A Novel One‐Class Convolutional Autoencoder Combined With Excitation–Emission Matrix Fluorescence Spectroscopy for Authenticity Identification of Food

ABSTRACT In this work, a novel one‐class classification algorithm one‐class convolutional autoencoder (OC‐CAE) was proposed for the detection of abnormal samples in the excitation–emission matrix (EEM) fluorescence spectra dataset. The OC‐CAE used Boxplot to analyze the reconstruction errors and use...

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Veröffentlicht in:	Journal of chemometrics Jg. 38; H. 11
Hauptverfasser:	Yan, Xiaoqin, Jia, Baoshuo, Long, Wanjun, Huang, Kun, Wang, Tong, Wu, Hailong, Yu, Ruqin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester Wiley Subscription Services, Inc 01.11.2024
Schlagworte:	abnormal sample detection Algorithms Authenticity autoencoder convolutional Emission spectra Emissions Excitation spectra excitation–emission matrix fluorescence Fluorescence Food food fraud Food quality one‐class classification Pattern analysis Pattern recognition Quality control Spectrum analysis
ISSN:	0886-9383, 1099-128X
Online-Zugang:	Volltext
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