scholarly journals Standardization of the fourier transform near-infrared reflectance spectroscopy for estimation of some oil quality parameters in mustard (Brassica spp.)

2013 ◽  
Vol 59 (No. 10) ◽  
pp. 478-483 ◽  
Author(s):  
J. Singh ◽  
Sharma PC ◽  
Sharma SK ◽  
A. Kumar
Keyword(s):  
Erucic Acid ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Quality Parameters ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Crude Fibre ◽  
Near Infrared Reflectance ◽  
The Fourier Transform ◽  
Crude Fibre Content

The possibility of the application of the fourier transform near-infrared reflectance spectroscopy (NIRS) to the analysis of the selected quality parameters in the mustard oil was followed to determine oil, protein, erucic acid and crude fibre content at the Central Soil Salinity Research Institute, Karnal, India. The samples were analysed by reference methods and by the fourier transformed near infrared (FT-NIR) spectroscope at integrating sphere within reflectance mode in the wavelength range 10 000&ndash;4000/cm (1000&ndash;2500 nm) with 32 scans. To develop the calibration model for the examined components, the partial least square was used and this model was validated by full cross validation. The coefficients of determination (r<sup>2</sup>) for intact seeds were 0.907, 0.922, 0.902 and 0.903 for oil, protein, erucic acid and crude fibre content, respectively thus showing that NIRS calibrations are applicable for the estimation of seed quality parameters which is highly desirable in Brassica breeding programs for a quick and non-destructive analysis of oil, protein, erucic acid and crude fibre contents in intact seed.

EFFECT OF GROWING YEAR AND APPLICATION OF A MULTI-YEAR CALIBRATION FOR PREDICTING QUALITY PARAMETERS BY NEAR INFRARED REFLECTANCE SPECTROSCOPY IN WHOLE-PLANT CORN FORAGE

1990 ◽  
Vol 70 (3) ◽  
pp. 747-755 ◽  
Author(s):  
E. V. VALDES ◽  
G. E. JONES ◽  
G. J. HOEKSTRA
Keyword(s):  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Quality Parameters ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Test Set ◽  
Infrared Reflectance Spectroscopy ◽  
Whole Plant

Near infrared reflectance spectroscopy analysis (NIRA) to predict quality parameters in whole-plant corn forage was investigated. Quality parameters studied included acid detergent fiber (ADF), in vitro dry matter digestibility (IVDMD) and crude protein (CP). Samples of whole-plant corn forage were collected during three growing years (1984, 1985, 1986) across six geographical locations in Ontario, Canada and were harvested at an average 35% dry matter content. Samples were randomly divided into two sets: a calibration (CAL) set to develop NIRA equations and a testing (TEST) set to validate these equations. NIRA calibrations for ADF, IVDMD and CP percent were performed for each growing season across locations. A multi-year calibration was also developed with samples drawn from the three growing years. The accuracy of the NIRA predictions was assessed by the standard error of the estimate (SEE), bias or the mean difference between laboratory and NIRA data, the coefficient of determination (r2) and the slope (b) of the regressions between laboratory and NIRA data. The single year calibrations showed good predictions for all quality parameters in samples drawn within the year. The SEE values in the TEST sets for single year calibrations varied from 1.6 to 1.9% for ADF, 1.5 to 2.3% for IVDMD and 0.3 to 0.5% for CP, respectively. However, recalibration was necessary every year because calibrations based on a single year failed to account for variances introduced by samples drawn from other years. The multi-year calibration predicted ADF, IVDMD and CP percent accurately regardless of year. The SEE and bias for ADF, IVDMD and CP percent for the TEST set were 1.6 and 0.1, 2.2 and 0.3, and 0.5 and 0.1, respectively. The success of the multi-year calibration encourage the possibility to develop a standard calibration for whole-plant corn.Key words: Near infrared reflectance analysis, calibrations, whole-plant corn forage, year, multi-year quality parameters

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

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