Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Applied Optics

Volumn 45, Issue 29, 2006, Pages 7679-7683

  Cen, Haiyan ^a   Bao, Yidan ^a   He, Yong ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFRARED SPECTROSCOPY; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; NEURAL NETWORKS; PATTERN RECOGNITION; SPECTRUM ANALYSIS;

BACKPROPAGATION NEURAL NETWORK; BAYBERRY JUICE; PARTIAL LEAST SQUARES (PLS); PLS ANALYSIS;

FRUIT JUICES;

EID: 33750567548     PISSN: 1559128X     EISSN: 15394522     Source Type: Journal    
DOI: 10.1364/AO.45.007679     Document Type: Article

References (19)

1. W. S. Zhang, K. S. Chen, B. Zhang, C. D. Sun, C. Cai, C. H. Zhou, W. P. Xu, W. Q. Zhang, and I. B. Ferguson, "Postharvest responses of Chinese bayberry fruit," Postharvest Biol. Technol. 37, 241-251 (2005).
2. J. Cheng, Q. S. Liu, H. Q. Lu, and Y. W. Chen, "Ensemble learning for independent component analysis," Pattern Recogn. 39, 81-88 (2006).
3. Z. Q. Bian and X. G. Zhang, Pattern Recognition (Qinghua U. Press, 2000).
4. K. Lizuka and H. Hasimoto, "Pattern recognition of NIR spectra: varieties and roasting levels of coffee beams," in Proceedings of the Second NIR International Conference, M. Iwamoto and S. Kawano, eds. (NIR, 1991), pp. 249-258.
5. Y. He, X. L. Li, and Y. N. Shao, "Quantitative analysis of the varieties of apple using near infrared spectroscopy by principal component analysis and BP model," Lecture Notes Artif. Intell. 3809, 1053-1056 (2005).
6. Y. He, S. J. Feng, X. F. Deng, and X. L. Li, "Study on lossless discrimination of varieties of yogurt using the visible/NIR-spectroscopy," Food Res. Int. 39, 645-650 (2006).
7. Z. Seregely, T. Deak, and G. D. Bisztray, "Distinguishing melon genotypes using NIR spectroscopy," Chemom. Intell. Lab. Syst. 72, 195-203 (2004).
8. Y. F. Tang, Z. Y. Zhang, and G. Q. Fan, "Identification of rhubarb samples based on IR spectra by using Takagi-Sugeno fuzzy systems," Spectrosc. Spectral Anal. 25, 521-524 (2005).
9. S. Q. Sun, J. M. Tang, Z. M. Yuan, and Y. Bai, "FTIR and classification study on trueborn tuber dioscorease samples," Spectrosc. Spectral Anal. 23, 258-261 (2003).
10. Y. D. Zhang, K. Dong, and L. F. Ren, "Pattern recognition of laser-induced autofluorescence spectrum from colorectal cancer tissues using partial-least-square and neural network," China Med. Eng. 12, 52-59 (2004).
11. G. Carlomagno, L. Capozzo, G. Attolico, and A. Distante, "Non-destructive grading of peaches by near-infrared spectroscopy," Infrared Phys. Technol. 46, 23-29 (2004).
12. S. Haykin, Neural Network: A Comprehensive Foundation (Macmillan, 1994).
13. G. P. Zhang, "An investigation of neural networks for linear time-series forecasting," Artif. Intell. 137, 239-263 (2002).
14. C. L. Hsieh, S. F. Cheng, and T. T. Lin, "Application of image texture analysis and neural network on the growth stage recognition for head cabbage seedlings," J. Agric. Mach. 6, 1-13 (1997).
15. C. Hsieh, B. P. Chen, and D. E. Chan, "Separating septicemic and normal chicken livers by visible/near-infrared spectroscopy and back-propagation neural networks," Trans. ASAE 45, 459-469 (2002).
16. Y. B. Ying, Y. D. Liu, and Y. Tao, "Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy," Appl. Opt. 44, 5224-5229 (2005).
17. W. Z. Lu, H. F. Yuan, G. T. Xu, and D. M. Qiang, Modern NIR Spectroscopy Analysis Technology (Chinese Shihua Publishing Company, 2000).
18. M. R. Widyanto, H. Novuhara, K. Kawamoto, K. Hirota, and B. Kusumoputro, "Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)," Appl. Soft Comput. 6, 72-84 (2005).
19. T. Naes, T. Isaksson, T. Fearn, and A. M. Davies, A UserFriendly Guide to Multivariate Calibration and Classification (NIR, 2002).