利用近红外非接触式高光谱成像快速评价中药质量的智能过程分析方法:魏福春浓缩液案例研究。

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Phytochemical Analysis Pub Date : 2024-10-01 Epub Date: 2024-06-25 DOI:10.1002/pca.3408
Yi Zhong, Wu Wen, Xiaohui Fan, Ningtao Cheng
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引用次数: 0

摘要

简介中药制剂的质量在很大程度上受中间体(如提取物或浓缩物)质量的影响。然而,在生产过程中以快速、非接触的方式进行质量评估极具挑战性。在此,我们介绍了一种智能高光谱分析方法,该方法将自建的异常区域去除算法与机器学习相结合,并使用由红参、紫河车和枳实制成的传统中药制剂威化春(WFC)的浓缩物证明了其实用性:为了快速、无损地检测中药生产过程中中间体的质量属性,本研究开发了一种智能高光谱分析方法,用于同时定量检测柚皮苷、新橙皮苷、迷迭香酸的含量以及 WFC 浓缩物的相对密度:将样品均匀涂抹在白色平底实心容器上,分批放置在水平样品台上。采集近红外高光谱图像后,首先根据二元灰度图中的像素差值和马哈拉诺比斯距离度量剔除大/小气泡和细小固体等异常像素。然后,使用偏最小二乘法(PLS)和支持向量机(SVM)算法构建质量属性的高光谱定量校准模型。根据这些模型重建高光谱图像,以便在生产过程中直观地评估浓缩物的质量:作为案例研究,同时测定了柚皮苷、新橙皮苷、迷迭香酸和相对密度等 WFC 浓缩物的质量属性,这些定量校正模型的确定系数分别为 0.900、0.891、0.851 和 0.920:本研究提出的方法有利于实时测定粘性样品中的多种属性,具有工业应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent process analysis method for rapidly evaluating the quality of Chinese medicine with near-infrared non-contact hyperspectral imaging: A case study of Weifuchun concentrate.

Introduction: The quality of Chinese medicine preparations can be greatly influenced by the quality of the intermediates such as extracts or concentrates. However, it is highly challenging to evaluate the quality in a rapid and non-contact manner during manufacturing. Here, we introduce an intelligent hyperspectral analysis method integrating a self-built abnormal region removal algorithm with machine learning and demonstrate its utility using the concentrate of Weifuchun (WFC), a traditional Chinese medicine preparation made from Ginseng Radix et Rhizoma Rubra, Rabdosia Amethystoides, and Aurantii Fructus.

Objective: To rapidly and non-destructively detect quality attributes of the intermediates in the manufacturing processes of Chinese medicine, an intelligent hyperspectral analysis method was developed for simultaneously quantifying the contents of naringin, neohesperidin, rosmarinic acid, and relative density of WFC concentrates.

Methodology: Samples were evenly spread on solid white flat bottom containers, which were batch placed on a horizontal sample stage. Subsequent to the acquisition of near-infrared (NIR) hyperspectral images, abnormal pixels such as large/small bubbles and fine solids were first removed according to the differential pixel values in the binary grayscale map and the Mahalanobis distance metric. Then, partial least squares (PLS) and support vector machine (SVM) algorithms were used to construct hyperspectral quantitative calibration models for quality attributes. The hyperspectral images were reconstructed based on these models to visually evaluate the quality of the concentrates during manufacturing.

Results: As a case study, quality attributes of the WFC concentrates including contents of naringin, neohesperidin, rosmarinic acid, and relative density were determined simultaneously, and coefficients of determination of these quantitative correction models were 0.900, 0.891, 0.851, and 0.920, respectively.

Conclusion: The method proposed in this study favors real-time determination of multiple attributes in viscous samples with industrial application prospects.

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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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