Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/53586
Title: Performance Evaluation of Chemometric Prediction Models—Key Components of Wheat Grain
Authors: Joe, A Anne Frank
Gopal, A
Pandian, R
Keywords: Wheat;Support vector machine;Quality parameters;Near Infrared Spectrometer
Issue Date: Feb-2020
Publisher: NISCAIR-CSIR, India
Abstract: The present study was aimed to evaluate the accuracy of using near-infrared spectroscopy (NIRS) for predicting protein, moisture, starch and ash content values of wheat. The physiochemical properties of wheat were predicted using twelve prediction models of preprocessing coupled with regression tools. The performance measure of SVM aided with extended multiplicative scatter correction gave confident prediction results of protein, moisture, ash and starch content with R2 values of 0.989, 0.987, 0.976, 0.998 and RMSECV values of 0.263, 0.285793, 0.369 and 0.03 respectively. These results indicate the practical applicability of NIRS in wheat grain quality profiling.
Page(s): 148–152
URI: http://nopr.niscair.res.in/handle/123456789/53586
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(02) [February 2020]

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