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Developing an NIRS prediction model for oil, protein, amino acids and fatty acids in amaranth and buckwheat

Shruti, Shukla, Alka, Rahman, Saman Saim, Suneja, Poonam, Yadav, Rashmi, Hussain, Zakir, Singh, Rakesh, Yadav, Shiv Kumar +3 more·2023·Agriculture
DOI 10.3390/agriculture13020469
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Abstract

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.

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  • Comparable descriptors are needed before machine learning can identify robust predictors.

    “Comparable descriptors are needed before machine learning can identify robust predictors”

    From Structure-function design of amaranth, buckwheat and quinoa proteins as hydrocolloids for emulsion and gel structuring (2027) · cites this paper as Shruti et al. (2023)

    The Introduction (page unavailable) says NIR spectra are preprocessed to reduce noise and enhance feature extraction before prediction models are developed. That supports a narrower point about preparing spectral data, but the paper does not say that comparable descriptors are required before machine learning can identify robust predictors. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Shruti, Shukla, A., Rahman, S. S., Suneja, P., Yadav, R., Hussain, Z., Singh, R., Yadav, S. K., Rana, J. C., Yadav, S., & Bhardwaj, R.
Affiliations
  • ICAR-NBPGR, Pusa, New Delhi 110012, India
  • ICAR-IARI, Pusa, New Delhi 110012, India
ORCID
Venue
Agriculture
Publisher
MDPI AG
Published
2023-02-16
Volume
13
Issue
2
Pages
469
ISSN
2077-0472
DOI
10.3390/agriculture13020469
Cited by
19
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Indian Council of Agricultural Research; Department of Science & Technology, Government of India, Global Environment Facility (GEF) of the United Nations Environment Program (UNEP) (LoA No. L19INDIA173, 385 number “BT/PR23811/AGIII/103/1029/2018”)
  • Department of Biotechnology Government of India (LoA No. L19INDIA173, 385 number “BT/PR23811/AGIII/103/1029/2018”)

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