A calibration set of 140 soybeans from seven different varieties, ranging in moisture from 5 to 22%, wet basis, was used to calibrate a spectrophotometer for moisture prediction of single soybean seeds. Near-infrared absorbance (A) of individual soybean seeds was measured over the spectral region from 800 to 1100 nm by 0.5 nm. The axis of the soybean seed parallel to the incident light beam was measured as an estimate of optical path length. Three mathematical techniques were used to develop calibration equations: linear correlation to a difference in absorbance (AA), stepwise multiple linear regression (MLR), and partial least squares (PLS). A validation set contained 100 soybeans from five different varieties, ranging in moisture from 5 to 20%, wet basis. The standard error of prediction (SEP) for equations using absorbance data only was 0.88% for AA, 0.82% for MLR, and 0.81% for PLS. The SEP for equations using path length and absorbance data was 0.73% for AA, 0.69% for MLR, and 0.65% for PLS.
A calibration set of 140 soybeans from seven different varieties, ranging in moisture from 5 to 22%, wet basis, was used to calibrate a spectrophotometer for predicting moisture of single soybean seeds. Near-infrared absorbance (A) of individual soybean seeds was measured over the spectral region from 800 to 1100 nm by 0.5 nm increments. The axis of the soybean seed parallel to the incident light beam was measured as an estimate of optical pathlength. Three mathematical techniques were used to develop calibration equations: linear correlation with a difference in absorbance (AA), stepwise multiple linear regression (MLR), and partial least squares (PLS). A validation set contained 100 soybeans from 5 different varieties, ranging in moisture from 5 to 20%, wet basis. The standard error of prediction (SEP) for equations using absorbance data only was 0.88% for AA, 0.82% for MLR, and 0.81% for PLS. The SEP for equations using pathlength and absorbance data was 0.73% for AA, 0.69% for MLR, and 0.65% for PLS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.