Application of a hybrid variable selection method for determination of carbohydrate content in soy milk powder using visible and near infrared spectroscopy

Journal of Agricultural and Food Chemistry

Volumn 57, Issue 2, 2009, Pages 334-340

  Chen, Xiaojing ^a   Lei, Xinxiang ^b  

^a WENZHOU UNIVERSITY   (China)

^b XIAMEN UNIVERSITY   (China)

Author keywords

Simulated annealing; Soy milk powder; Variable selection; Visible and near infrared spectroscopy; Wavelet packet transform

Indexed keywords

GLYCINE MAX;

CARBOHYDRATE;

ARTICLE; CHEMISTRY; EVALUATION; METHODOLOGY; NEAR INFRARED SPECTROSCOPY; POWDER; SOYBEAN MILK;

CARBOHYDRATES; POWDERS; SOY MILK; SPECTROSCOPY, NEAR-INFRARED;

EID: 59849087955     PISSN: 00218561     EISSN: None     Source Type: Journal    
DOI: 10.1021/jf8025887     Document Type: Article

References (29)

1. Sloan. A. E. Healthy vending and other emerging trends. Food Technol. 2005, 59, 26-35.
2. Rosenthal, I.; Rosen. B. 100 years of measuring the fat content of milk. J. Chem. Educ. 1993, 70, 480-482.
3. Kamizake, N.; Gonçalves, M.; Zaia, C.; Zaia, D. Determination of total proteins in cow milk powder samples: a comparative study between the Kjeldahl method and spectrophotometric methods. J. Food Compos. Anal. 2003. 16. 507-516.
4. Luykx, D.; Cordewener, J.; Ferranti, P.; Frankhuizen, R.; Bremer, M.; Hooijerink, H.; America, A. Identification of plant proteins in adulterated skimmed milk powder by high-performance liquid chromatography-mass spectrometry. J. Chromatogr. A 2007, 1164, 189-197.
5. Wagley, D.; Schmiedel, G.; Mainka, E.; Ache, H. J. Direct determination of some essential and toxic elements in milk and milk power by graphite furnace atomic absorption spectrometry. At. Spectrosc. 1989, 10, 106-111.
6. Hu, F.; Furihata, K.; Kato, Y.; Tanokura, M. Nondestructive quantification of organic compounds in whole milk without pretreatment by two-dimensional NMR spectroscopy, J. Agric. Food Chem. 2007, 55, 4307-4311.
7. Yan. Y. L.; Zhao. L. L.; Han. D. H; Yang. S. M. The Foundation and Application of Near-Infrared Spectroscopy Analysis, 1st ed.; China Light Industry Press: Beijing, China. 2005; p 32.
8. Borin, A.; Ferrao, M. F.; Mello, C: Maretto, D. A.: Poppi, R. J. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Anal. Chim. Acta 2006. 579, 25-32.
9. Maraboli, A.; Cattaneo, T. M. P.; Giangiacomo, R. Detection of vegetable proteins from soy, pea and wheat isolates in milk powder by near infrared spectroscopy. J. Near Infrared Spectrosc. 2002, 10, 63-69.
10. Wu, D.; Feng, S.; He, Y. Infrared spectroscopy technique for the nondestructive measurement of fat content in milk powder. J. Dairy Sci. 2007, 90, 3613-3619.
11. Wu, D.; He, Y.; Feng, S.: Sun, D. W. Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM. J. Food Eng. 2008, 84, 124-131.
12. Wu, D.; Feng, S.; He, Y. Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis. J. Dairy Sci. 2008, 91, 939-949.
13. Wu, D.; He, Y.; Feng, S. Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment. Anal. Chim. Acta 2008. 610, 232-242.
14. Shao, X. G.; Wang, F.; Chen, D.; Su, Q. D. A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables. Anal. Bioanal. Chem. 2004, 378, 1382-1387.
15. Ying, Y. B.; Liu, Y. D. Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy. J. Food Eng. 2008, 84, 206-213.
16. Barnes, R. J.; Dhanoa, M. S.; Lister, J. S. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. J. Appl. Spectrosc. 1989, 43, 772-777.
17. Cocchi. M.; Corbellini, M.; Foca, G.; Lucisano, M.; Pagani, A.; Tassi, L.: Ulrici, A. Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on N1R spectra. Anal. Chim. Acta 2005, 544, 100-107.
18. Chen, D.; Shao, X. G.; Hu, B.; Su, Q. D. A Background and noise elimination method for quantitative calibration of near infrared spectra. Anal. Chim. Acta 2004, 511, 37-45.
19. Mallat, S. G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11, 674-693.
20. Cocchi, M.; Seeber, R.; Ulrici, A. WPTER: Wavelet packet transform for efficient pattern recognition of signals. Chemom. Intell. Lab. Syst. 2001, 57, 97-119.
21. Kirkpatrick, S.; Vecchi, M. P. Optimization by simulated annealing. Science 1983. 220, 671-680.
22. Hijrchner, U.: Kalivas, J. H. Further investigation on a comparative study of simulated annealing and genetic algorithm for wavelength selection. Anal. Chim. Acta 1995, 311, 1-13.
23. Suman, B. Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem. Comput. Chem. Eng. 2004, 28, 1849-1871.
24. Geng, X.; Xu, J.; Xiao, J.; Pan, L. A simple simulated annealing algorithm for the maximum clique problem. Inf. Sci. 2007, 177, 5064-5071.
25. Salinas, S. V.; Chang, C. W.; Liew, S. C. Multiparameter retrieval of water optical properties from above-water remote-sensing reflectance using the simulated annealing algorithm. Appl. Opt. 2007. 46, 2727-2742.
26. Ghazanfari, M.; Alizadeh, S.; Fathian, M.; Koulouriotis, D. E. Comparing simulated annealing and genetic algorithm in learning FCM. Appl. Math. Comput. 2007. 192, 56-68.
27. Liu, F.; He, Y. Classification of brands of instant noodles using Vis/NIR spectroscopy and chemometrics. Food Res. Int. 2008, 41, 562-567.
28. Wang, F. Y. Modern Techniques for Food Fermentation; China Light Industry Press: Beijing, China, 2004.
29. Williams, P. C. Implementation of near-infrared technology. In Near-Infrared Technology in the Agricultural and Food Industries, 2nd ed.; Williams, P., Norris. K., Eds.; American Association of Cereal Chemists: St. Paul, MN, 2001; pp 145-169.