首页|Development of NIR spectroscopy based prediction models for nutritional profiling of pearl millet (Pennisetum glaucum (L.)) R.Br: A chemometrics approach

Development of NIR spectroscopy based prediction models for nutritional profiling of pearl millet (Pennisetum glaucum (L.)) R.Br: A chemometrics approach

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  • NSTL
  • Elsevier
Pearl millet can be viably used for food diversification due to its balanced nutritional composition. Nutritional parameters are conventionally assessed using labour and time-intensive strenuous conventional methods for germplasm screening. Near-infrared reflectance spectroscopy (NIRS) uses near-infrared sections of the electromagnetic spectrum for precise and speedy determination of biochemical parameters for large germplasm. MPLS (Modified Partial Least Squares) regression based NIRS prediction models were developed to assess starch, resistant starch, amylose, protein, oil, total dietary fibre, phenolics, total soluble sugars, phytic acid for high throughput screening of pearl millet germplasm. Mathematical treatments executed by permutation and combinations for calibrating the model, where 2nd, 3rd, and 4th derivatives produced the best results. Treatments "4,5,4,1" was finalized for protein, oil, resistant starch, total dietary fibre, "3,4,4,1" for phenolics, "2,8,4,1" for amylose, "2,4,4,1" for phytic acid, "4,7,4,1" for total soluble sugars and "2,8,4,1" for starch. Treatments with the highest 1-Variance ratio, RSQinternal (coefficient of determination) values, lowest SEC(V) (standard error of crossvalidation), SEP(C) (standard error of performance) were identified for subsequent validation. External validation determined the prediction accuracy based on RSQexternal, RPD (residual prediction deviation), SD (standard deviation), p-value >= 0.05 and low SEP(C).

NIRS (Near-infrared spectroscopy)QuantificationRegression based-satistical modellingNutritional compositionMPLS (Modified partial least squares)regression

Tomar, Maharishi、Bhardwaj, Rakesh、Kumar, Manoj、Singh, Sumer Pal、Krishnan, Veda、Kansal, Rekha、Verma, Reetu、Yadav, Vijay Kumar、Dahuja, Anil、Ahlawat, Sudhir Pal、Rana, Jai Chand、Satyavathi, C. Tara、Praveen, Shelly、Sachdev, Archana

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ICAR Indian Grassland & Fodder Res Inst, Div Seed Technol, Jhansi, Uttar Pradesh, India

Natl Bur Plant Genet Resources, Germplasm Evaluat Div, New Delhi 110012, India

ICAR Cent Inst Res Cotton Technol, Chem & Biochem Proc Div, Mumbai 400019, Maharashtra, India

ICAR Indian Agr Res Inst, Div Genet, New Delhi, India

ICAR Indian Agr Res Inst, Div Biochem, New Delhi 110012, India

ICAR Natl Inst Plant Biotechnol, New Delhi 110012, India

ICAR Indian Grassland, Div Crop Improvement, Jhansi, Uttar Pradesh, India

Alliance Biovers Int & CIAT, NASC Complex,Pusa Campus, New Delhi 110012, India

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2021

10.1016/j.lwt.2021.111813

LWT-Food Science & Technology

LWT-Food Science & Technology

ISSN：0023-6438
年,卷(期)：2021.149
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