. 45

35 ~----...,.------.------r-----~

20 30 40

EE %50 60

Figure 4.8. Linear regression plot oflipid (EE) versus protein content on a dry matterbasis (R2=O.68) ofPacific herring.

74

Protein: Latent Variables va. Standard Error of Prediction(SEP)

14.00

12.00

10.00

Q. 8.00wfI) 6.00

4.00

2.00

•..~ .L~

.....

-

-----------...~ ~ • !I I •..

1210842 6

Latent Variables

r ----+- Lot A • Lot B ----A- Lot c=-J

0.00o

Figure 4.9. Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for protein in Pacific herring lots A (LV=5), B (LV=7), and C (LV=6).

Lipid: Latent Variables vs. Standard Error of Prediction(SEP)

7.00

6.00

5.00

Q. 4.00wf/) 3.00

2.00

1.00

-

~

=--------",. &

...~~ -.-- -' • .....- -

0.00o 2 468

Latent Variables

10 12

----+- Lot A • LotB ----A- Lot C

Figure 4.10. Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for lipid in Pacific herring lots A (LV=8), B (LV=6), and C (LV=6).

75

Lot D Protein: Latent Variables vs. Standard Error ofPrediction (SEP)

\\\~

.\" .. ~ -....

3.40

3.20

3.00

D. 2.80wrn 2.60

2.40

2.20

2.00

o 2 4 6

Latent Variables

8 10 12

Figure 4.11. Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for protein in Pacific herring lots D (LV=5).

Lot 0 Upid: Latent Variables va. Standard Error of Prediction(SEP)

5.50

5.00

4.50

D. 4.00wrn 3.50

3.00

~

~~

~ .......... ..---2.50

2.00o 2 4 6

Latent Variables

8 10 12

Figure 4.12. Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for lipid in Pacific herring lots D (LV=5).

76

Lot A, B & C Cormined Protein: Latent Variables vs. StandardError of Prediction (SEP)

\

\~ ......,

~ /~-.r

~

9.00

8.50

8.00C1.wUJ

7.50

7.00

6.50o 2 4 6

Latent Variables

8 10 12

Figure 4.13. Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for protein in Pacific herring lots A, B, & C combined (LV=6).

Lot A, B & C Contined Upld: Latent Variables va. StandardError of Prediction (SEP)

.....~

•~~ -

.~

...... • • •

6.00

5.50

5.00

C1. 4.50wUJ 4.00

3.50

3.00

2.50o 2 4 6

L8tent Variables

8 10 12

Figure 4.14. Graph oflatent variables (LV) and SEP's to choose the best-fit calibrationmodel for lipid in Pacific herring lots A, B, & C combined (LV=7).

77

Regression PlotEE (Predic) =7.11750 + 0.834250 EE (Chern)

S =1.48al9 R-Sq =93.1 % R--Sq(adj) =92.8 %

50

--uis~e:, 40

WW

30

30 40

EE (Chern)50

Figure 4.15. Pacific herring lot C linear regression plot oflipid (EE) content by chemicalanalysis versus EE predicted by SW-NIR (R2=O.93) using six latent variables.

Regression PlotAsh (predic) =14.7558 + 0.777546 Ash (01em)

S =0.772920 R-Sq =91.9 % R-Sq(adD =91.4 %

70

Uis~e:, 65

.ct/)

«

60 ---...--,---------r------,.----'60 65

Ash (Chern)70

Figure 4.16. Pacific herring lot D linear regression plot of ash content by chemicalanalysis versus ash predicted by SW-NIR (R2=O.91) using five latent variables.

78

CHAPTER 5. OVERALL SUMMARY AND CONCLUSIONS

5.1 PROXIMATE ANALYSIS

In this study, samples were oven dried. Errors between duplicate samples were

below five percent with this method, however for future studies, freeze drying should

also be used and compared to oven drying for accuracy of predictability.

In this study, higher errors were seen between duplicates ofash content. This

may have been due to the high fat content of herring. Because of this there is the

possibility of some splattering as samples are heated quickly during the ashing process,

thus there is a potential for sample loss. Future research should be done to test whether

fat really does boil and cause spattering during the ashing process.

5.2 MORPHOMETRIC MEASUREMENTS

When statistical analyses were run on morphometric measurements (length (cm),

volume (ml), weight (g), volume per unit length (ml/cm), and weight per unit length

(g/cm)) results, suggested that length is related to both volume and weight since volume

and weight were also highly positively correlated.

5.3 MORPHOMETRICS AND MAIN VARIABLES

Data for the main variables oflot, location, and sex were analyzed to determine if

nutrient content of herring varied in this study. Size was statistically analyzed to confirm

that sample size classes (small, medium and large), as assigned by visual appraisal,

significantly differed from one another. There was no significant effect oflot on

79

morphometric measurements. When the effects of location were looked at, similar results

were seen for both locations. When graphed, however, herring from Alaska were slightly

smaller in comparison to those from British Columbia.

The main variable of sex was also analyzed to assess differences in morphometric

measurements. When all of the data were analyzed, there was a very significant

difference between males and females without eggs for volume (ml), volume per unit

length (ml/cm) and weight per unit length (glcm). Because some of the fish within the

small size category that were characterized as "females" may not have been sexually

mature, the analyses were rerun with the small size category data removed. The results

were similar with the difference being between males and females without eggs in only

volume (ml) and volume per unit length (ml/cm). Morphometric measurements were

very similar for males and females with eggs. The analysis of these data was made

difficult because the reproductive stages ofthe Pacific herring are fairly complex.

Because some of the herring in this study were fairly small in size, they might not have

been accurately identified and/or assigned to the three sex categories correctly.

5.4 MORPHOMETRICS AND PROXIMATE CONTENT

In this study, no correlation was found between morphometric measurements and

the various nutrient concentrations of the homogenized whole Pacific herring. In contrast

to other studies which found significant correlations between standard length (cm) and

crude lipid content, this study showed a very low correlation (R2<0.01). Further research

should be conducted on herring from various locations in order to ensure that a

representative range ofboth nutrient content and size are included in the database. In

80

addition, morphometric measurements ofvolume (ml), weight (g), volume per unit length

(ml/cm) and weight per unit length (g/cm) should be recorded along with the common

measurement of standard length (cm). Statistical analyses should also be done to discern

whether or not a true correlation exists between the various morphometric measurements

and each nutrient concentration (moisture, fat, protein, ash, carbohydrates and gross

energy).

5.5 MAIN VARIABLES AND PROXIMATE CONTENT

Correlations were also determined between the main variables of lot, location and

sex and the concentration of the various nutrients (protein, lipid, ash, moisture,

carbohydrate (CHO) and gross energy (kcal/g)) in the herring. Both lot and location were

found to have an effect on nutrient content (P<0.001). Lot most likely had an effect on

the nutrient content since each lot was caught in a different season and year. Specific

years and seasons of catch were not used or compared because data were obtained from

random sampling, not programmed sampling from lots for specific harvest or catch dates,

or even catch method. Previous studies have found that both season and year have an

effect on the nutrient content of herring (Blaxter and Holiday, 1963; Paul and Paul, 1999;

Anthony et al., 2000; Castellini et aI., 2001). Studies have also found that herring from

different locations have dissimilar nutrient content (Anthony et al., 2000). Sex was found

to have no impact on the nutrient content ofthe Pacific herring in this study. Previous

studies have suggested a relationship between lipid and energy content to sex and

reproductive status (Henderson and Almatar, 1989; Paul and Brown, 1998). Pacific

herring store fat during the spring and summer to survive the winter's decline in prey

81

availability. In the winter these fish also allocate stored energy to gonadal products for

spring spawning (Hay et al., 1988). These relationships have also been found in Atlantic

herring (Clupea harengus). In this species, a depletion of lipid corresponds with the

development ofthe gonads in the winter season and the lowest levels were found in

March, after the fish have spawned (Anokhina, 1959). Lipid content has also been shown

to be directly related to fecundity (Anokhina, 1959). The results from the study herein

warrant further research based on what has been found in previous literature. Most

likely, a significant correlation was not seen in this study because each sex category

lacked sufficient sample numbers and ranges in size and maturity to get accurate

representation.

5.6 PROXIMATE CONTENT

Several studies have found correlations between different nutrients. One

relationship that has been cited is between moisture content and energy density. This

correlation, between moisture content and calculated GE, was also seen in this study

(R2=O.96). This correlation may exist because of the close relationship between lipid

content and gross energy, and the inverse relationship between moisture and lipid

content. Because lipid is so high in energy (9.8 kcallg) it makes up a very large portion

of a diet's gross energy, therefore, it would make sense that the two would be strongly

correlated. This high correlation (R2=O.97) between lipid and GE in this study

substantiates this very closer relationship.

Because of the extremely high correlation coefficient ofthese nutrient

relationships, the hypothesis that the linear regression formulas being created could be

82

used to calculate the entire nutrient content ofa Pacific herring was tested. Indeed, when

analyses were run, the prediction ability of these regression equations (n=99) was fairly

reliable. This reliability however, depends upon the R2 of the regression equation. The

lower the R2, the lower the predictability of the formula. With added proximate data on

herring, these linear regression equations could be improved upon to increase

predictability. Further research should be done to continue to study the possibility of

utilizing simple regression equations to predict the nutrient content of herring, and

potentially other fish species. The sample preparation required for this method would be

very minimal. Calculations would use the moisture content of a herring as a starting

point. This would be the easiest to measure, requiring the least amount ofequipment and

training. This method is by far the simplest, most cost effective and quickest method of

predicting the nutrient content ofa herring. This method would only require a fairly

accurate digital scale and a drying oven or microwave, and it would not require a trained

laboratory technician or special analytical equipment. Therefore, this method could be

implemented in most any situation, especially when timely decisions need to be made at

marine mammal facilities using large quantities ofherring. This method might also be

applicable for other species of fish that are commonly fed to marine mammals or other

aquatic animals. Further research should be conducted to validate these possibilities.

5.7 SW-NIR SPECTROSCOPY

From the results of this study, calibration models for lots Band C could be used

to predict to a fairly accurate degree, the proximate composition ofwhole Pacific herring.

The calibration model created for lot A however, was not as accurate, yielding R2 values

83

ranging from 0.24 through 0.74. This is most likely due to the variability in reference

results from this lot. A calibration model ofthis sort would not be applicable to the field

as a feasible method ofproximate analysis because a high degree of accuracy is required.

Because lots A through D differed by catch date, without a new calibration model

representing each new catch date, these models would not be practical. Given this,

calibration models were also created by location ofcatch. The calibration model for lot

D was also the model for herring caught off the southeastern coast ofAlaska. The model

created for lots A through C combined could be used to predict the nutrient content of

Pacific herring caught offthe coast ofBritish Columbia, Canada. These location based

calibrations models are more practicable. The model for the British Columbian herring

(R2=0.66-0.91) was fairly comparable to that of the Alaskan herring (R2=0.47-0.81).

This technology has widespread application and potential in the field ofmarine

mammal husbandry. The strengths of this technology lie in its ability to predict nutrient

content quickly and easily. This technology, once set up, is cost effective and non­

destructive to samples. A portable NIR spectrophotometer with a hand-held fiber-optic

probe is now available. This expands the potential for this technology, making it easier

to analyze components of interest outside of the laboratory. One of the drawbacks of this

technology is the start-up cost ofequipment. Also, in order to scan a sample, a

calibration model must be created and installed. Finally, a trained laboratory technician

would be required.

A component of current NIR research focuses on minimizing sample preparation.

Increasingly, a hand-held fiber optic NIR probe is used to scan a section of an intact fish

to determine its nutrient composition. Usually, the portion of the fish scanned has been

84

found, in previous studies, to be representative of the nutrient content in the entire fish.

A fiber optic probe was not available for use in this study; therefore, the next simplest

method of preparation was used. Herring were homogenized using a store-bought Oster

brand blender. Because an NIR spectrophotometer is very sensitive to the particle size

within a sample, homogeneity must be carefully considered. In this study, methodology

used for sample preparation was created through trial and error. Problems of incomplete

homogenization were encountered with the skin, scales and small bones in the herring.

Precaution should be taken with the increasing size ofa fish due to thicker skin and larger

bones and vertebrae. These factors may increase the difficulty in homogenizing samples.

It has been suggested that a commercial meat grinder could be used on partially thawed

fish samples. Further testing on proper particle size is still needed to obtain optimal

reproducibility.

Further research should be done looking at increasing the predictability of these

calibration models. Additional nutrient data for samples would need to be added to the

calibrations created herein. More research is needed to look at the effects ofother

variables, such as season and prey availability, on the nutrient content ofPacific herring.

Once a better understanding of these variables is gained, a more comprehensive

calibration model could possibly be created.

This technology and the principle of nutrient analysis prediction could be applied

to other fish species used in marine mammal facilities. Research should be conducted to

look at the use of SW-NIR spectroscopy to predict the nutrient content of Atlantic herring

(Clupea harengus). That study should look at the possibility ofusing or adding to the

Pacific herring calibration to predict the proximate content ofAtlantic herring. These

85

nutrient calculation regression equations should also be evaluated for their applicability

in other industries such as the aquaculture and fish-farming industries.

86

Appendix Table 1. Average morphometric measurements, standard deviation, minimumand maximum values for lots and locations.

Lot Length Vol (em) Weight V/L W/L WN(em) (g) (mllem) (g/em) (g/ml)

A Avg 18.34 86.90 86.03 4.55 4.49 0.98n=29 StDev 2.64 36.44 38.46 1.39 1.51 0.10

Min 14.00 30.00 27.22 2.07 1.81 0.68Max 23.50 145.00 145.15 6.36 6.60 1.32

B Avg 18.80 93.04 93.31 4.74 4.73 0.99n=28 StDev 2.76 40.90 42.68 1.57 1.69 0.10

Min 14.00 30.00 27.22 2.14 1.88 0.78Max 22.50 170.00 154.22 7.56 7.01 1.21

C Avg 18.52 81.36 85.15 4.37 4.57 1.05n=22 StDev 1.15 14.97 16.56 0.58 0.66 0.07

Min 17.00 55.00 54.43 3.14 3.20 0.91Max 20.50 110.00 113.40 5.50 5.82 1.17

0 Avg 17.66 72.75 73.77 4.05 4.10 1.01n=20 StDev 1.68 21.01 21.12 0.86 0.88 0.09

Min 14.00 35.00 31.28 2.50 2.23 0.85Max 20.00 105.00 98.95 5.41 5.21 1.22

Location

Canada Avg 18.56 87.53 88.36 4.57 4.60 1.00n=79 StDev 2.35 33.73 35.34 1.29 1.39 0.10

Min 14.00 30.00 27.22 2.07 1.81 0.68

Max 23.50 170.00 154.22 7.56 7.01 1.32Alaska Avg 17.66 72.75 73.77 4.05 4.10 1.01n=20 StDev 1.68 21.01 21.12 0.86 0.88 0.09

Min 14.00 35.00 31.28 2.50 2.23 0.85

Max 20.00 105.00 98.95 5.41 5.21 1.22

87

Appendix Table 2. Average morphometric measurements, standard deviation, minimumand maximum values for size and sex categories.

Size Length Vol (em) Weight VIL WIL WN(em) (g) (mUcm) (glem) (g/ml)

Small Avg 15.95 52.35 51.97 3.23 3.19 0.98

n=34 StDev 1.40 15.02 18.10 0.68 0.88 0.13

Min 14.00 30.00 27.22 2.07 1.81 0.68

Max 18.00 80.00 81.65 4.44 4.54 1.32Medium Avg 18.52 81.67 81.25 4.39 4.37 1.00

n=24 StDev 0.92 13.92 13.55 0.59 0.55 0.08

Min 17.00 52.50 63.36 3.04 3.70 0.91

Max 20.00 105.00 108.86 5.38 5.58 1.22Large Avg 20.30 112.93 115.59 5.52 5.66 1.03n=41 StDev 1.23 22.50 21.52 0.81 0.76 0.06

Min 18.50 75.00 83.37 4.05 4.30 0.91

Max 23.50 170.00 154.22 7.56 7.01 1.13

Sex

Male Avg 18.54 84.52 85.59 4.53 4.58 1.01

n=26 StDev 1.24 14.87 17.16 0.57 0.72 0.07

Min 14.50 47.50 40.32 3.28 2.78 0.85

Max 20.50 110.00 113.40 5.50 5.82 1.13Female Avg 17.61 69.64 71.13 3.89 3.97 1.02

n=14 StDev 1.73 19.95 20.49 0.77 0.83 0.08

Min 14.00 35.00 31.28 2.50 2.23 0.89

Max 20.00 105.00 97.66 5.25 4.94 1.13Female Avg 18.73 81.04 86.21 4.29 4.57 1.07wI eggs StDev 1.17 17.43 17.04 0.71 0.66 0.09

n=12 Min 17.00 52.50 63.50 3.04 3.63 0.91Max 20.00 105.00 108.86 5.38 5.58 1.22

88

Appendix Table 3. Average proximate results, standard deviation, minimum and maximum values for lots and locations

Lot Iprotein % EE % Ash % CHO% Moist % (k~7/9) IIProteln % EE % Ash % CHO% Molat %

A Avg 52.33 31.23 8.44 8.01 72.40 6.25

n=29 StDev 4.99 5.51 1.13 5.13 2.84 0.28

Min 39.01 20.66 6.05 2.71 67.94 5.48

Max 64.08 40.32 10.88 31.30 80.26 6.71B Avg 54.52 35.53 7.71 2.49 71.03 6.55

n=28 stDev 4.64 4.54 0.99 2.38 1.95 0.22

Min 45.89 24.10 5.92 0.00 68.32 5.98

Max 63.83 42.56 10.02 8.96 75.72 6.93

C Avg 44.43 43.39 6.92 5.26 68.24 6.83n=22 StDev 5.21 6.36 1.05 1.29 2.67 0.30

Min 37.02 27.50 5,47 3.06 63.29 6.08

00 Max 59.11 51.85 9.18 8.56 74.60 7.22.\0

D Avg 42.80 45.73 6.57 5.11 66.26 6.95n=20 StDev 3.38 5.57 0.70 3.37 3.26 0.30

Min 35.90 35.55 5.40 0.00 59.88 6.43

Max 48.59 56.81 8.08 11.55 71.93 7.60

Location

Alaska Avg 42.80 45.73 6.57 5.11 66.26 6.95 14.75 10.79 2.24 1.54 70.71n=79 StDev 3.38 5.57 0.70 3.37 3.26 0.30 1.24 3.09 0.24 1.29 2.98

Min 35.90 35.55 5,40 0.00 59.88 6.43 10.59 4.77 1.42 0.00 63.29Max 48.59 56.81 8.08 11.55 71.93 7.52 17.32 19.04 2.79 10.04 80.28

Canada Avg 50.91 36.14 7.76 5.29 70.76 6.52 14.35 15.19 2.20 1.68 66.66n=20 StDev 6.40 7.27 1.21 4.17 2.99 0.35 0.97 3.83 0.13 1.02 3.66

Min 37.02 20.66 5,47 0.00 63.29 5.48 12.18 10.24 1.90 0.00 59.88

Max 64.08 51.85 10.88 31.30 80.26 7.22 16.05 22.58 2.46 3.65 71.93Shaded areas indicate proximate results on an as fed basis: unshaded areas indicate proximate results on a dry matter basis.

Appendix Table 4. Average proximate results, standard deviation, minimum and maximum values for size and sex cateQories.Size IProtein EE % Ash % CHO% Mol.t % OE Pratefn EE

% (~~ %Small Avg 48.58 38.23 7.58 5.70 70.53 6.60 14,13

n=34 StDev 6.91 7.46 1.29 3.24 3.43 0.35 1.19

Min 38.43 20.66 5.92 0.00 65.56 5.77 10.59

Max 64.08 49.63 10.88 11.71 80.26 7.11

Medium Avg 49.88 36.59 7.76 5.79 69.99 6.52

n=24 StDev 5.43 6.95 1.03 2.57 2.94 0.33

Min 38.29 23.31 5.69 0.00 63.33 5.92

Max 58.46 50.68 9.46 9.70 74.60 7.15

Large Avg 49.49 38.82 7.32 4.56 69.20 6.68

n=41 StDev 7.38 8.88 1.26 5.11 3.88 0.44

Min 35.90 21.00 5.40 0.00 59.88 5.48

\0 Max 63.83 56.81 10.07 31.30 77.42 7.600

Sex

Male Avg 45.23 43.80 6.92 4.16 67.84 6.87

n=26 StDev 5.72 7.04 0.99 2.31 3.36 0.34

Min 35.90 27.50 5.40 0.00 59.88 6.08

Max 59.11 56.81 9.18 8.56 74.60 7.60

Female Avg 43.35 43.87 8.81 6.10 67.02 6.84n=14 StDev 3.86 4.99 0.71 3.05 2.95 0.26

Min 38.29 35.18 5.69 0.00 61.30 6.45

Max 51.72 54.38 8.27 11.55 71.93 7.48Female Avg 46.29 41.19 7.04 5.49 68.86 6.74

wI eggs StDev 4.20 5.25 0.98 1.68 2.21 0.25~ 0.52 2.81n=12 Min 37.02 34.19 5.47 1.88 63.29 6.39 13.55 9.89

Appendix Table 5 Linear regression equations and R2 results for analyte correlations.

:;c

0.98

Analyte Regression equation R2

VS. CHO Ash =-7.32 + 0.04 (CHO) 0.01

VS. Moisture Ash =-11.51 + 0.27 (Moist) 0.62

VS. Gross energy (GE) Ash =25.55 - 2.73 (GE) 0,72

VS. Moisture CHO = -1.47 + 0.10 (Moist) 0

VS. Gross energy (GE) CHO =36.50 - 4.74 (GE) 0.19 bHO.

vs. Ash EE = 78.32 - 5.35 (Ash) 0.68

vs. CHO EE =41.42 - 0.64 (CHO) 0.09

VS. Moisture EE =173.29 - 1.94 (Moist) 0.74

vs. Gross energy (GE) EE = -98.11 • 20.64 (GE) 0.97

vs. Lipid (EE) Prot =75.93 - 0.70 (EE) 0.68

vs. Ash Prot =20.11 + 3.88 (Ash) 0.49

vs. CHO Prot =51.58 - 0.44 (CHO) 0.06

vs. Moisture Prot =-58.42 + 1.54 (Moist) 0.65

vs. Gross energy (GE) Prot =134.25 - 12.88 (GE) 0.52

Shaded areas indicate results on an as fed basis; unshaded areas indicate results on a dry matter basis.

Ash

CHO

Moisture VS. Gross energy (GE) Moist = 120.93 - 7.74 (GE) 0.69 'Moist = 90.23

Lipid (EE)

Protein

"0-

Appendix Table 6 Linear regression equations and R2 results for 8W-NIR predicted andcalculated (using linear regression equations to predict analyte content) values comparedto chemical reference results.

Analyte Comparison Regression equation

SW-NIR predicted vs. Pred =10.02 + 0.78 (Chern) 0.75Protein

Chemical reference

Calculated vs. ChemicalCalc =19.79 + 0.59 (Chern) 0.64

reference

SW-NIR predicted VS. Pred =5.93 + 0.85 (Chern) 0.86Chemical reference

Lipid (EE)Calculated VS. Chemicalreference

Calc =7.73 + 0.81 (Chern) 0.79

SW-NIR predicted vs. Pred =2.46 + 0.66 (Chern) 0.68Ash

Chemical reference

Calculated vs. ChemicalCalc =2.05 + 0.73 (Chern) 0.70

reference

SW-NIR predicted vs.Pred. =94.09 - 0.81 (Chern) 0.84

MoistureChemical reference

Calculated VS. Chemicalreference - -SW-NIR predicted VS.

Pred =0.93 + 0.86 (Chern) 0.85Chemical reference

Gross energy (GE)Calculated vs. Chemicalreference

Calc =1.19 + 0.82 (Chern) 0.77

92

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