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Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

文献类型： 外文期刊

作者： Chen, Yunfeng 1 ; Chen, Yue 2 ; Feng, Xuping 1 ; Yang, Xufeng 1 ; Zhang, Jinnuo 1 ; Qiu, Zhengjun 1 ; He, Yong 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Hort, Hangzhou 310021, Zhejiang, Peoples R China

关键词： orchids; variety identification; FTIR spectroscopy; stacked sparse auto-encoder

期刊名称：MOLECULES （ 影响因子：4.411； 五年影响因子：4.587 ）

ISSN： 1420-3049

年卷期： 2019 年 24 卷 13 期

页码：

收录情况： SCI

摘要： The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000-550 cm(-1) were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

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