Attenuated total reflectance-flourier transformed infrared spectroscopy (ATR-FTIR) coupled with deep learning: A rapid method for geographical origin identification of sea cucumber Apostichopus japonicus

文献类型： 外文期刊

第一作者： Sun, Yong

作者： Sun, Yong;Liu, Nan;Zhao, Ling;Liu, Qi;Wang, Shanshan;Sun, Guohui;Zhao, Yanfang;Zhou, Deqing;Cao, Rong

作者机构：

关键词： Apostichopus japonicas; ATR-FTIR spectroscopy; Deep learning; Geographical origin; Rapid discrimination

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：4.9； 五年影响因子：4.5 ）

ISSN： 0026-265X

年卷期： 2024 年 204 卷

页码：

收录情况： SCI

摘要： The sea cucumber Apostichopus japonicus is one of the China's most prized seafoods, its geographical origin is a decisive factor for its economic value. To quickly and effectively identify the geographical origin of this species, ATR-FTIR spectroscopy coupled with deep learning methods was applied in the present study. Compared to conventional machine learning models (support vector machine, random forest and lightGBM), the proposed one-dimensional convolutional neural network (1D-CNN) model demonstrates significant advantages in terms of data automation and accuracy. The model can correctly classify sea cucumbers from different sea regions up to 90.7%, among which 100% identification of Apostichopus japonicus from the East China Sea can be realized. The results proved that ATR-FTIR spectroscopy combined with 1D-CNN could be used as a rapid and effective technique for tracing the geographical origin of Apostichopus japonicus.

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