红外光谱效应与深度学习相结合，预测 Gentiana rigescens Franch.的起源。

IF 3.8 2区农林科学 Q1 PLANT SCIENCES

Journal of Applied Research on Medicinal and Aromatic Plants Pub Date : 2024-10-29 DOI:10.1016/j.jarmap.2024.100599

Mingyu Han , Tao Shen , Yuanzhong Wang

{"title":"红外光谱效应与深度学习相结合，预测 Gentiana rigescens Franch.的起源。","authors":"Mingyu Han , Tao Shen , Yuanzhong Wang","doi":"10.1016/j.jarmap.2024.100599","DOIUrl":null,"url":null,"abstract":"<div><div><em>Gentiana rigescens</em> Franch. (GR) is a high-value medicinal plant and is widely used as food additive and beverage. Due to the influence of the environment, the accumulation of active ingredients of GR from different origins varies and produces different brand values, which is of great significance for the certification of the GR origin. This study employs the infrared-spectrum-effect to reflect the differences among different origins. The partial least squares-discriminant analysis (PLS-DA) and data-driven version of SIMCA (DD-SIMCA) models were used to determine origin. The Residual Neural Network (ResNet) model was constructed using two-dimensional correlation spectra (2DCOS) and three-dimensional correlation spectra (3DCOS) to discriminate between different origins. Maximum Entropy (MaxEnt) was used to screen out environmental variables that have a significant effect on the accumulation of active ingredients. The conclusion is that the ResNet model based on synchronous 2DCOS and 3DCOS has better performance, the accuracy of training and test sets were 100 %.</div></div>","PeriodicalId":15136,"journal":{"name":"Journal of Applied Research on Medicinal and Aromatic Plants","volume":"43 ","pages":"Article 100599"},"PeriodicalIF":3.8000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Infrared-spectrum-effect combined with deep learning to predict the origin of Gentiana rigescens Franch.\",\"authors\":\"Mingyu Han , Tao Shen , Yuanzhong Wang\",\"doi\":\"10.1016/j.jarmap.2024.100599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div><em>Gentiana rigescens</em> Franch. (GR) is a high-value medicinal plant and is widely used as food additive and beverage. Due to the influence of the environment, the accumulation of active ingredients of GR from different origins varies and produces different brand values, which is of great significance for the certification of the GR origin. This study employs the infrared-spectrum-effect to reflect the differences among different origins. The partial least squares-discriminant analysis (PLS-DA) and data-driven version of SIMCA (DD-SIMCA) models were used to determine origin. The Residual Neural Network (ResNet) model was constructed using two-dimensional correlation spectra (2DCOS) and three-dimensional correlation spectra (3DCOS) to discriminate between different origins. Maximum Entropy (MaxEnt) was used to screen out environmental variables that have a significant effect on the accumulation of active ingredients. The conclusion is that the ResNet model based on synchronous 2DCOS and 3DCOS has better performance, the accuracy of training and test sets were 100 %.</div></div>\",\"PeriodicalId\":15136,\"journal\":{\"name\":\"Journal of Applied Research on Medicinal and Aromatic Plants\",\"volume\":\"43 \",\"pages\":\"Article 100599\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Research on Medicinal and Aromatic Plants\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221478612400072X\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Research on Medicinal and Aromatic Plants","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221478612400072X","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

龙胆草（Gentiana rigescens Franch.(Gentiana rigescens Franchens）是一种高价值的药用植物，被广泛用作食品添加剂和饮料。由于受环境的影响，不同产地的龙胆有效成分积累不同，产生的品牌价值也不同，这对龙胆产地的认证具有重要意义。本研究采用红外光谱效应来反映不同产地之间的差异。采用偏最小二乘判别分析（PLS-DA）和数据驱动版 SIMCA（DD-SIMCA）模型来确定产地。利用二维相关谱（2DCOS）和三维相关谱（3DCOS）构建了残差神经网络（ResNet）模型，以区分不同的产地。使用最大熵（MaxEnt）筛选出对活性成分积累有显著影响的环境变量。结论是基于同步 2DCOS 和 3DCOS 的 ResNet 模型具有更好的性能，训练集和测试集的准确率均为 100%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Infrared-spectrum-effect combined with deep learning to predict the origin of Gentiana rigescens Franch.

Gentiana rigescens Franch. (GR) is a high-value medicinal plant and is widely used as food additive and beverage. Due to the influence of the environment, the accumulation of active ingredients of GR from different origins varies and produces different brand values, which is of great significance for the certification of the GR origin. This study employs the infrared-spectrum-effect to reflect the differences among different origins. The partial least squares-discriminant analysis (PLS-DA) and data-driven version of SIMCA (DD-SIMCA) models were used to determine origin. The Residual Neural Network (ResNet) model was constructed using two-dimensional correlation spectra (2DCOS) and three-dimensional correlation spectra (3DCOS) to discriminate between different origins. Maximum Entropy (MaxEnt) was used to screen out environmental variables that have a significant effect on the accumulation of active ingredients. The conclusion is that the ResNet model based on synchronous 2DCOS and 3DCOS has better performance, the accuracy of training and test sets were 100 %.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Applied Research on Medicinal and Aromatic Plants Pharmacology, Toxicology and Pharmaceutics-Drug Discovery

CiteScore

6.40

自引率

7.70%

发文量

审稿时长

41 days

期刊介绍： JARMAP is a peer reviewed and multidisciplinary communication platform, covering all aspects of the raw material supply chain of medicinal and aromatic plants. JARMAP aims to improve production of tailor made commodities by addressing the various requirements of manufacturers of herbal medicines, herbal teas, seasoning herbs, food and feed supplements and cosmetics. JARMAP covers research on genetic resources, breeding, wild-collection, domestication, propagation, cultivation, phytopathology and plant protection, mechanization, conservation, processing, quality assurance, analytics and economics. JARMAP publishes reviews, original research articles and short communications related to research.