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作 者:Miri Park Annette Somborn Dennis Schlehuber Volkmar Keuter Görge Deerberg
出 处:《Horticulture Research》2023年第5期282-296,共15页园艺研究(英文)
基 金:This study was partially supported by the Federal Ministry of Education and Research(BMBF)through the program Agricul-tural Agricul-tural Systems of the Future in the framework of the“National Research Strategy BioEconomy 2030”under grant No.031B0728A“SUSKULT-Development of a Sustainable Cultivation System of Resilient Metropolitan Regions”;through the program“Model Region Bioeconomy in the Rhenish mining area”under grant No.031B1137BX“Model region,Phase 1,BioRevierPLUS:InnoLA,TP2-circular PhytoREVIER”.
摘 要:As a crop quality sensor,Raman spectroscopy has been consistently proposed as one of the most promising and non-destructive methods for qualitative and quantitative analysis of plant substances,because it can measure molecular structures in a short time without requiring pretreatment along with simple usage.The sensitivity of the Raman spectrum to target chemicals depends largely on the wavelength,intensity of the laser power,and exposure time.Especially for plant samples,it is very likely that the peak of the target material is covered by strong fluorescence effects.Therefore,methods using lasers with low energy causing less fluorescence,such as 785 nm or near-infrared,are vigorously discussed.Furthermore,advanced techniques for obtaining more sensitive and clear spectra,like surface-enhanced Raman spectroscopy,time-gated Raman spectroscopy or combination with thin-layer chromatography,are being investigated.Numerous interpretations of plant quality can be represented not only by the measurement conditions but also by the spectral analysis methods.Up to date,there have been attempted to optimize and generalize analysis methods.This review summarizes the state of the art of micro-Raman spectroscopy in crop quality assessment focusing on secondary metabolites,from in vitro to in vivo and even in situ,and suggests future research to achieve universal application.
关 键 词:SPECTROSCOPY qualitative LIKELY
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