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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Vydané v:Journal of chemometrics Ročník 38; číslo 11
Hlavní autori: Yan, Xiaoqin, Jia, Baoshuo, Long, Wanjun, Huang, Kun, Wang, Tong, Wu, Hailong, Yu, Ruqin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester Wiley Subscription Services, Inc 01.11.2024
Predmet:
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
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Popis
Shrnutí: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 used the LOF algorithm to handle features extracted by the hidden layer in the convolutional autoencoder (CAE). The fused information provides the basis for more accurate pattern recognition, ensures flexibility in model training, and can obtain higher model specificity, which is important in the field of food quality control. To demonstrate the reliability and advantages of OC‐CAE, two EEM cases related to the authentication of food including the Zhenjiang aromatic vinegar (ZAV) case and the camellia oil (CAO) case were studied. The results showed that OC‐CAE identified all abnormal samples in the two cases, reflecting excellent performance in the detection of abnormal samples, and that it, coupled with EEM, would be an effective tool for the authenticity identification of food.
Bibliografia:Funding
This work was supported by the National Natural Science Foundation of China (nos. 22204049 and 22174036), the Key Scientific Research Project of Hunan Provincial Department of Education (no. 23A0028), and the Natural Science Foundation of Hunan Province (no. 2022JJ40042).
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ISSN:0886-9383
1099-128X
DOI:10.1002/cem.3592