Use of Near Infrared Spectroscopy (NIRS) as a tool to discriminate species of the genus Dimorphandra Schott (Leguminosae: Caesalpinioideae)

Guilherme Sousa da Silva, Michael John Gilbert Hopkins
{"title":"Use of Near Infrared Spectroscopy (NIRS) as a tool to discriminate species of the genus Dimorphandra Schott (Leguminosae: Caesalpinioideae)","authors":"Guilherme Sousa da Silva, Michael John Gilbert Hopkins","doi":"10.14808/sci.plena.2024.031201","DOIUrl":null,"url":null,"abstract":"This work aimed to use near-infrared spectroscopy (NIRS) as a tool to discriminate species of the genus Dimorphandra Schott (Leguminosae, Caesalpinioideae). Spectra were collected from 315 individuals (six readings per individual) distributed in 20 species of Dimorphandra using a Thermo Nicollet spectrophotometer, FT-NIR Antaris II Method Development System (MDS) in the INPA (National Institute of Amazonian Research) Herbarium. Absorbance values comprise the wavenumbers from 4,000 to 10,000 cm-1, corresponding to the near infrared region, recorded for 16 scans at a resolution of 8 cm-1. Principal Component Analysis (PCA) was used to visualize the spectral distribution. Discriminant functions were generated in order to evaluate the potential of the data to correctly distinguish the species and the 70-30 cross-validation technique was used to validate the generated models, with selections randomized 1, 10, 50 and 100 times. Excellent results were obtained in the PCA, with prediction values of 95-92%, using the 70-30 validation test in the linear discriminant analyses (LDA), thus indicating high predictive power in the discrimination of species of the genus Dimorphandra. Thus, it is inferred that NIRS contributes to the discrimination of species of the genus and elucidation of taxonomic problems.","PeriodicalId":22090,"journal":{"name":"Scientia Plena","volume":"15 15","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientia Plena","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14808/sci.plena.2024.031201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This work aimed to use near-infrared spectroscopy (NIRS) as a tool to discriminate species of the genus Dimorphandra Schott (Leguminosae, Caesalpinioideae). Spectra were collected from 315 individuals (six readings per individual) distributed in 20 species of Dimorphandra using a Thermo Nicollet spectrophotometer, FT-NIR Antaris II Method Development System (MDS) in the INPA (National Institute of Amazonian Research) Herbarium. Absorbance values comprise the wavenumbers from 4,000 to 10,000 cm-1, corresponding to the near infrared region, recorded for 16 scans at a resolution of 8 cm-1. Principal Component Analysis (PCA) was used to visualize the spectral distribution. Discriminant functions were generated in order to evaluate the potential of the data to correctly distinguish the species and the 70-30 cross-validation technique was used to validate the generated models, with selections randomized 1, 10, 50 and 100 times. Excellent results were obtained in the PCA, with prediction values of 95-92%, using the 70-30 validation test in the linear discriminant analyses (LDA), thus indicating high predictive power in the discrimination of species of the genus Dimorphandra. Thus, it is inferred that NIRS contributes to the discrimination of species of the genus and elucidation of taxonomic problems.
将近红外光谱仪(NIRS)用作区分Dimorphandra Schott属(豆科:Caesalpinioideae)物种的工具
这项工作旨在使用近红外光谱(NIRS)作为一种工具来区分Dimorphandra Schott属(豆科,Caesalpinioideae)的物种。在 INPA(亚马逊国家研究所)标本馆使用 Thermo Nicollet 分光光度计、FT-NIR Antaris II 方法开发系统(MDS)采集了分布在 20 种 Dimorphandra 中的 315 个个体(每个个体 6 个读数)的光谱。吸收值包括 4,000 至 10,000 cm-1 的波长,相当于近红外区域,以 8 cm-1 的分辨率记录了 16 次扫描。主成分分析(PCA)用于直观显示光谱分布。为了评估数据正确区分物种的潜力,生成了判别函数,并使用 70-30 交叉验证技术对生成的模型进行验证,随机选择 1、10、50 和 100 次。在线性判别分析(LDA)中使用 70-30 验证测试,在 PCA 中获得了极好的结果,预测值为 95-92%,从而表明在区分二斑蝶属物种方面具有很高的预测能力。因此,可以推断近红外光谱有助于区分二斑蝶属的物种和阐明分类问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信