Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat
Amaranth and buckwheat are two pseudo-cereals preferred for their high nutritional value, are gluten free and carry religious importance as fasting food. Germplasm resources are the reservoir of diversity for different traits, including nutritional characteristics. These resources must be evaluated...
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MDPI AG
2023-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/2/469 |
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| author | Shruti Alka Shukla Saman Saim Rahman Poonam Suneja Rashmi Yadav Zakir Hussain Rakesh Singh Shiv Kumar Yadav Jai Chand Rana Sangita Yadav Rakesh Bhardwaj |
| author_facet | Shruti Alka Shukla Saman Saim Rahman Poonam Suneja Rashmi Yadav Zakir Hussain Rakesh Singh Shiv Kumar Yadav Jai Chand Rana Sangita Yadav Rakesh Bhardwaj |
| author_sort | Shruti |
| collection | DOAJ |
| description | Amaranth and buckwheat are two pseudo-cereals preferred for their high nutritional value, are gluten free and carry religious importance as fasting food. Germplasm resources are the reservoir of diversity for different traits, including nutritional characteristics. These resources must be evaluated to utilize their potential in crop improvement programs. However, conventional methods are labor-, cost- and time-intensive and prone to handling errors when applied to large samples. NIRS-based machine learning to predict different nutritional traits is applied in different food crops for multiple traits. NIRS prediction models are developed in this study using the mPLS regression technique for oil, protein, fatty acids and essential amino acid estimation in amaranth and buckwheat. Good RSQ external (power of determination) values were obtained for the above traits ranging from 0.72 to 0.929. Ratio performance deviation (RPD) value for most of the traits ranged between 2 and 3, except for valine (1.88) and methionine (3.55), indicating good prediction capabilities in the developed model. These prediction models were utilized in screening the germplasm of amaranth and buckwheat; the results obtained were in good agreement and confirmed the applicability of developed models. It will enable the identification of a trait-specific germplasm as a potential gene source and aid in crop improvement programs. |
| first_indexed | 2024-03-11T09:18:20Z |
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| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T09:18:20Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-d6cbf3559b894b1bb66a9ad4478ffa072023-11-16T18:31:38ZengMDPI AGAgriculture2077-04722023-02-0113246910.3390/agriculture13020469Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and BuckwheatShruti0Alka Shukla1Saman Saim Rahman2Poonam Suneja3Rashmi Yadav4Zakir Hussain5Rakesh Singh6Shiv Kumar Yadav7Jai Chand Rana8Sangita Yadav9Rakesh Bhardwaj10ICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-IARI, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaICAR-NBPGR, Pusa, New Delhi 110012, IndiaAmaranth and buckwheat are two pseudo-cereals preferred for their high nutritional value, are gluten free and carry religious importance as fasting food. Germplasm resources are the reservoir of diversity for different traits, including nutritional characteristics. These resources must be evaluated to utilize their potential in crop improvement programs. However, conventional methods are labor-, cost- and time-intensive and prone to handling errors when applied to large samples. NIRS-based machine learning to predict different nutritional traits is applied in different food crops for multiple traits. NIRS prediction models are developed in this study using the mPLS regression technique for oil, protein, fatty acids and essential amino acid estimation in amaranth and buckwheat. Good RSQ external (power of determination) values were obtained for the above traits ranging from 0.72 to 0.929. Ratio performance deviation (RPD) value for most of the traits ranged between 2 and 3, except for valine (1.88) and methionine (3.55), indicating good prediction capabilities in the developed model. These prediction models were utilized in screening the germplasm of amaranth and buckwheat; the results obtained were in good agreement and confirmed the applicability of developed models. It will enable the identification of a trait-specific germplasm as a potential gene source and aid in crop improvement programs.https://www.mdpi.com/2077-0472/13/2/469machine learningRSQRPDmPLSWINISIvalidation |
| spellingShingle | Shruti Alka Shukla Saman Saim Rahman Poonam Suneja Rashmi Yadav Zakir Hussain Rakesh Singh Shiv Kumar Yadav Jai Chand Rana Sangita Yadav Rakesh Bhardwaj Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat Agriculture machine learning RSQ RPD mPLS WINISI validation |
| title | Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat |
| title_full | Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat |
| title_fullStr | Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat |
| title_full_unstemmed | Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat |
| title_short | Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat |
| title_sort | developing an nirs prediction model for oil protein amino acids and fatty acids in amaranth and buckwheat |
| topic | machine learning RSQ RPD mPLS WINISI validation |
| url | https://www.mdpi.com/2077-0472/13/2/469 |
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