Evidence map›Paper›PMID 41756654›Full record

ArticleFrontiers in plant science2026

Estimating sorghum leaf dhurrin content using a handheld near infrared instrument.

Kamaranga H S Peiris, Scott R Bean, Chad M Hayes, Yves Y Emendack, Jacobo Sanchez

Abstract read
In one paragraph

Article in Frontiers in plant science, 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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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kamaranga H S PeirisCenter for Grain and Animal Health, USDA-ARS, Manhattan, KS, United States.
Scott R BeanCenter for Grain and Animal Health, USDA-ARS, Manhattan, KS, United States.
Chad M HayesCropping Systems Research Laboratory, USDA-ARS, Lubbock, TX, United States.
Yves Y EmendackCropping Systems Research Laboratory, USDA-ARS, Lubbock, TX, United States.
Jacobo SanchezCropping Systems Research Laboratory, USDA-ARS, Lubbock, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dhurrin is a toxic cyanogenic glycoside present in sorghum as a secondary metabolite. As such, dhurrin content of plants is important when sorghum is used as a forage crop. Moreover, leaf dhurrin content may indicate pre- and post-flowering drought resistance of sorghum germplasm. Standard method of analysis by HPLC is expensive and time consuming. Therefore, a feasibility study was conducted to measure dhurrin content in sorghum leaves by NIR spectroscopy using a handheld NIR spectrometer. Results: Partial least squares (PLS) regression, back propagation neural network (BPNN) and deep learning artificial neural network (ANNDL) models were developed to estimate dhurrin content in fresh and dry sorghum leaves. However, it was impossible to develop useful models with fresh leaf scans. NIR spectroscopy models could estimate the dhurrin content of dry sorghum leaves with R Conclusions: Therefore, NIR spectroscopy may be used as a rapid and cost-effective technique for pre-screening sorghum germplasm for leaf dhurrin content in plant breeding programs.

Indexed as

deep learning modelsdhurrin content estimationnear infrared spectroscopyneural network modelssorghum

Identifiers

PMID41756654
PMCPMC12932412

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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.