Evidence map›Paper›PMID 42591817›Full record

ArticleAnalytical science advances2026

Non-Destructive Analysis of Phenolic and Flavonoid Contents in Medicinal Plant Powders Using Hyperspectral Imaging and Variable Selection.

Rahul Joshi, Sushma Kholiya, Himanshu Pandey, Mahipal Singh, Ameeta Tiwari, Jinsu Lim, Ramaraj Sathasivam, Mohammad Akbar Faqeerzada, Sang Un Park, Byoung-Kwan Cho

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Article in Analytical science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

10 authors.

Rahul JoshiDepartment of Smart Agriculture Machinery Engineering, College of Agricultural and Life Science Chungnam National University Daejeon South Korea.
Sushma KholiyaDepartment of Chemistry, M.B.G.P.G College Haldwani Kumaun University Nainital India.
Himanshu PandeyDepartment of Chemistry, M.B.G.P.G College Haldwani Kumaun University Nainital India.
Mahipal SinghDepartment of Chemistry, M.B.G.P.G College Haldwani Kumaun University Nainital India.
Ameeta TiwariDepartment of Chemistry, M.B.G.P.G College Haldwani Kumaun University Nainital India.
Jinsu LimDepartment of Smart Agricultural Systems, College of Agricultural and Life Science Chungnam National University Daejeon South Korea.
Ramaraj SathasivamDepartment of Crop Science Chungnam National University Daejeon South Korea.
Mohammad Akbar FaqeerzadaDepartment of Smart Agriculture Machinery Engineering, College of Agricultural and Life Science Chungnam National University Daejeon South Korea.
Sang Un ParkDepartment of Smart Agricultural Systems, College of Agricultural and Life Science Chungnam National University Daejeon South Korea.
Byoung-Kwan ChoDepartment of Smart Agriculture Machinery Engineering, College of Agricultural and Life Science Chungnam National University Daejeon South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phenolic and flavonoid contents in medicinal plants are essential to their growth and development and provide numerous health benefits, yet their quantification using traditional wet chemistry is labor-intensive and time-consuming. This study utilized the combination of two benchtop hyperspectral imaging (HSI) systems, namely short-wave infrared (SWIR) and visible near-infrared, for the non-destructive quantitative estimation of the phenolic and flavonoid contents present in 12 powder samples of medicinal plants. For quantitative analysis, partial least squares regression (PLSR) prediction models used several spectral preprocessing techniques. To further enhance performance, variable importance in projection (VIP) selected the most informative spectral variables and combined them with PLSR to improve predictions. The optimized VIP-PLSR models further improved the predictive performance, yielding coefficients of determination (

Indexed as

flavonoidmedicinal plantspartial least squares regressionphenolicshort‐wave infrared (SWIR)variable importance in projection (VIP)visible near infrared (VIS/NIR)

Identifiers

PMID42591817
PMCPMC13462912

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.