Nutritional variability and milling losses of rice landraces from Arunachal Pradesh, Northeast India

Longvah, T.; Prasad, V.S.S.

doi:10.1016/j.foodchem.2020.126385

Cited by 27 publications

(16 citation statements)

References 27 publications

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“…The fourth component was attributed mainly to lipid, ash, amylose, and Mn. These findings were in agreement with previous studies, which reported that ash and insoluble dietary fibre were the major factors contributing to variation of rice cultivars and other cereals [17] .…”

Section: Multivariate Analysissupporting

confidence: 94%

Variability in Nutritional Composition, Kernel Morphology and Cooking Quality of Selected Rice in Xingan Meng from Northeast China

et al. 2020

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Paddy rice cultivation expanded in Northeast China, and Xingan Meng of Inner Mongolia is an emerging area of rice production area. The goals of this study: i) to investigate varietal differences in levels of nutritional quality, kernel morphology and cooking quality and ii) to identify clusters of rice samples from Xingan Meng, northeast part of China. Research was conducted in Xingan Meng, China during the 2019 rice-growing season. The nutritional quality (energy, protein, carbohydrate, lipid, amylose, ash, Ca, Na, Fe, Zn, Mn), cooking quality (alkali spreading value, gel consistency) and kernel morphology (length, width, length width ratio, chalky rice percentage, chalky rice degree) were analysed. Significant difference were found across all traits. The largest variation was found for Mn, followed by Ca, Fe and Zn content. Four principal components were found that accounted for 95.14% of overall variability. Cluster analysis sorted the rice sample into four clusters based on nutritional quality, kernel morphology, and cooking quality. The findings of this study can support to demonstrate the quality of rice from Xingan Meng, northeast part of China.

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Section: Multivariate Analysissupporting

confidence: 94%

Variability in Nutritional Composition, Kernel Morphology and Cooking Quality of Selected Rice in Xingan Meng from Northeast China

et al. 2020

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“…This was observed in the case of dietary fiber (data not shown); and therefore, to reduce the impact of non-responsive regions, the selection of wavelength segments at constant intervals (gap; up to 16) was used to select responsive segments in spectra that provided better results. Smoothening (S1, S2) was employed to reduce the signal-to-noise ratio in the spectral region due to high-frequency perturbations ( 30 ).…”

Section: Resultsmentioning

confidence: 99%

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

et al. 2022

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Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

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“…The water and ash content were analyzed using the gravimetric method (Longvah & Prasad 2020). For water content, it was measured by comparing the weight of rice before and after being heated.…”

Section: Water and Ash Contentmentioning

confidence: 99%

The Physicochemical Properties of Several Indonesian Rice Varieties

Susiyanti

Rusmana²,

Maryani³

et al. 2019

BIOTROPIA

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Nutritional variability and milling losses of rice landraces from Arunachal Pradesh, Northeast India

Cited by 27 publications

References 27 publications

Variability in Nutritional Composition, Kernel Morphology and Cooking Quality of Selected Rice in Xingan Meng from Northeast China

Variability in Nutritional Composition, Kernel Morphology and Cooking Quality of Selected Rice in Xingan Meng from Northeast China

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

The Physicochemical Properties of Several Indonesian Rice Varieties

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