DEVELOPMENT OF MULTIVARIATE REGRESSION MODEL FOR QUANTIFICATION OF PROXIMATE CONTENT IN VIGNA RADIATA USING FOURIER TRANSFORM –NIR SPECTROSCOPY
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Title |
DEVELOPMENT OF MULTIVARIATE REGRESSION MODEL FOR QUANTIFICATION OF PROXIMATE CONTENT IN VIGNA RADIATA USING FOURIER TRANSFORM –NIR SPECTROSCOPY
Not Available |
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Creator |
R. Pandiselvam
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Subject |
FT-NIR
Green gram Composition |
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Description |
Not Available
The Fourier Transform Near Infrared (FT-NIR) absorbance spectra (12800-3600 cm-1) of 222 green gram samples was used to build calibration models for the determination of the content of protein, fat and carbohydrate. The samples that comprised the dataset had an average composition of 22.18% of protein, 1.30% fat, and 50.72% carbohydrate. Multivariate regression was used to develop the quantitative models for protein, fat and carbohydrate compounds. The root mean square error of cross validation (RMSECV) was 0.191 (R2 = 91.52) for protein, 0.0271 (R2 = 88.54) for fat and 0.765 (R2 = 93.62) for carbohydrate. A fast, simple and accurate method to quantify the proximate content of green gram was developed by using FT-NIR spectroscopy. The results showed that FT-NIR spectroscopy could support chemical analysis methods. Not Available |
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Date |
2018-11-24T07:50:44Z
2018-11-24T07:50:44Z 2016-04-01 |
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Type |
Research Paper
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Identifier |
Pandiselvam, R., Sunoj, S. and Uma, D., 2016. Development of Multivariate Regression Model for Quantification of Proximate Content in Vigna Radiata Using Fourier Transform–NIR Spectroscopy.
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/13053 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Not Available
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