Identification of oil, sugar and crude fiber during tobacco (Nicotiana tabacum L.) seed development based on near infrared spectroscopy. (April 2018)

• Record Type: Journal Article
• Authors: Li, Zhan; Li, Cheng; Gao, Yue; Ma, Wenguang; Zheng, Yunye; Niu, Yongzhi; Guan, Yajing; Hu, Jin
• Is Part Of: Biomass and bioenergy. Volume 111(2018)
• Page Start: 39
• Page End: 45
• Publication Date: 2018-04
• Subjects: Tobacco seed -- Seed development -- Seed composition -- Near infrared spectroscopy; 1st D first derivate -- LS-SVM least-squares support vector machine -- LVs latent variables -- MC-UVE Monte Carlo uninformative variable elimination -- MSC multiplicative scatter correction -- NIR near infrared reflection -- PLS partial least squares -- RBF radial basis function -- RMSECV the root-mean square error of cross validation -- RMSEP the root mean square error of prediction -- RPD the residual predictive deviation -- Rp2 the coefficient of determination of prediction -- Rc2 the coefficient for determination of calibration -- SD standard deviation -- SG Savitzky-Goly -- SNV standard normal variate -- SVM support vector machine; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2018.01.017
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: Tobacco seeds are a potential feedstock for biofuels. To insure make full use of tobacco seed biomass, the present study was carried out to estimate seed oil, sugar and crude fiber during seed development through near infrared spectroscopy (NIRS) nondestructive determination. Four pre-processing methods, Savitzky-Goly after standard normal variate (SNV-SG), first Savitzky-Goly derivative after standard normal variate (SNV-SG-1 st D), Savitzky-Goly after multiplicative scatter correction (MSC-SG) and first Savitzky-Goly derivative after multiplicative scatter correction (MSC-SG-1 st D), were respectively performed to optimize the original spectra before establishment of the calibration models. Then linear partial least squares (PLS) and nonlinear least-squares support vector machine (LS-SVM) methods were utilized to develop the calibration models, in which the LS-SVM models were found to have better performance than PLS models. The best LS-SVM models of oil, sugar and crude fiber were established after pre-processed by MSC-SG-1st D. These results indicated that NIRS was suitable to rapidly and accurately analyze tobacco seed composition. Highlights: NIR study to examine biomass including oil, sugar and crude fiber during tobacco seed development to insure full use of tobacco seed. The NIR technique could be rapid, accurate and easy insight into the tobacco seed development process. The models could be used to develop a simple and quick instrument or may provide important guidance for tobacco biomass energy industry.