从人工智能角度看高光谱成像技术与机器学习相结合在中药质量控制中的应用:综述。

IF 4.2 2区 化学 Q1 CHEMISTRY, ANALYTICAL Critical reviews in analytical chemistry Pub Date : 2024-01-01 Epub Date: 2023-05-29 DOI:10.1080/10408347.2023.2207652
Yixia Pan, Hongxu Zhang, Yuan Chen, Xingchu Gong, Jizhong Yan, Hui Zhang
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引用次数: 0

摘要

中药是中华瑰宝,中药质量控制至关重要。近年来,随着人工智能(AI)的迅速崛起和高光谱成像(HSI)技术的飞速发展,二者的结合在中药质量评价中得到了广泛应用。机器学习(ML)是人工智能的核心智慧,其在快速分析和更高精度方面的进步提高了高光谱成像技术在中药领域的应用潜力。本文从数据集分割、数据预处理、数据降维、定性或定量模型以及模型性能测量五个方面综述了应用于中医药高光谱数据分析的机器学习。此外,还比较了研究人员提出的用于中医质量评估的不同算法。最后,总结了中医高光谱图像分析所面临的挑战,并展望了未来的工作。
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Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review.

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.

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来源期刊
CiteScore
12.00
自引率
4.00%
发文量
137
审稿时长
6 months
期刊介绍: Critical Reviews in Analytical Chemistry continues to be a dependable resource for both the expert and the student by providing in-depth, scholarly, insightful reviews of important topics within the discipline of analytical chemistry and related measurement sciences. The journal exclusively publishes review articles that illuminate the underlying science, that evaluate the field''s status by putting recent developments into proper perspective and context, and that speculate on possible future developments. A limited number of articles are of a "tutorial" format written by experts for scientists seeking introduction or clarification in a new area. This journal serves as a forum for linking various underlying components in broad and interdisciplinary means, while maintaining balance between applied and fundamental research. Topics we are interested in receiving reviews on are the following: · chemical analysis; · instrumentation; · chemometrics; · analytical biochemistry; · medicinal analysis; · forensics; · environmental sciences; · applied physics; · and material science.
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