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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