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Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species

1
College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China
2
College of Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Jesus Simal-Gandara
Molecules 2019, 24(12), 2210; https://doi.org/10.3390/molecules24122210
Received: 4 May 2019 / Revised: 6 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
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Abstract

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment. View Full-Text
Keywords: Ganoderma; authentication; attenuated total reflection-Fourier transform infrared spectroscopy; chemometrics; random forest; support vector machine; partial least squares discriminant analysis Ganoderma; authentication; attenuated total reflection-Fourier transform infrared spectroscopy; chemometrics; random forest; support vector machine; partial least squares discriminant analysis
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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 (CC BY 4.0).

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Wang, Y.-Y.; Li, J.-Q.; Liu, H.-G.; Wang, Y.-Z. Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species. Molecules 2019, 24, 2210.

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