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Principles and applications of convolutional neural network for spectral analysis in food quality evaluation: A review

文献类型： 外文期刊

作者： Luo, Na 1 ; Xu, Daming 1 ; Xing, Bin 1 ; Yang, Xinting 1 ; Sun, Chuanheng 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China

4.Beijing Acad Agr & Forestry Sci, 9 Shuguang huayuan Middle Rd, Beijing 100097, Peoples R China

关键词： Convolutional neural network; Spectroscopic technologies; Evaluation; Food quality

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.3； 五年影响因子：4.6 ）

ISSN： 0889-1575

年卷期： 2024 年 128 卷

页码：

收录情况： SCI

摘要： The spectroscopic technologies have been successfully applied to food quality evaluation owing to their abilities of wavelengths being sensitive to biological components of food, and they also have advantages of nondestructive, rapid, convenient. For the food quality evaluation, it's a critical step to build mapping relationships between the spectral data and the specific food quality targets. Convolutional neural network (CNN), as a data-driven deep learning method, provides an end-to-end modeling approach to build mapping without spectral preprocessing techniques, which reduce the need from prior knowledge and human efforts. Meanwhile, in terms of model accuracy, robustness and generalization, CNN also have exhibited superior performances in the recent studies. In this review, we provide a brief overview of the CNN technique firstly, and then systematically analyze existing studies on CNN techniques for spectral analysis into three categories, including end-to-end modeling, spectral dimension reduction and interoperability. Meanwhile, we review the latest applications of CNN techniques for both qualitative and quantitative evaluation of food quality. Then, based on the large number of CNN models in the existing studies, we provide some guidelines for new users to develop a CNN model in their data analysis process, including the design of network structure, tuning the hyperparameters and training strategies. Finally, the advantages, limitations and future perspectives for food quality evaluation by spectral analysis and CNN approaches are also discussed in the study.

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