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Rapid on-site differentiation of turbot from different culture modes using miniaturized near infrared spectroscopy coupled with interpretable machine learning

文献类型： 外文期刊

作者： Sun, Yong 1 ; Li, Lin 1 ; Meng, Zhen 1 ; Sun, Huihui 1 ; Cao, Rong 1 ; Liu, Ruiyuan 1 ; Wang, Shanyu 1 ; Liu, Nan 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Qingdao 266071, Shandong, Peoples R China

2.Qingdao Marine Sci & Technol Ctr, Lab Marine Drugs & Bioprod, Qingdao 266237, Shandong, Peoples R China

关键词： Miniaturized NIR spectroscopy; Turbot; Culture mode; Interpretable machine learning; On-site differentiation

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：5.1； 五年影响因子：4.7 ）

ISSN： 0026-265X

年卷期： 2024 年 207 卷

页码：

收录情况： SCI

摘要： Culture modes have a significant impact on the quality of farmed fish, such as turbots. It is urgently needed to monitor and manage turbot quality during the entire aquaculture process. Recent advances in miniaturized near infrared spectroscopy allow rapid, on-site, and non-invasive analysis of biological tissues. This proof-of-principle investigation tested whether miniaturized near infrared spectroscopy techniques, coupled with interpretable machine learning, could be applied in living turbots to differentiate their culture modes. In this work, spectra of turbots of two culture modes were collected, and spectra analysis including preliminary preprocessing, principal component analysis, machine learning modeling, and subsequent interpretation were conducted. The results show that, compared with linear discriminative analysis, support vector machine, and random forest, an XGBoost model with proper preprocessed spectra data achieved the best differentiation performance, with validation accuracy as high as 97.62 %. The precision, recall, and F1-score were 97.37 %, 97.92 %, and 97.58 %, respectively. The model interpretation results show that the absorbance at 1542.0 nm, 1415.6 nm, and 1412.3 nm held dominant positions in determining turbot culture mode. Miniaturized NIR spectroscopy coupled with machine learning proved valuable for on-site noninvasive assessment of turbot properties in vivo, providing a promising technique for more efficient and sustainable aquaculture.

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