the potential of nir spectroscopy for the detection of the adulteration of orange juice

J Sci Food Agric 1995,67,77-84

The Potential of NIR Spectroscopy for the Detection of the Adulteration of Orange Juice Michael Twomey," Gerard Downeyb and Paul B McNulty"* "Department of Agricultural & Food Engineering, University College Dublin, Earlsfort Terrace, Dublin 2, Ireland bThe National Food Centre, Teagasc, Dunsinea, Castleknock, Dublin 15, Ireland (Received 10 March 1994; revised version received 13 June 1994; accepted 22 August 1994)

Abstract: Near-infrared spectroscopy was used to investigate the adulteration of 65 authentic concentrated orange juice samples obtained from Brazil and Israel. These samples were adulterated with 100 g kg-' additions (ie 100 g added to 900 g) of (1) orange pulpwash, (2) grapefruit juice, and (3) a synthetic sugar/acid mixture and with 50 g kg-' additions (ie 50 g added to 950 g) of (4) orange pulpwash, and (5) grapefruit juice. All samples were scanned on the NIR systems 6500 spectrophotometer over the 1100-2498 nm wavelength range. Principal component analysis was used to reduce each spectrum to 20 principal components. Factorial discriminant analysis was used to distinguish between the different sample groups. Using orange juice and orange juice adulterated at the 100 g kg- ' level, accurate classification rates of 94-95% were obtained. To clas- sify samples adulterated at the 50 g kg- ' level, the calibration development sample set had to be augmented by the inclusion of samples adulterated at this lower level-after this augumentation, an accurate classification rate of 94% was obtained. The results demonstrated that the application of principal component and factorial discriminant analysis to NIR reflectance spectra can detect the adulteration of orange juice with an average accuracy of 90%. Furthermore, not one adulterated sample was predicted as being an authentic orange juice throughout the entire test regime.

Key words: adulteration, orange juice, NIR spectroscopy.

INTRODUCTION et al(1993) used principal components and discriminant analysis to distinguish between Brazilian, Israeli and

Adulteration of food liquids for economic gain is a well- adulterated Israeli juice. They investigated the effect of established malpractice. Adulteration of products such treating the spectra by different mathematical pro- as orange juice concentrate has progressed from simple cedures prior to principal component analysis, and additions of water and sugar to more sophisticated found that the best results were obtained by using the methods including the addition of minor components. untreated spectra. Thus, the identification of adulteration has become Work reported in this study aimed to extend investi- more complex and requires the use of sophisticated gations into the utility of NIR spectroscopy for detec- techniques and analysis for successful detection. Scotter tion of orange juice adulteration using a combination of et al (1992) used discriminant analysis with principal principal component and factorial discriminant analysis components and canonical variates to distinguish and adulterant mixtures of (1) sucrose/glucose/fructose/ between near-infrared (NIR) spectra of orange juice, citric acid/malic acid and water (model orange juice) orange juice adulterated with pulpwash, and orange which has these components in the same ratio as pure juice adulterated with beet medium invert sugar. Evans orange juice, (2) orange pulpwash which has an insolu-

ble solids content similar to that typically found in con- * To whom correspondence should be addressed. centrated juice, and (3) grapefruit juice.

J Sci Food Agric 0022-5142/95/$09.00 0 1995 SCI. Printed in Great Britain 77

78 M Twomey, G Downey, P B McNul ty

MATERIALS AND METHODS

Orange juice (ORB)

Sixty-five concentrated orange juice samples (labelled ORB 10 to ORB 74) from Brazil (65% soluble solids) and Israel (60% soluble solids) were used. These samples were stated by their suppliers to be authentic. Thirty-five of the 65 samples were analysed for sucrose, glucose, fructose, citric acid, iso-citric acid, phosphate, sodium, magnesium, potassium, calcium, proline and hesperidin to ensure that the constituents were within the range typically found in authentic orange juice (RSK 1987; AFNOR 1989; Hofsommer 1989).

Orange pulpwash (PWT, PWF)

Six concentrated orange pulpwash samples at 45" Brix were obtained from Brazil. These samples were diluted to 11.8" Brix in which their suspended pulp (insoluble solids) content was approximately 150 ml litre-' whereas the orange samples contained about 50- 70 ml litre-' of suspended pulp. Therefore, it was necessary to reduce the pulp content by about 50% to more accurately reflect the composition of authentic orange juice. Each pulpwash sample at 11.8" Brix was divided into two halves. One half was centrifuged at 350 x y for 10 min 2nd then added to the (pulp-free) supernatant of the other half. This resulted in a pulpwash sample with about 75 ml litre-' insoluble solids. These samples at 11.8" Brix were then concentrated to 60" Brix in a rotary evaporator.

Sugarlacidlwater mixture (MIX)

Analytical grade samples of sucrose, glucose, fructose, citric acid and malic acid were purchased from Sigma Chemical Co Ltd, Dorset, UK. These were mixed to produce a model adulterant orange juice which had these components in the same ratio typically found in orange juice (Table 1). The soluble solids of the final solution was 65%.

Grapefruit (GRT, GRF)

Five Israeli grapefruit samples were used. The samples were tested for ascorbic acid, phosphate, citric acid, malic acid, glucose, fructose, sodium, potassium, calcium and proline to ensure that the constituents were within the ranges normally found in authentic grapefruit juice.

Preparation of samples for NIR

Thirty grams of each sample was vacuum-dried at 45°C and 5.3 kPa for 9 h in a 10 cm x 15 cm x 5 cm (high)

TABLE 1 Average composition (g litre-') of 35 orange juices used in this study. The composition of the model adulterant orange juice was based on the average composition (Data take from

Twomey (1993))

Authentic juice Adulterant juice, mean

Mean Standard Deviation

Citric acid 11.1 0.7 11.0 Malic acid 1.7 0.3 1.7 Glucose 23.2 1 .o 23.0 Fructose 24.7 0.8 24.0 Sucrose 34.5 3.9 35.0

sample holder. The samples were manually pestle ground through a 300 pm sieve to form a fine powder.

NIR instrumentation and collection of spectra

An NIR Systems 6500 digitally controlled scanning monochromator was used (Silver Springs, MD, USA). The dried samples were placed in powder cells which were circular cups with a diameter of about 50 mm and a depth of about 10 mm with a quartz window in one side. The NIR instrument was connected to an IBM compatible computer and controiled by a dedicated software package (NIRS3; Intrasoft International). Reflectance spectra were recorded over the 1100- 2498 nm region at 2 nm intervals to give 700 log ( l /R) Fbsorbance points. The instrument was configured to scan a reference ceramic tile five times, the sample cell 20 times and the reference tile a further five times. Average spectra were then calculated for both the reference tile and sample; the final spectrum was the differ- ence between the average sample spectrum and the average reference spectrum.

File transfer and data manipulation

For statistical examination, the collected spectra were transferred into the file format used by the software package chosen (SAISIR-devised and supplied by D Bertrand, INRA, Nantes, France). This required the conversion of each individual spectrum from a binary file format into a tightly defined JCAMP DX (ASCII) format promulgated by McIntyre and Rutledge (1992). This collection of individual files was then transformed into the SAISIR format using a batch decoding file written by D Bertrand and modified by G Downey.

Raw spectra were input into the principal component analysis with wavelengths as principal variables and 'class' values (eg ORB, PWF, GRF, etc) as supplementary variables. Calibration and prediction samples were

Detection of adulteration of orange juice 79

principal and supplementary observations, respectively. Principal component space was generated using principal observations and variables. The 700 data points in this spectrum were reduced to 20 principal components following the procedures of Devaux et a1 (1986) and Bertrand et a1 (1990); all 20 components were used in the factorial discriminant analysis. The gravity centre of each sample type in the model was calculated from the calibration sample scores; test sample spectra were pro- jected onto the factor space produced by the PC and discriminant analysis; and the Euclidean distance from each to each level of the gravity centres was measured. Test samples were assigned to the group with the nearest gravity centre.

RESULTS AND DISCUSSION

NIR spectra

The mean NIR spectra of authentic (ORB 1) and adulterated (PWT 2, MIX 3, GRT 4, PWF5, GRF 6) orange juice samples are presented in Fig 1. Authentic orange juice (group 1) exhibits intermediate absorbance between groups 5 and 6 (higher absorbance) and groups 2 to 4 (lower absorbance).

Individual NIR spectra of authentic (ORB 53) and adulterated (PWT 54, MIX 54, GRT54, PWF 54, GRF 53) orange juices are presented in Fig 2. In this case the authentic orange juice exhibits higher absorbances than any of the adulterated orange juices in contrast to the pattern in Fig 1. This merely serves to confirm that visual examination of raw spectra cannot be used to

effectively discriminate between authentic and adulterated orange juices. The greatest source of variation in any collection of NIR reflectance spectra is light scatter caused by particle size variations; changes in reflectance arising from differences in chemical composition are much smaller than scatter-induced variations and normally require mathematical manipulation of the spectra to be detectable.

Experiments to investigate the utility of NIR reflectance in this application were performed in two stages; the first only used samples adulterated at the 100 g kg-' level while the second extended this work to incorporate juices adulterated at the lower 50 g kg-' level.

Detection of 10% adulterations (experiments 1-3)

Thirty-six samples were selected as a calibration set; these comprised 10 orange juice, eight adulterated with pulpwash, nine with grapefruit and nine with the model orange juice; 103 separate samples were available with which to test the discriminant model developed. Because of the limitations of working in the DOS environment and the size of each spectral file, it was not possible to perform the statistical operations on all these files at once. For this reason, the chemometric procedures were performed in three experiments. Each used the same calibration sample set but the number and type of samples in the prediction sets varied as follows: experiment 1 : 34 mixed-type samples; experiment 2: 43 mixed-type samples; experiment 3: 26 orange juice samples only.

6 .........................................

3 .........................................

..............................................................................................................................

I Wavelength (nm)

Fig 1. Mean NIR absorption spectra of (1) authentic orange juice and authentic orange juices adulterated with (2) 100 g kg-' orange pulpwash, (3) 100 g kg-' model orange juice, (4) 100 g kg-' grapefruit juice, (5 ) 50 g kg-' orange pulpwash and (6)

50 g kg- ' grapefruit juice. Spectra are identified at three wavelengths by numbers 1-6 on three vertical lines.

80 M Twomey, G Downey, P B McNulty

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@ I 1 1 1 1 , 1100 1300 1500 1700 1900 2100 2300 2

Wavelength (nm)

Fig 2. Individual NIR absorption spectra of samples of (1) authentic orange juice ORB53 and authentic orange juices adulterated with (2) 100 g kg-' orange pulpwash (PWT54), (3) 100 g kg-' model orange juice (MIX 54), (4) 1 0 0 g kg-' grapefruit juice (GRT54), (5) 50 g kg-' orange pulpwash (PWF 54) and (6) 50 g kg-' grapefruit juice (GRF 53). Spectra are identified at three

wavelengths by numbers 1-6 on three vertical lines.

The samples used in test 1 are summarised in Table 2. The results of this experiment (Table 3) revealed that 32 out of the 34 samples (94%) in the prediction set were correctly identified. Sample ORB47 (classified as authentic orange juice by the suppliers) was identified as an orange juice adulterated with grapefruit juice. Addi- tionally, PWT47 (ORB47 adulterated with 100 g kg-' pulpwash) was also identified as an orange juice adulterated by grapefruit-thus the model is internally con- sistent. On subsequent analysis it was found by the composition of ORB47 was more similar to grapefruit than orange juice. Thus, ORB47 might well have been an adulterated rather than an authentic orange juice in the first instance and hence the prediction may have been correct rather than false. If this is the case, the correct prediction rate is 97% (33 out of 34). The critical importance of establishing a reliable calibration sample set is clearly indicated.

Experiment 2 was similar to experiment 1 except that the individual samples in the prediction set were different from those in the prediction set in experiment 1. In experiment 2,41 out of 43 samples (95%) were correctly predicted (Table 4). Two authentic orange juices (ORB 24, ORB 27) were identified as orange juices adulterated with grapefruit additions. In this case, however, these identifications are believed to be faulty on the basis of subsequent chemical analysis.

In experiment 3, only 20 out of 26 samples (77%) were correctly predicted (Table 5). In this experiment only authentic orange juice was used in the prediction sample set. No obvious explanation was evident for this disappointing performance particularly when compared with the excellent predictions in the two previous tests. The orange juice samples used in the prediction set were

not used in any other experiment. Consequently, it was not possible to determine if they were correctly identified in other experiments or if in fact they were truly authentic samples in the first instance. Overall, 93 (or 94 depending on ORB47) of the 103 prediction samples were correctly classified-this represents a success rate of 90% (or 91%). A similar level of success was regis- tered by Evans et a1 (1993) when 20 principal components were used.

Detection of 5% adulteration (experiments 4-6)

The purpose of the second series of experiments was to determine if a lower level of adulteration of orange juice could be detected. In experiment 4, the calibration set was identical to that used in experiments 1-3; the prediction set consisted of previously untested samples, including 50 g kg-' adulterations of orange juice with orange pulpwash and grapefruit. Fifty-six out of 63 samples (89%) were correctly identified (Table 6). Seven orange juices adulterated with 50 g kg-' additions of orange pulpwash (PWF) were incorrectly predicted as orange juices adulterated with grapefruit juice. This misclassification may have been due to an insufficiency of information in the discriminant model, viz, the absence of any 50 g kg-' adulterated samples in the calibration set. Experiment 5 was undertaken to address this possibility; the calibration set was extended to include eight samples of orange juice adulterated with 50 g kg-' orange pulpwash (PWF). The orange juice samples adulterated with 50 g kg- pulpwash (PWF) which had been incorrectly predicted as orange juice

D r t w t i o n of adulteration of orange juice 81

TABLE 2 Samples used for experiment 1

Calibration set"

1 2 3 4

ORB43 PWT49 MIX31 GRT47 ORB44 PWT51 MIX32 GRT49 ORB48 PWT52 MIX33 GRTS 1 ORB5 1 PWT54 MIX37 GRT52 ORB53 PWT56 MIX38 GRT54 ORB56 PWT58 MIX49 GRT56 ORB59 PWT61 MIX51 GRT58 ORB60 PWT62 MIX53 GRT59 ORB63 MIX55 GRT6 1 ORB65 Total 10 8 9 9

Prediction set"

I 2 3 4

ORB47 ORB50 ORB52 ORB54 ORB55 ORB57 ORB58 ORB61 ORB64 ORB66 Total 10

PWT47 PWTSO PWT53 PWT55 PWT57 PWT59 PWT60

7

MIX28 MIX34 MIX35 MIX36 MIX45 MIX48 MIX50 MIX52 MIX54

9

GRT33 GRT48 GRTSO GRT53 GRT55 GRT57 GRT60 GRT62

8

a PW represents pulpwash where the T represents addition of 100 g pulpwash to 900 g juice. GR represents grapefruit where the T represents 100 g kg-' additions. The number that follows is the number of the orange juice which has been adulterated, ie PWT47 = 900 g ORB47 + 100 g pulpwash.

adulterated with grapefruit juice in experiment 4 were now correctly classified (Table 7).

These observations confirm the necessity for an appropriate calibration set for effective detection of

adulteration. Of the three samples in this experiment which were misclassified, one was the orange juice (ORB47) which had previously been identified by the discriminant model as a juice adulterated by grapefruit addition which could not, as a result of subsequent chemical analysis, be unambiguously claimed to be authentic. Thus, this classification may not be in error. One of the 10 juice samples in the prediction set which were adulterated by 50 g kg- grapefruit juice addition was misclassified. In an attempt to rectify this, a final experiment (6) was conducted which included such samples in the calibration set.

In experiment 6 (Table 8), one orange juice adulterated with 50 g kg-' grapefruit juice (GRF 51) which had been incorrectly predicted in experiment 5 was now correctly predicted. However, two orange juice samples adulterated with 50 g kg-' orange pulpwash (PWF) were predicted as orange juice adulterated with grapefruit juice even though they had been correctly predicted in test 5. Thus, the choice of the calibration set was again shown to be crucial to the efficacy of sample classification. Interestingly, ORB47 alone and ORB47 adulterated with 100 g kg-' pulpwash (PWT47) were both classified as orange juice adulterated by grapefruit juice addition. Given the doubts about the authenticity of the juice, the percentage of samples correctly predicted by this model is actually 94% (45 out of 48).

Model examination

All 20 principal components were used for the development of the discriminant models reported in this work. In an attempt to unravel the molecular basis for the observed classification, examination of the principal component loadings plots and/or the discriminant spectral patterns has been reported (Downey et a1 1990). In this as in many other cases, however, interpretation proved to be difficult but it was noteworthy that the components of greatest significance in the discriminant models were numbers 4, 3 and 12 (Fig 3). The early introduction of a component as high as 12 which

TABLE 3 Results of experiment 1

Group No of samples No of samples No of samples in calibration set in prediction set incorrectly predicted

1 ORB 10 2 PWT 8 3 Mix 9 4 GRT 9

10 7 9 8

1" l b 0 0

ORB47 was predicted as orange juice adulterated with a grapefruit addition.

PWT47 (ORB47 adulterated with 100 g kg-' pulpwash) was predicted as orange juice adulterated with a grapefruit addition. Percentage correctly predicted = 94%.

82


M Twomey, G Downey, P B McNulty

Group N o of samples N o of samples No of samples in calibration set in prediction set incorrectly predicted

1 ORB 10 2 PWT 8 3 Mix 9 4 GRT 9

12 11 10 10

2" 0 0 0

ORB 24, ORB 27 were incorrectly predicted as orange juice adulterated with grapefruit additions. Percentage correctly predicted = 95%.

appears as high in noise and explains only a small percentage of the variance of the input spectral collection is not altogether surprising as it is likely to be variations in minor components which are detected by an adulterant-sensitive model.

Component 1 resembles an inverted orange juice spectrum although the relative magnitude of the water peaks at 1480 and 1940 nm are altered; this is typical for the first PC, which describes variation in a spectral collection due to the light scatter caused by particle size differences. Principal component 2 contains peaks at 1400 and 1980 nm, characteristic of water; thus mois- ture content is the next most significant variable in the

sample set studied. PC3 is more difficult to interpret, although it also appears to contain some information about water. Maxima at 2110 and 2280 nm are close to reported positions for glucose (Osborne et a1 1993) at 2103 and 2275 nm and may be extracting information about juice sugar content. The fourth component contains a number of interesting features. The peak at 2300 and the shoulder at 2345 nm are characteristic of oil absorbance maxima while the troughs at 1500 and 2100 nm have been previously observed in an orange juice collection weights plot by Evans et al (1993); this pair were attributed to sugar absorptions and in that work, as in this, were in opposition to information



1 ORB 10 2 PWT 8 3 Mix 9 4 GRT 9

26 6" 0 0 0 0 0 0

ORB15, ORB31, ORB33, ORB40, were incorrectly predicted as orange juice adulterated with pulpwash additions. ORB37, ORB41 were incorrectly predicted as orange juice adulterated with grapefruit additions. Per- centage correctly predicted = 77%.


Group No of samples No ofsamples No of samples in calibration set in prediction set incorrectly predicted

1 ORB 10 2 PWT 8 3 Mix 9 4 GRT 9 5 PWF 6 GRF

12 11 10 10 10 10

0 0 0 0 7" 0

Seven of the 10 juices which were adulterated with pulpwash were predicted as grapefruit additions. Percentage correctly predicted = 89%.

Detection of' adulteration of' orange juice

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Group N o of samples N o of samples No of samples in calibration set in prediction set incorrectly predicted

1 ORB 10 10 2" 2 PWT 8 8 0 3 Mix 9 10 0 4 GRT 9 10 0 5 PWF 8 8 0 6 GRF 10 lb

a ORB47 is predicted as a grapefruit addition. ORB61 is predicted as a 50 g kg-' pulpwash addition.

One orange juice with 50 g kg-' grapefruit (GRFS1) is predicted as a pulpwash addition. Percentage correctly predicted = 95%.

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1200 1400 1600 1800 2000 2200 2400 WAVELENGTH

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1200 1400 1600 1800 2000 2200 2400

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1200 1400 1600 1800 2000 2200 2400

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PC 12 WEIGHTS Fig 3. Plots of principal component loadings. (1) PC1; (2) PC2; (3) PC3; (4) PC4; ( 5 ) PC12.

84


M Twomey, G Downey, P B McNulty


1 ORB 8 8 2' 2 PWT 8 8 1" 3 Mix 8 8 0 4 GRT 9 8 0 5 PWF 8 8 2b 6 GRF 8 8 0

PWT47 (ORB47 adulterated with 100 g kg-' pulpwash) was predicted as an orange juice adulterated with grapefruit juice.

These orange juices with 50 g kg-' pulpwash additions were predicted as samples of orange juice adulterated with grapefruit juice.

ORB47 was predicted as an orange juice adulterated with grapefruit juice. ORB61 was predicted as an orange juice adulterated with pulpwash. Per- centage correctly predicted = 90%.

about oil. PC12 is a 'noisy' component as may be expected although it does contain one relatively large absorption peak a t 2260 nm which may be hemi- cellulose material.

CONCLUSION

This limited series of experiments has clearly demonstrated the utility of NIR reflectance spectroscopy coupled with a factorial discriminant technique for the detection of certain types of orange juice adulteration. Crucial to the successful implementation of this pro- cedure is a guarantee of the authenticity of those juice samples used for development of the discriminant model and the availability of a n adequate number and selec- tion of samples (both authentic and adulterated) which arises from the empirical nature of the discrimination process. Encouragement may be taken from the obser- vation that while a very small number of purportedly authentic juice samples were classified as adulterated (false negatives), no adulterated samples were classified as authentic (false positives).

ACKNOWLEDGEMENT

The authors wish to gratefully acknowledge the assist- ance of Atlantic Products, Drogheda and the Irish American Partnership, Dublin.

REFERENCES

AFNOR 1989 Union Nationale des Producteurs de Jus de Fruits. AFNOR-UNPJF Fruit Juices Specifications. Associ- ation Francaise de Normalisation, France.

Bertrand D, Courcoux P, Autran J C, Meritan R, Robert P 1990 Stepwise canonical discriminant analysis of contin- uous digitalized signal: Application to chromatograms of wheat proteins. J Chemometrics 4 413-427.

Devaux M F, Bertrand D, Martin G 1986 Discrimination of bread-baking quality of wheat according to their variety by NIR spectroscopy. Cereal Chem 63 (2) 151-154.

Downey G, Robert P, Bertrand D, Kelly P 1990 Classification of commercial skim milk powders according to heat treat- ment using factorial discriminant analysis of near-infrared reflectance spectra. Appl Spectroscopy 44 (10) 150-155.

Evans D G, Scotter C N G, Day L Z, Hall M N 1993 Deter- mination of the authenticity of orange juice by discriminant analysis of near infrared spectra J Near Infrared Spectros-

Hofsommer H 1989 Analytical methodologies of detection as practiced in Europe. Presented at the fruit juice adulteration workshop, 9-10, August Herndon, VA, USA.

McIntyre P S, Rutledge D 1992 A proposed implementation of JCAMP. DX in Near infrared spectroscopy. Bridging the Gap between Data Analysis and NIR Applications. eds Hildrum K I, Isaksson T, Naes T & Tandberg A. Ellis Horwood, Chichester, UK, pp 119-124.

Osborne B G, Fearn T, Hindle P H 1993 Practical NIR spectroscopy with Applications in Food and Beverage Analysis. Longman, Essex, UK.

RSK (Richtwerte und Schwankungsbreiten Kennzahlen) 1987 Apfelsaft, Traubensaft und Orangensaft Values. Flussiges Obst Gmbh, Association of the German Fruit Industry, Bonn, Germany. 1987.

Scotter C N G, Hall M N, Day L Evans D G 1992 The authentication of orange juice and other fruit juices in Near Infrared Spectroscopy. Bridging the Gap between Data Analysis and NIR Applications, eds Hildrum K I, Isaksson T, Naes T & Tandberg A Ellis Horwood, Chichester, UK.

Twomey M 1993 Near infra-red reflectance spectroscopy as a method to detect the adulteration of orange juice with 5 and 10% liquid additions of orange pulpwash, grapefruit juice and a typical sugar-acid-water mixture. MEngSc thesis, National University of Ireland.

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the potential of nir spectroscopy for the detection of the adulteration of orange juice

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