Comparison of Data Pre-processing in Pattern Recognition of Milk Powder Vis/NIR Spectra
The effect of data pre-processing, including standard normal variate transformation (SNV), Savitzky-Golay first derivative transformation (S. Golay 1st-Der) and wavelet transforms (WT) on the identification of infant milk powder varieties were investigated. The potential of visible and near infrared spectroscopy (Vis/NIRS) for its ability to nondestructively differentiate infant formula milk powder varieties was evaluated. A total of 270 milk powder samples (30 for each variety) were selected for Vis/NIRS on 325-1075 nm using a field spectroradiometer. Partial least squares (PLS) analysis was performed on the processed spectral data. In terms of the total classification results, the model with the wavelet transforms processed data is the best, and its prediction statistical parameters were r2 of 0.978, SEP of 0.435 and RMSEP of 0.413. This research shows that visible and near infrared reflectance spectroscopy has the potential to be used for discrimination of milk powder varieties, and a suitable pre-processing method should be selected for spectrum data analysis.
KeywordsPartial Less Square Milk Powder Wavelet Transform Partial Less Square Model Standard Normal Variate
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- 1.He, Y., Li, X.L., Shao, Y.N.: Quantitative Analysis of the Varieties of Apple Using Near Infrared Spectroscopy by Principal Component Analysis and BP Model. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1053–1056. Springer, Heidelberg (2005)Google Scholar
- 2.He, Y., Feng, S.J., Deng, X.F., Li, X.L.: Study on Lossless Discrimination of Varieties of Yogurt Using the Visible/NIR-spectroscopy. Food Research International 39(6) (2006)Google Scholar
- 7.Chu, X.L., Yuan, H.F., Lu, W.Z.: Progress and Application of Spectral Data Pretreatment and Wavelength Selection Methods in NIR Analytical Technique. Progress in Chemistry 16(4), 528–542 (2004)Google Scholar
- 8.Liang, Y.C., Yi, Z.S.: The Handbook of Analytical Chemistry: Chemistry Metrology. Chemical Industry Press, Beijing (2001)Google Scholar
- 9.Staszewski, W.J.: Wavelet Based Compression and Feature Selection for VibrationGoogle Scholar
- 10.Analysis. Journal of Sound and Vibration 211(5), 735–760 (1998)Google Scholar
- 12.Wang, F., Chen, D., Shao, X.G.: Application of Wavelet Transform and Partial Least Square in Prediction of Common Chemical Compositions in Tobacco Samples. Tobacco Science & Technology/ Inspection & standard (3), 31–34 (2004)Google Scholar
- 13.FECIT Sci-Tech.: The Theory of Wavelet Analysis and MATLAB 7 Application. Publishing House of Electronics Industry, Beijing (2005)Google Scholar
- 15.Dagnew, M., Crowe, T.G., Schoenau, J.J.: Sensing of Hog Manure Nutrients with Reflectance Spectroscopy. In: CSAE/SCGR-NABEC Meeting, July 8-11, Guelph, Canada (2001)Google Scholar
- 16.Naes, T., Isaksson, T., Fearn, T., Davies, A.M.: A User-friendly Guide to Multivariate Calibration and Classification. NIR Publications, UK (2002)Google Scholar