Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals

Keerthi Chadalavada
,
Krithika Anbazhagan
ORCID logo ,
Adama Ndour
,
Sunita Choudhary
,
William M. Palmer
,
Jamie R. Flynn
,
Srikanth Mallayee
,
P. Sharada
,
K.V.S.V. Prasad
ORCID logo ,
V. Padmakumar
,
Chris Jones
ORCID logo ,
Jana Kholová
*
Version 1 : Received: 24 February 2022 / Approved: 25 February 2022 / Online: 25 February 2022 (11:21:57 CET)

A peer-reviewed article of this Preprint also exists.

Chadalavada, K.; Anbazhagan, K.; Ndour, A.; Choudhary, S.; Palmer, W.; Flynn, J.R.; Mallayee, S.; Pothu, S.; Prasad, K.V.S.V.; Varijakshapanikar, P.; Jones, C.S.; Kholová, J. NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals. Sensors 2022, 22, 3710. Chadalavada, K.; Anbazhagan, K.; Ndour, A.; Choudhary, S.; Palmer, W.; Flynn, J.R.; Mallayee, S.; Pothu, S.; Prasad, K.V.S.V.; Varijakshapanikar, P.; Jones, C.S.; Kholová, J. NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals. Sensors 2022, 22, 3710.

Abstract

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.

Keywords

Cereals; Grain protein; Near Infrared Spectroscopy (NIRS)-based sensors; Prediction algorithms; FOSS; Hone Lab

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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