Hyperspectral Imaging for Detection and Classification of Plant Primary and Secondary Metabolites: A Review.

IF 2.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Muskan Raghav, Akhilesh Dubey, Jyotsna Singh
{"title":"Hyperspectral Imaging for Detection and Classification of Plant Primary and Secondary Metabolites: A Review.","authors":"Muskan Raghav, Akhilesh Dubey, Jyotsna Singh","doi":"10.1002/pca.70029","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hyperspectral imaging (HSI) is a nondestructive technique that simultaneously captures spectral and spatial information across multiple wavelengths. It has gained importance in plant science for detecting primary metabolites, vital for growth, and secondary metabolites, essential for plant defense and human health. Conventional methods such as chromatography and mass spectrometry, though accurate, are destructive, time-consuming, and require laborious sample preparation.</p><p><strong>Objectives: </strong>This review examines the potential of HSI as a rapid and noninvasive tool for metabolite detection and classification, emphasizing its role in precision agriculture, plant phenotyping, and medicinal plant research.</p><p><strong>Methods: </strong>This review summarizes principles of HSI, hardware components, image acquisition strategies, and processing techniques. Special focus is given to the integration of machine learning for extracting and classifying biochemical information from high-dimensional spectral data.</p><p><strong>Results: </strong>Studies show that HSI enables accurate, real-time assessment of plant metabolic profiles. Machine learning approaches enhance predictive performance, while advances in imaging sensors, illumination systems, and computational tools are improving applicability. HSI is increasingly adopted for monitoring plant quality, stress responses, and bioactive compound content.</p><p><strong>Conclusion: </strong>This review highlights HSI as a transformative tool in plant metabolomics, providing scalable, rapid, and sustainable alternatives to traditional methods, with strong potential to advance agricultural productivity and medicinal plant applications.</p>","PeriodicalId":20095,"journal":{"name":"Phytochemical Analysis","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Phytochemical Analysis","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/pca.70029","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Hyperspectral imaging (HSI) is a nondestructive technique that simultaneously captures spectral and spatial information across multiple wavelengths. It has gained importance in plant science for detecting primary metabolites, vital for growth, and secondary metabolites, essential for plant defense and human health. Conventional methods such as chromatography and mass spectrometry, though accurate, are destructive, time-consuming, and require laborious sample preparation.

Objectives: This review examines the potential of HSI as a rapid and noninvasive tool for metabolite detection and classification, emphasizing its role in precision agriculture, plant phenotyping, and medicinal plant research.

Methods: This review summarizes principles of HSI, hardware components, image acquisition strategies, and processing techniques. Special focus is given to the integration of machine learning for extracting and classifying biochemical information from high-dimensional spectral data.

Results: Studies show that HSI enables accurate, real-time assessment of plant metabolic profiles. Machine learning approaches enhance predictive performance, while advances in imaging sensors, illumination systems, and computational tools are improving applicability. HSI is increasingly adopted for monitoring plant quality, stress responses, and bioactive compound content.

Conclusion: This review highlights HSI as a transformative tool in plant metabolomics, providing scalable, rapid, and sustainable alternatives to traditional methods, with strong potential to advance agricultural productivity and medicinal plant applications.

植物初级和次级代谢物的高光谱成像检测和分类研究进展
背景:高光谱成像(HSI)是一种非破坏性技术,可以同时捕获多个波长的光谱和空间信息。检测对植物生长至关重要的初级代谢物和对植物防御和人类健康至关重要的次级代谢物,在植物科学中具有重要意义。传统的方法,如色谱法和质谱法,虽然准确,但具有破坏性,耗时,并且需要费力的样品制备。目的:本综述探讨了HSI作为代谢物检测和分类的快速、无创伤工具的潜力,强调了其在精准农业、植物表型和药用植物研究中的作用。方法:本文综述了HSI的原理、硬件组成、图像采集策略和处理技术。特别关注了从高维光谱数据中提取和分类生化信息的机器学习集成。结果:研究表明,HSI能够准确、实时地评估植物代谢谱。机器学习方法增强了预测性能,而成像传感器、照明系统和计算工具的进步正在提高适用性。HSI越来越多地用于监测植物品质、胁迫反应和生物活性化合物含量。结论:本综述强调了HSI作为植物代谢组学的变革性工具,为传统方法提供了可扩展、快速和可持续的替代方法,具有提高农业生产力和药用植物应用的强大潜力。
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
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.
×
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:604180095
Book学术官方微信