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NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

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Relation http://oar.icrisat.org/12442/
https://www.mdpi.com/1424-8220/22/10/3710
https://doi.org/10.3390/s22103710
 
Title NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals
 
Creator Chadalavada, K
Anbazhagan, K
Ndour, A
Choudhary, S
Palmer, W
Flynn, J R
Mallayee, S
Pothu, S
Prasad, K V S S
Varijakshapanikar, P
Jones, C S
Kholová, J
 
Subject Cereals
 
Description 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., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.
 
Publisher MDPI
 
Date 2022-05-13
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_by
 
Identifier http://oar.icrisat.org/12442/1/Sensors_22_1-18_2022.pdf
Chadalavada, K and Anbazhagan, K and Ndour, A and Choudhary, S and Palmer, W and Flynn, J R and Mallayee, S and Pothu, S and Prasad, K V S S and Varijakshapanikar, P and Jones, C S and Kholová, J (2022) NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals. Sensors, 22. pp. 1-18. ISSN 1424-8220