Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning

文献类型： 外文期刊

第一作者： Sun, Dawei

作者： Sun, Dawei;Zhou, Chengquan;Ye, Hongbao;Hu, Jun;Li, Li

作者机构：

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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