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<p>In this study, computer vision and deep learning was combined to develop a rapid method for quantifying the astaxanthin isomer content in krill meal. A total of 310 Antarctic krill meal samples were collected and their astaxanthin isomer content was determined as observed values using high-performance liquid chromatography. A computer vision system was then used to acquire images of the krill meal samples, which were subsequently preprocessed and fed into a Convolutional Neural Network (CNN) to establish a predictive model; its performance was compared with that of a feature-based artificial neural networks model. The results showed that the 13-cistrine (13-Cis) astaxanthin, all-trans astaxanthin, and 9-cistrine (9-Cis) astaxanthin content were distributed in the range of 0–2.05&#xa0;mg/kg, 0.09–62.97&#xa0;mg/kg, and 0–7.58&#xa0;mg/kg, respectively. For the test set, CNN achieved an R<sup>2</sup> of 0.96 in predicting all-trans astaxanthin and an R<sup>2</sup> of 0.88 for 9-Cis astaxanthin. In out-of-sample validation, the CNN achieved mean relative errors of 5.20% and 11.35% for all-trans and 9-Cis astaxanthin, respectively. In conclusion, computer vision combined with CNN offers an efficient, precise, and non-destructive technique for quantitatively analysing astaxanthin isomers in krill meal.</p>

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Rapid Quantitative Analysis of Astaxanthin Isomers in Antarctic Krill Meal by Combining Computer Vision with Convolutional Neural Network

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Quantong Zhang,
  • Yao Zheng,
  • Liu Yang,
  • Shuaishuai Zhang,
  • Quanyou Guo

摘要

In this study, computer vision and deep learning was combined to develop a rapid method for quantifying the astaxanthin isomer content in krill meal. A total of 310 Antarctic krill meal samples were collected and their astaxanthin isomer content was determined as observed values using high-performance liquid chromatography. A computer vision system was then used to acquire images of the krill meal samples, which were subsequently preprocessed and fed into a Convolutional Neural Network (CNN) to establish a predictive model; its performance was compared with that of a feature-based artificial neural networks model. The results showed that the 13-cistrine (13-Cis) astaxanthin, all-trans astaxanthin, and 9-cistrine (9-Cis) astaxanthin content were distributed in the range of 0–2.05 mg/kg, 0.09–62.97 mg/kg, and 0–7.58 mg/kg, respectively. For the test set, CNN achieved an R2 of 0.96 in predicting all-trans astaxanthin and an R2 of 0.88 for 9-Cis astaxanthin. In out-of-sample validation, the CNN achieved mean relative errors of 5.20% and 11.35% for all-trans and 9-Cis astaxanthin, respectively. In conclusion, computer vision combined with CNN offers an efficient, precise, and non-destructive technique for quantitatively analysing astaxanthin isomers in krill meal.