Abstract
A simple, fast and nondestructive approach was put forward to classify rice seed of different storage time. This discrimination was conducted by integrated with wavelet transform (WT), principal component analysis (PCA) and artificial neural networks (ANN) based on near infrared reflectance spectroscopy (NIRS). Four classes’ samples from four different storage times were used for Vis/NIR spectroscopy on 325-1075 nm using a field spectroradiometer. WT and PCA were used to reduce spectral data dimension and extract diagnostic information from spectra data. The first eight PCs, which accounted for 99.94% of the raw spectral variables, were used as input of the ANN model. The ANN model yielded high discrimination accuracy. The discrimination accuracy was 97.5% for rice seed samples of four different storage years.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Donald, D., Everingham, Y., Coomans, D.: Integrated Wavelet Principal Component Mapping for Unsupervised Clustering on Near Infrared Spectra. Chemometrics and Intelligent Laboratory Systems 77, 32–42 (2005)
He, Y., Li, X.L., Deng, X.F.: Discrimination of Varieties of Tea Using Near Infrared Spectroscopy by Principal Component Analysis and BP Model. Journal of Food Engineering 79, 1238–1242 (2007)
Boscaini, E., Mikoviny, T., Wisthaler, A., Hartungen, E.V., Mark, T.D.: Characterization of Wine with PTR-MS. International Journal of Mass Spectrometry 239, 215–219 (2004)
Vannucci, M., Sha, N.J., Brown, P.J.: NIR and Mass Spectra Classification: Bayesian Methods for Wavelet-based Feature Selection. Chemometrics and Intelligent Laboratory System 77, 139–148 (2005)
Cocchi, M., Corbellini, M., Foca, G., Lucisano, M., Pagani, M.A., Tassi, L., Alessandro, U.: Classification of Bread Wheat Flours in Different Quality Categories by a Wavelet-based Feature Selection/Classification Algorithm on NIR Spectra. Analytica Chimica Acta 544, 100–107 (2005)
Karoui, R., Mouazen, A.M., Ramon, H., Schoonheydt, R., Baerdemaeker, J.D.: Feasibility Study of Discriminating the Manufacturing Process and Sampling Zone in Ripened Soft Cheeses Using Attenuated Total Reflectance MIR and Fiber Optic Diffuse Reflectance VIS-NIR Spectroscopy. Food Research International 39, 588–597 (2006)
Pontes, M.J.C., Santos, S.R.B., Araujo, M.C.U., Almeida, L.F., Lima, R.A.C., Gaiao, E.N., Souto, U.T.C.P.: Classification of Distilled Alcoholic Beverages and Verification of Adulteration by Near infrared Spectrometry. Food Research International 39, 182–189 (2006)
Dardenne, P., Pierna, J.A.F.: A NIR Data Set Is the Object of a Chemometric Contest at ’Chimiometrie 2004’. Chemometrics and Intelligent Laboratory System 80, 236–242 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Dengsheng, Z., Xiaoli, L. (2007). Determination of Storage Time of Rice Seed Using ANN Based on NIRS. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_127
Download citation
DOI: https://doi.org/10.1007/978-3-540-72395-0_127
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
eBook Packages: Computer ScienceComputer Science (R0)