Discrimination of pear varieties using three classification methods based on near-infrared spectroscopy

Transactions of the ASABE

Volumn 50, Issue 4, 2007, Pages 1355-1361

  Fu, Xiaping ^a   Zhou, Ying ^a   Ying, Yibin ^a   Lu, Huishan ^a   Xu, Huirong ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Discriminant analysis; Fruit; Near infrared; Neural network; Partial least squares; Variety classification

Indexed keywords

CALIBRATION MODELS; DISCRIMINANT MODELS; PARTIAL LEAST SQUARES; VARIETY CLASSIFICATION;

FOURIER TRANSFORM INFRARED SPECTROSCOPY; LEAST SQUARES APPROXIMATIONS; NEAR INFRARED SPECTROSCOPY; NEURAL NETWORKS; REGRESSION ANALYSIS;

FRUITS;

CLASSIFICATION; DICOTYLEDON; FRUIT; INFRARED SPECTROSCOPY; NEAR INFRARED;

ASIA; CHINA; EURASIA; FAR EAST; ZHEJIANG;

PYRUS;

EID: 34548714243     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Chen, S., W. H. Chang, and K. W. Hsieh. 1998. The study on prediction models for determination of sugar content in fruit juice. J. Agric. Machinery (Taipei) 7(3): 41-60.
2. Clark, C. J., V. A. McGlone, C. Requejo, A. White, and A. B. Woolf. 2003. Dry matter determination in 'Hass' avocado by NIR spectroscopy. Postharvest Biol. Tech. 29(3): 300-307.
3. Cozzolino, D., H. E. Smyth, and M. Gishen. 2003. Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. J. Agric. Food Chem. 51(26): 7703-7708.
4. Ding, H. B., and R. J. Xu, 2000. Near-infrared spectroscopic technique for detection of beef hamburger adulteration. J. Agric. Food Chem. 48(6): 2193-2198.
5. Downey, G., P. Mcintyre, and A. N. Davies. 2002. Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the eastern Mediterranean by visible and near-infrared spectroscopy. J. Agric. Food Chem. 50(20): 5520-5525.
6. Downey, G., V. Fouratier, and J. D. Kelly. 2003. Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy. J. Near Infrared Spectroscopy 11(6): 447-456.
7. Esteban-Diez, I., J. M. Gonzalez-Saiz, C. Saenz-Gonzalez, and C. Pizarro. 2007. Coffee varietal differentiation based on near infrared spectroscopy. Talanta 71(1): 221-229.
8. Hajmeer, M., and I. Basheer. 2003. Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiol. 20(1): 43-55.
9. Kim, J., A. Mowat, P. Poole, and N. Kasabov. 2000. Linear and non-linear pattern recognition models for classification of fruit from visible-near infrared spectra. Chemometrics Intelligent Lab. Syst. 51(2): 201-216.
10. Lafargue, M. E., M. Feigerg. J. Daudin, and D. N. Rutledge. 2003. Detection of heterogeneous wheat samples using near infrared spectroscopy. J. Near Infrared Spectroscopy 11(2): 109-121.
11. Liu, Y., and Y. Ying. 2005. An optical system for measurement of internal pear quality using near-infrared spectroscopy. J. Optical Eng. 47(7): 1-5.
12. Lu, R., and D. A. Ariana. 2002. Near-infrared sensing technique for measuring internal quality of apple fruit. Applied Eng. in Agric. 18(5): 585-590.
13. Marten, G. C., J. S. Shenk, and F .E. Barton. 1989. Near infrared reflectance spectroscopy (NIRS): Analysis of forage quality. Agriculture Handbook No. 643. Washington, D.G: USDA.
14. Specht. D. F. 1990. Probabilistic neural networks. Neural Networks 3(1): 109-118.
15. Sun, G., X. Dong, and G. Xu. 2006. Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier. Neurocomputing 69(4-6): 387-402.
16. Woo, Y. A., C. H. Cho, H. J. Kim, J. S. Yang, and K. Y. Seong. 2002. Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES. Microchemical J. 73(3): 299-306.
17. Woo, Y. A., C. H. Cho, H. J. Kim, K. R. Ze, and H. Chung. 2005. Near-infrared spectroscopy for the non-destructive and fast determination of geographical origin of Angelicae gigantis radix. J. Pharm. Biomed Analysis 36(5): 955-959.
18. Xue, C. X., X. Y. Zhang, M. C. Liu, Z. D. Hua, and B. T. Fan. 2005. Study of probabilistic neural networks to classify the active compounds in medicinal plants. J. Pharm. Biomed. Analysis 38(3): 497-507.
19. Yan, Y. L., L. L. Zhao, D. H. Han, and S. M. Yang. 2005. Basics and Application of Near Infrared Spectral Analysis. Bejing, China: China Light Industry Press.
20. Ying, Y., Y. Liu, J. Wang, X. Fu, and Y. Li. 2005. Fourier transform near-infrared determination of total soluble solids and available acid in intact peaches. Trans. ASAE 48(1): 229-234.