Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast
文献类型： 外文期刊
作者： Qiu, Ruicheng 1 ; Zhao, Yinglei 4 ; Kong, Dandan 2 ; Wu, Na 2 ; He, Yong 2 ;
作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
2.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
3.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China
4.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310000, Peoples R China
关键词： Food pathogens; Hyperspectral imaging; Convolutional neural network; Deep learning; Chicken
期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.831； 五年影响因子：4.073 ）
ISSN： 1386-1425
年卷期： 2023 年 285 卷
页码：
收录情况： SCI
摘要： Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral infor-mation and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classi-fication models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.
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