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基于人工智能视角的高光谱成像技术与机器学习在中药质量控制中的应用综述。

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review.

机构信息

College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China.

Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

出版信息

Crit Rev Anal Chem. 2024;54(8):2850-2864. doi: 10.1080/10408347.2023.2207652. Epub 2023 May 29.

Abstract

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

摘要

中医是中国的瑰宝,而中医的质量控制至关重要。近年来,随着人工智能(AI)的快速崛起和高光谱成像(HSI)技术的飞速发展,两者的结合已广泛应用于中药的质量评价。机器学习(ML)是 AI 的核心智慧,其在快速分析和更高精度方面的进步提高了将 HSI 应用于中医领域的潜力。本文综述了机器学习(ML)在中药高光谱数据分析中的五个方面:数据集划分、数据预处理、数据降维、定性或定量模型以及模型性能测量。还比较了研究人员为中药质量评估提出的不同算法。最后,总结了中药高光谱图像分析中面临的挑战,并展望了未来的工作。

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