ArticleSensors (Basel, Switzerland)2022
NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Convolutional Neural Network-Based Models for Near-Infrared Prediction of Nutritional Quality in Multi-Product Animal Feeds.Animals : an open access journal from MDPI · 2026Article
- Multi-locus genome-wide association study on the rheological traits in wheat flour dough.BMC genomics · 2026Article
- Nutritional profiling of horse gram through NIRS-based multi-trait prediction modelling.Scientific reports · 2025Article
- Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation.Current research in food science · 2025Article
- Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS.Sensors (Basel, Switzerland) · 2024Article
- Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato.Foods (Basel, Switzerland) · 2024Article
- Prediction of protein content in paddy rice (Frontiers in plant science · 2024Article
- Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation.Sensors (Basel, Switzerland) · 2023Article
- Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry.Frontiers in chemistry · 2023Article
- Evaluation of millets for physio-chemical and root morphological traits suitable for resilient farming and nutritional security in Eastern Himalayas.Frontiers in nutrition · 2023Article
- Quality Evaluation of Fair-Trade Cocoa Beans from Different Origins Using Portable Near-Infrared Spectroscopy (NIRS).Foods (Basel, Switzerland) · 2022Article
- Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.Frontiers in nutrition · 2022Article
- Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm.Frontiers in nutrition · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R
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Registered trials
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.