NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals
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...
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2022
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/119626 |
| _version_ | 1855539178663575552 |
|---|---|
| author | Chadalavada, K. Anbazhagan, K. Ndour, A. Choudhary, S. Palmer, W. Flynn, J.R. Mallayee, S. Sharada, Pothu Prasad, Kodukula V.S.V. Varijakshapanicker, Padmakumar Jones, Christopher S. Kholová, Jana |
| author_browse | Anbazhagan, K. Chadalavada, K. Choudhary, S. Flynn, J.R. Jones, Christopher S. Kholová, Jana Mallayee, S. Ndour, A. Palmer, W. Prasad, Kodukula V.S.V. Sharada, Pothu Varijakshapanicker, Padmakumar |
| author_facet | Chadalavada, K. Anbazhagan, K. Ndour, A. Choudhary, S. Palmer, W. Flynn, J.R. Mallayee, S. Sharada, Pothu Prasad, Kodukula V.S.V. Varijakshapanicker, Padmakumar Jones, Christopher S. Kholová, Jana |
| author_sort | Chadalavada, K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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. |
| format | Journal Article |
| id | CGSpace119626 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1196262025-12-08T10:29:22Z NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals Chadalavada, K. Anbazhagan, K. Ndour, A. Choudhary, S. Palmer, W. Flynn, J.R. Mallayee, S. Sharada, Pothu Prasad, Kodukula V.S.V. Varijakshapanicker, Padmakumar Jones, Christopher S. Kholová, Jana cereals protein near-infrared spectroscopy (nirs) prediction methods winisi hone create convolution neural network (cnn) 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. 2022-05-02 2022-05-23T08:34:28Z 2022-05-23T08:34:28Z Journal Article https://hdl.handle.net/10568/119626 en Open Access MDPI 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. and Kholová, J. 2022. NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals. Sensors 22(10):3710. |
| spellingShingle | cereals protein near-infrared spectroscopy (nirs) prediction methods winisi hone create convolution neural network (cnn) Chadalavada, K. Anbazhagan, K. Ndour, A. Choudhary, S. Palmer, W. Flynn, J.R. Mallayee, S. Sharada, Pothu Prasad, Kodukula V.S.V. Varijakshapanicker, Padmakumar Jones, Christopher S. Kholová, Jana NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| title | NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| title_full | NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| title_fullStr | NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| title_full_unstemmed | NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| title_short | NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| title_sort | nir instruments and prediction methods for rapid access to grain protein content in multiple cereals |
| topic | cereals protein near-infrared spectroscopy (nirs) prediction methods winisi hone create convolution neural network (cnn) |
| url | https://hdl.handle.net/10568/119626 |
| work_keys_str_mv | AT chadalavadak nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT anbazhagank nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT ndoura nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT choudharys nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT palmerw nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT flynnjr nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT mallayees nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT sharadapothu nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT prasadkodukulavsv nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT varijakshapanickerpadmakumar nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT joneschristophers nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals AT kholovajana nirinstrumentsandpredictionmethodsforrapidaccesstograinproteincontentinmultiplecereals |