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
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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., 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.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Accelerated Crop Improvement Research Program : West & Central Africa |
| CRP: | CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC) |
| Uncontrolled Keywords: | cereals, protein, near-infrared spectroscopy (NIRS), prediction methods, winISI, Hone Create, Convolution Neural Network (CNN) |
| Subjects: | Others > Cereals |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 07 Feb 2024 05:34 |
| Last Modified: | 07 Feb 2024 05:34 |
| URI: | http://oar.icrisat.org/id/eprint/12442 |
| Official URL: | https://www.mdpi.com/1424-8220/22/10/3710 |
| Projects: | UNSPECIFIED |
| Funders: | This research was funded by the CGIAR Research Program grant for Grain Legumes and Dryland Cereals–ICRISAT (GLDC-ICRISAT; 2018–2022); CGIAR’s Crop to End Hunger initiative– ICRISAT (a multi-funder initiative led by USAID and including the Gates Foundation |
| Acknowledgement: | The authors would like to thank B.D. Ranjitha Kumari and T. Senthil Kumar from Bharathidasan University, for supporting the PhD student Keerthi Chadalavada. The authors thank the team at ICRISAT and CIMMYT for sharing the genetic material with us, Intertek AgriTech and International Livestock Research Institute (ILRI) for the laboratory analyses, and the Hone team for their expert input. Special acknowledgment goes to Felicity Fraser, Sivasakthi Kaliamoorthy, Rekha Baddam, and Srikanth Mallayee for their support with experimentation; Premalatha Teegalnagaram and Mallesh Rahini for their support in laboratory activities; andWilliam Nelson for his inputs on improving the manuscript. |
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