{"title":"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","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":"{\"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}","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}
Hyperspectral Imaging for Detection and Classification of Plant Primary and Secondary Metabolites: A Review.
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.
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.