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  • Title: [Producing area identification of Letinus edodes using mid-infrared spectroscopy].
    Author: Zhu ZY, Zhang C, Liu F, Kong WW, He Y.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Mar; 34(3):664-7. PubMed ID: 25208387.
    Abstract:
    In the present study, Mid-infrared spectroscopy was used to identify the producing area of Letinus edodes, and relevance vector machine (RVM) was put forward to build classification models as a novel classification technique, and they obtained good performances. The head and the tail of the acquired mid-infrared spectra with the absolute noise were cut off, and the remaining spectra in the range of 3,581-689 cm(-1) (full spectra) of Letinus edodes were preprocessed by multiplicative scatter correction (MSC). Five classification techniques, including partial least Squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor algorithm (KNN), support vector machine (SVM) and RVM, were applied to build classification models based on the preprocessed full spectra. All classification models obtained classification accuracy over 80%, KNN, SVM and RVM models based on full spectra obtained similar and good performances with classification accuracy over 90% in both the calibration set and the prediction set. The weighted regression coefficients (Bw) were used to select effective wave numbers of mid-infrared spectra and 6 effective wave numbers in total were selected on the basis of the weighted regression coefficients of PLS-DA model based on full spectra. PLS-DA, KNN, SVM and RVM models were built using these effective wave numbers. Compared with the classification models based on full spectra, PLS-DA models based on effective wave numbers obtained relatively worse results with classification accuracy less than 80%, and KNN, SVM and RVM obtained similar results in both calibration set and prediction set with classification accuracy over 90%. RVM performed well with classification rate over 90% based on full spectra and effective wave numbers. The overall results indicated that producing area of Letinus edodes could be identified by mid-infrared spectroscopy, while wave number selection and the RVM algorithm could be effectively used in mid-infrared spectroscopy analysis. In this study, mid-infrared spectroscopy was successfully applied to identify the producing area of Letinus edodes, which could provide a new concept for quality analysis of Letinus edodes and other agricultural products, and the application of mid-infrared spectroscopy had practical significance.
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