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Journal of Chemistry
Volume 2015, Article ID 692983, 7 pages
http://dx.doi.org/10.1155/2015/692983
Research Article

The Feasibility of Using Near Infrared Spectroscopy for Rapid Discrimination of Aged Shiitake Mushroom (Lentinula edodes) after Long-Term Storage

1College of Material and Chemical Engineering, Tongren University, Tongren, Guizhou 554300, China
2College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
3College of Life Sciences, China Jiliang University, Hangzhou 310018, China
4College of Chemistry and Life Science, Chuxiong Normal University, Chuxiong 675000, China

Received 17 March 2015; Accepted 4 June 2015

Academic Editor: Luciana M. Coelho

Copyright © 2015 Lu Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Long-term storage can largely degrade the taste and quality of dried shiitake mushroom (Lentinula edodes). This paper aimed at developing a rapid method for discrimination of the regular and aged shiitake by near infrared (NIR) spectroscopic analysis and chemometrics. Regular () and aged () samples of shiitake were collected from six main producing areas in two successive years (2013 and 2014). NIR reflectance spectra (4000–12000 cm−1) were measured with finely ground powders. Different data preprocessing method including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV) were investigated to reduce the unwanted spectral variations. Partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) were used to develop classification models. The results indicate that SNV and D2 can largely enhance the classification accuracy. The best sensitivity, specificity, and accuracy of classification were 0.967, 0.953, and 0.961 obtained by SNV-LS-SVM and 0.933, 0.930, and 0.932 obtained by SNV-PLSDA, respectively. Moreover, the low model complexity and the high accuracy in predicting objects produced in different years demonstrate that the classification models had a good generalization performance.