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Ying (or initial) Zhou [email protected]

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College of Biosystems Engineering and Food Science

Zhejiang University，Hangzhou 310029

China

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Huirong Xu [email protected]

Affiliation




China

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Yibing Ying ASABE Member

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Xiaoying (or initial) Niu [email protected]

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Publication Information

Pub ID Pub Date

073063 2007 ASABE Annual Meeting Paper


An ASABE Meeting Presentation

Paper Number: 073063

Evaluation of goose down and duck’s down’s content by NIR method

Ying Zhou

College of Biosystems Engineering and Food Science，Zhejiang University，Hangzhou 310029，China [email protected]

Huirong Xu


Yibing Ying*


Xiaoying Niu


Written for presentation at the2007 ASABE Annual International Meeting

Sponsored by ASABEMinneapolis Convention Center

Minneapolis, Minnesota17 - 20 June 2007

Abstract. Down products have large market share, especially in China, but it’s hard to distinguish goose down and duck down by visual appraisal. The traditional method is using microscope to attain the goal. But there are some problems such as limited samples and low accuracy. The objective of this study was to discriminate goose down and duck down with different pureness using Fourier transform (FT) near infrared (NIR) spectroscopy. In this study, 4 groups of goose down and duck down with different proportion were prepared. Group 1 and group 2 are made up of pure goose down


and pure duck down, separately. Group 3 is made up of 1/3 goose down and 2/3 duck down, and group 4 is made up of 2/3 goose down and 1/3 duck down. NIR instrument was used to scan every sample in order to acquire spectrum. This research was based on the hardware of NEXUS intelligent FT-IR spectrometer, with using fiber optic diffuse reflectance accessory. Spectrum of each sample was acquired using diffuse reflectance mode. Chemometrics methods including PCA and DPLS were used to analyse the spectrum attained. Various pre-treatment of experimental data such as MSC and derivative were also used to make better model. The result shows that NIR is a fast and effective method to discriminate between goose down and duck down even in mixture.

Keywords. NIR, goose down, duck down, PCA, DPLS

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1. INTRODUCTIONIn the process of testing goods in international trade, a suitable, quick and legal testing method is such important factor to make sure the result believable and let the trade goes along successfully. China is very abundant in the down resources. As the biggest country of producing and exporting down, we have a long history of down processing, utilizing and trading [1]. Generally, down and products made of it is one of the most important part of china’s export birds product. There is a wide variety of down comforters available, ranging from those made with duck down and feathers to those made from synthetic down alternatives. Now, the feather and down products mainly include the goose down and the duck down two major types in the market (Qisheng Yan et al., 2006).Of all the down comforters, though, goose down is the most luxurious. Duck down content contained in goose down is an important character index. Generally speaking, the best down comes from larger, more mature birds. When age and maturity are equal, goose down is better than duck down. Goose down grows thicker and in bigger clusters than duck down, resulting in a higher-quality comforter. Unlike duck down comforters, goose down does not have an unpleasant odor. In fact, they have virtually no smell at all. Rarity and light color of it make goose down much more expensive than duck down.

Therefore, some factories replace the goose down with the duck’s down or mix goose down into duck’s down to gain more profit. So, accurate recognition of goose down and duck down seems to be the most important. Now, down category recognition is often done by man with a microscope. This method of discrimination demands a lot of training and practice experience. At the same time, many artificial factors in the process may result indifferent examination results of the same sample (Yan et al., 2006). In another way, this kind of method detector samples with a microscope cluster by cluster, so it is time-consuming and not suitable for large quantities of samples’ detection. Therefore, doubtless finding another more quickly and nondestructive way to complete recognition of goose down and duck down make great sense.

Near-infrared (NIR) spectroscopy, allied to multivariate calibration techniques, has become a powerful analytical technique in various fields of science and technology. It is non-destructive, simple, fast, and require no sample pre-treatment, which makes this technology ideally suited for on line process monitoring and quality control(Chen et al., 2004).

Of note in the progress of software are soft independent modeling of class analogy (SIMCA) (Braekeleer et al., 1999;Young-AhWoo. et al., 2005), neural networks, principal component analysis(PCA)( Langeron et al., 2006; Jaillais et al., 2005), discriminant partial least squares (DPLS)( Cozzolino et al.,2005) and various methods for the pretreatment of experimental data such as multiplicative scatter correction(MSC)( Pravdova et al., 2001). And one of the most common applications of near-infrared spectroscopy combined with pattern recognition methods has been to discriminate between samples belonging to one of several distinct groups based on spectral properties(Casale et al., 2006).

Though any report about using NIR to Discrimination of Goose Down and Duck Down hasn’t be found, this convenient measuring technique for product and quality control has been used in the other parts of textile industry, such as the analysis of wool fibers (Flee et al., 2006; Hammersley et al., 1995; Wojciechowska et al.,2000) and their properties; the degree of maturity and sugar content of cotton fibers (Ramey et al., 1982; Barton et al., 2005) and so on.

The object of this trial was as follows:

a. Using NIR method to recognize pure goose down and pure duck down

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b. Discrimination of pure goose down and goose down mixed with different percentage of duck down.

c. Using different methods of chemometrics and pretreatment of spectra to analysis experimental data to deliver the best results

Experimental

Samples

Samples of pure goose down and duck down were applied by Xiaoshan substation of Hangzhou Quality and Technical Supervision Bureau. They were separated into four groups: group 1 containing 30 samples in total and each sample is 0.3 g made up of pure goose down; group 2 containing 30 samples in total and each sample is 0.3 g made up of pure duck down; group 3 containing 40 samples in total and each sample is 0.3 g made up of 0.2 g duck down and 0.1 g goose down; group 4 containing 40 samples in total and each sample is 0.3 g made up of 0.1 g duck down and 0.2 g goose down. Each sample’s weighting error was less than 2%, and samples in group 3 and group 4 were mixed round well. After weighting and being mixed, every sample was put into an airproof plastic bag separately and be detected in two days.

Equipment and method

This research was based on the hardware of NEXUS intelligent FT-IR spectrometer, made by Nicolet instrument company U.S.A, with using fiber optic diffuse reflectance accessory. Spectrum of each sample was acquired using diffuse reflectance method with a Si detector (670–1110 nm), and a 50W quartz halogen light source. Both light source beams and receptor beams were enclosed in the fiber probe randomly.

A personal computer was connected via a PCI card to the spectrometer, and specific softwareOMINIC6.1a (Thermo Electron Corp.) was used to store acquired spectra. The mirror velocity was 0.9494 cm s−1, and the resolution was 8 cm−1 in this work. Each spectrum consisted in an average of 32 successive scans.

In this trial, each sample was foisted into a lighttight plastic cap which has a columniform cavum inside (diameter: 17 mm; deepness: 14 mm). Then the cap was put downwards on the fiber. Consistent weight of every sample makes sure its density above the fiber is same, for getting rid of influence of air. Spectrum of each sample was stored as the logarithm of the reciprocal of the reflected (R) energy (log(1/R)). Before scanning of the samples, spectra of a solid cylinder the end of which is made of standard white Teflon was obtained as background to be subtracted from samples’ spectra.

Spectral data pre-treatment

The quality of the model constructed for the spectral data can depend to a high degree on the quality of the spectra (Pravdova et al., 2001). There are many methods of spectral data preprocessing. Among the most popular are: off-set correction, standard normal variate (SNV) transformation, multiplicative scatter correction (MSC), first and second derivatives smoothed with Savitzki–Golay polynomial filter (Savitzk et al., 1964). SNV transformation removes the multiplicative interference of scatter and particle size. The method is used to remove slope variations for individual spectra. Multiplicative scatter correction is carried out based on the assumption that al samples have the same scatter coefficient at all NIR wavelengths. An ideal spectrum, usually the mean spectrum of a representative data set, is used to estimate the scatter of the spectra. All other spectra are corrected to have the same scatter level as the

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selected one. Each spectrum is shifted and rotated to fit as closely as possible to the chosen mean spectrum (Pravdova et al., 2001).

Chemometrics methods

A key step in the implementation of a successful NIR analysis is the use of chemometric methods to extract analyte information from the spectral background arising from the sample matrix. The incorporation of sophisticated chemometric techniques into the standard software packages used to control spectrometers has made the use of these methods widespread and routine (Gary et al., 2006).

PCAPrincipal component analysis (PCA) was performed before discriminate partial least squares calibration models were developed. It is a well-known technique used for reducing the dimensionality of the data, detecting the number of components and visualising the outliers. It is one of the most commonly applied techniques in multivariate data analysis (Naes et al., 2002; Martens et al., 2001).

PCA is a mathematical procedure for resolving sets of data into orthogonal components whose linear combinations approximate the original data to any desired degree of accuracy(Naes et al., 2002; Martens et al., 2001). The PCA transforms the original independent variables (wavelengths) into new axes, or PCs. These PCs are orthogonal, so that the data set resented on these axes are uncorrelated with each other (Martens et al., 1989). That is to say, the second PC is orthogonal to the first PC covered as much of information of the variation in the data.

DPLSDiscriminant models were developed using the DPLS regression technique as described elsewhere (Cozzolino et al., 2005; Osborne et al.,1993; Cozzolino et al.,2002)

DPLS together with NIR spectroscopy was used in this study to establish a calibration model for distinguishing the different four groups. In this technique, each sample in the calibration set is assigned a dummy variable as a reference value, which is an arbitrary number if the sample belongs to a particular group or if it does not (Cozzolino et al., 2003) -in this case samples of group 1 were assigned a numeric value of 1, group 2, 4, group 3, 2 and group 4, 3. 45 spectrums, about 1/3 of total, were chosen as validation set. And the rest of the spectrums were calibration set. A DPLS model was established using calibration set and then model was used to give prediction value of each sample in validation set, comparing to its true value, for confirming capability of the model. A sample was considered to be correctly categorized if the predicted value lay on the same side of the midpoint of the assigned values.

Results and discussion

Spectral analysis

Figure 1 shows the mean spectrum of goose down and duck down over the 4000-12500 cm-1

region. They are very similar, reflection the common major chemical component of the downs, i.e. protein.

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Figure 1. Mean spectrum of goose down and duck down over the 4000-12500 cm-1 region.

Weak features between 8700-8100cm-1 are from second overtone of C-H stretching vibration. Bands in the 7500–6000 cm-1 region are due to combination bands of the first overtone of C–H, N-H and O–H stretching vibrations, along with the first overtone of C–H deformation vibration. The bands in the 5600-6000cm-1 are attributed to first overtone of C–H and S–H stretching vibrations. Band at 5155cm-1 is assigned to o-h stretching vibration and O–H deformation vibration. it’s the absorption peak of water in down. Band at 4861cm-1 is assigned to N-H symmetrical stretching vibration. Table 1 summaries the approximate band assignments.

Table 1. characteristic NIR bands and their assignments for goose and duck down

Assignments Band position/cm-1

C–H str. second overtone 8423

O–H str. + N–H str first overtone 6689

N–H str. first overtone 5890

S–H str. first overtone 5754

O–H str. + O–H def. (H2O) 5155

N-H sym, str. 4861

C–H str. + C= str 4556

C–H str. + C–H def. 4388

C–H def. second overtone 4230

Str stands for stretching vibration; def stands for deformation vibration and sym stands for symmetrical vibration

PCA

PCA was used on the spectra to examine qualitative differences between group 1 and group 2 to find out whether it is possible to distinguish goose down and duck down by NIR method after

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all of the spectra were pre-processed using MSC and first derivative to highlight the spectral features and reduce baseline variations. The PC1 and PC2 score plot was shown in Fig. 2. Figure 3 shows the three dimensional (3D) principal component score plot using first three score vectors which describe the most spectral variations related to origin, making differentiation clearer. Figure 2 and figure 3 illustrates the effectiveness of the PCA to distinguish goose down and duck down because of their chemical and structural difference. From both of the two plots we can see that the samples are divided completely into two groups without overlapping with each other except a data belong to pure goose down (group 1), which is considered to be the outlier because of error of experiment. PCA gives very important information about the basic data structure regarding a potential capability of separation of objects. Good result of PCA analysis prove that using NIR combined with Chemometrics method can differentiate pure goose and duck down well and give the possibility of next step to distinguish between pure down and mixture of different down content. As PCA only indicates the visualizing result, DPLS was utilized for an improved separation.

Figure 2. Three dimensional score plot using first three principal component scores for group 1 and group 4

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Figure 3. Scores plots of PC1 and PC2 based on MSC and first derivative of spectra.

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To investigate the basis for the observed spectral discrimination between group 1 and group2, the PCA eigenvectors were analyzed (Figure 4). PC1 explain 69.89%of the total variance in the spectral while PC2, PC3 and PC4 account for 7.217%, 4.797% and 3.119%, separately. Comparatively, all of the first four PCs account for 85.02%, and don’t cover very much of the original database. But it was said that when the principal components (PCs) have more than 85% cumulated reliability of the original dataset, these PCs can be used to replace the original one, and the corresponding results could be compellent (He et al ., 2006).

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Figure 4. Eigenvectors for the PCA on pure duck and goose down analyzed

3.3 DPLS

From the result of PCA analysis, an extra sample data belong to group 2 was found far away the others, so it was removed as the outlier during the process of DPLS analysis.

Spectral data of four groups of samples in which content of goose down or duck down is from 0% to 100% with 33.3% interval were all put together to be classified using this method. Table 1 shows comparison of DPLS calibration statistics before and after derivative and MSC. Table 2 shows calibration and validation statistics for the number of incorrectly classified samples in each group. It is amazing that derivative can make such remarkable effect during the process of DPLS. Take the original database as an example, r value=0.528, and No. of incorrectly classified samples was 87 in totally 140 samples without derivative, and after first derivative of the same database, r value increased to 0.999, only one sample was incorrectly classified. But advantage of MSC didn’t put up in this DPLS model.

Figure 5 shows the prediction of four groups in the validation set using the DPLS model depend on the first derivative. From the plot we can see that the four groups are distinctly classified. But symbols at the end of each group were inclined to be in validation set. It is because the chosen samples for validation may have the biggest or smallest value during the process of establishing the DPLS model, and the situation isn’t propitious to make correct prediction by the model, which may be the just reason for bad prediction result of the model using original spectrum.

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Figure 5. Prediction of four groups in the validation set using the DPLS model depend on the first derivative.

Table 1. Comparison of DPLS calibration statistics before and after derivative and MSC

r RMSEC RMSEP Factors

None 0.528 0.880 1.04 2

1st derivative 0.999 0.039 0.102 4

2st derivative 0.998 0.051 0.102 3

MSC 0.484 0.907 0.963 1

MSC +1st derivative 0.996 0.087 0.166 3

MSC +2st derivative 0.996 0.088 0.162 3

RMSEC: root mean square error of calibration; RMSEP: root-mean square error of prediction

Table 2. Down classification result of DPLS models

No. of incorrectly classified samplesCorrect

Percent (%)Group 1 Group2 Group 3 Group 4

Ca set Va set Ca

set Va set Ca set Va set Ca set Va set

None 18 10 17 9 7 6 11 9 39

1st de 0 0 0 0 0 0 0 0 100

2st de 0 0 0 0 0 0 0 0 100

MSC 19 10 15 9 12 8 15 10 30

MSC +1st de 1 0 0 0 0 0 0 0 99

MSC +2st de 1 0 0 0 0 0 0 0 99

Ca: calibration set; Va: validation set; de: derivative

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ConclusionThe results obtained in this trial illuminate the feasibility of using near infrared spectroscopy together with chemometric analysis such as PCA and DPLS, to distinguish goose down and duck down even mixed together. Though this was just a qualitative study, and a farther quantitative trial is to be made in the next step to confirm precision of NIR method of detecting duck down in goose down. It also shows that pre-treatment of spectrum, not always make good effect, e.g. MSC, but sometimes it can make the model really much better, e.g. derivative and it depends on the model itself.

ACKNOWLEDGMENT

We thank Xiaoshan substation of Hangzhou Quality and Technical Supervision Bureau for supplying the samples and the financial support Provided by the Program for New Century Excellent Talents in University (No.NCET-04-0524) and the National Natural Science Foundation of China (GrantNo.30671197) are also gratefully acknowledged.

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