基于多变量数据融合与 DBN 分类算法的伊萨提斯 Radix 起源追踪

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-07-26 DOI:10.1016/j.chemolab.2024.105190

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引用次数: 0

摘要

本研究收集了不同地区（安徽、湖北、陕西、新疆）山地乌药的色度值、质地、成分含量等多维特征数据。通过多元统计分析，选取了 44 个表征因子（VIP >1，P <0.05）来区分异地药材的产地。此外，通过将 44 个特征因子与深度信念网络（DBN）分类算法相结合，创建并优化了一种独特的人工智能算法。与传统的判别分析方法相比，这种新方法的准确性有了显著提高，对伊沙替迪菝葜产地的判别率达到了 100%，对伊沙替迪菝葜的溯源准确率也达到了 100%。这项研究有助于开发基于数据融合的智能算法，以追踪更多农产品的产地。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Tracing the origin of isatidis radix based on multivariate data fusion combined with DBN classification algorithm

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Tracing the origin of isatidis radix based on multivariate data fusion combined with DBN classification algorithm

In this study, multidimensional characterization data such as chromaticity value, texture and compositional content of Isatidis Radix from different regions (Anhui; Hubei; Shaanxi; Xinjiang) were collected. By multivariate statistical analysis, 44 characterization factors (VIP >1, P < 0.05) were selected to distinguish the origin of Isatidis Radix. In addition, a unique artificial intelligence algorithm was created and optimized by merging 44 characterization factors with the deep belief network (DBN) classification algorithm. Compared with the traditional discriminant analysis method, the accuracy of this new method was significantly improved, and the discrimination rate of Isatidis Radix origin reached 100 %, and the traceability accuracy of Isatidis Radix also reached 100 %. This study supports the development of intelligent algorithms based on data fusion to track the origin of more agricultural products.

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.