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Discrimination of Producing Areas of Auricularia auricula Using Visible/Near Infrared Spectroscopy

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Abstract

Visible and near infrared (Vis/NIR) spectroscopy combined with chemometric methods was applied for the discrimination of producing areas of Auricularia auricula. Four major varieties of commercial A. auricula were prepared for spectral acquisition. Some pretreatments were performed, such as Savitzky–Golay smoothing, standard normal variate, and the first and second Savitzky–Golay derivative. The scores of the top four latent variables, extracted by partial least squares, were considered as the inputs of back propagation neural network (BPNN) and least squares-support vector machine (LS-SVM). The performance was validated by 60 validation samples. The excellent recognition ratio was 98.3% by BPNN and 96.7% by LS-SVM model with the threshold prediction error ±0.1. The results indicated that Vis/NIR spectroscopy could be used as a rapid and high-precision method for the discrimination of different producing areas of A. auricula by both BPNN and LS-SVM methods.

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Acknowledgements

This study was supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, PRC, Natural Science Foundation of China (Project No: 30671213).

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  1. College of Biosystems Engineering and Food Science, Zhejiang University, 268 Kaixuan Road, Hangzhou, 310029, Zhejiang, People’s Republic of China

    Fei Liu & Yong He

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  1. Fei Liu
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  2. Yong He
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Correspondence to Yong He.

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Liu, F., He, Y. Discrimination of Producing Areas of Auricularia auricula Using Visible/Near Infrared Spectroscopy. Food Bioprocess Technol 4, 387–394 (2011). https://doi.org/10.1007/s11947-008-0174-7

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