Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning
文献类型： 外文期刊
作者： Sun, Dawei 1 ; Zhou, Chengquan 1 ; Hu, Jun 2 ; Li, Li 3 ; Ye, Hongbao 1 ;
作者机构： 1.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310000, Peoples R China
2.Zhejiang Acad Agr Sci, Food Sci Inst, Hangzhou 310000, Peoples R China
3.Ocean Univ China, Minist Educ, Key Lab Mariculture, Qingdao 266003, Peoples R China
关键词： Salmonids; Off-flavor; Hyperspectral imaging; Geosmin; 2-Methylisoborneol; Machine learning
期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）
ISSN： 0308-8146
年卷期： 2023 年 408 卷
页码：
收录情况： SCI
摘要： Off-flavors can have significant impacts on the quality of salmonid products. This study investigated the possi-bility of comprehensive off-flavor profiling considering both olfactory and taste sensory perspectives by combining near-infrared hyperspectral imaging (NIR-HSI) and machine/deep learning. Four feature extraction algorithms were employed for the extraction and interpretation of spectral fingerprint information regarding off -flavor-related compounds. Classification models, including the partial least squares discriminant analysis, least -squares support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were constructed using the full wavelengths and selected spectral features for the identification of off -flavor salmonids. The 1DCNN achieved the highest discrimination accuracy with full and selected wavelengths (i. e., 91.11 and 86.39 %, respectively). Furthermore, the prediction and visualization of off-flavor-related com-pounds were achieved with acceptable performances (R2 > 0.6) for practical applications. These results indicate the potential of NIR-HSI for the off-flavor profiling of salmonid muscle samples for producers and researchers.
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