Quantitative analysis of fruit firmness by near infrared spectroscopy and least-squares support vector machine

2009 ◽  
Author(s):  
Xiaping Fu ◽  
Yibin Ying ◽  
Huirong Xu
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Support Vector ◽  
Fruit Firmness

Least-squares support vector machine and successive projection algorithm for quantitative analysis of cotton-polyester textile by near infrared spectroscopy

2018 ◽  
Vol 26 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Yisen Liu ◽  
Songbin Zhou ◽  
Weixin Liu ◽  
Xinhui Yang ◽  
Jun Luo
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Projection Algorithm ◽  
Support Vector ◽  
Successive Projection ◽  
Successive Projection Algorithm

The application of near infrared spectroscopy for quantitative analysis of cotton-polyester textile was investigated in the present work. A total of 214 cotton-polyester fabric samples, covering the range from 0% to 100% cotton were measured and analyzed. Partial least squares and least-squares support vector machine models with all variables as input data were established. Furthermore, successive projection algorithm was used to select effective wavelengths and establish the successive projection algorithm-least-squares support vector machine models, with the comparison of two other effective wavelength selection methods: loading weights analysis and regression coefficient analysis. The calibration and validation results show that the successive projection algorithm-least-squares support vector machine model outperformed not only the partial least squares and least-squares support vector machine models with all variables as inputs, but also the least-squares support vector machine models with loading weights analysis and regression coefficient analysis effective wavelength selection. The root mean squared error of calibration and root mean squared error of prediction values of the successive projection algorithm-least-squares support vector machine regression model with the optimal performance were 0.77% and 1.17%, respectively. The overall results demonstrated that near infrared spectroscopy combined with least-squares support vector machine and successive projection algorithm could provide a simple, rapid, economical and non-destructive method for determining the composition of cotton-polyester textiles.

scholarly journals Discriminating the Geographical Origins of Chinese White Lotus Seeds by Near-Infrared Spectroscopy and Chemometrics

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Lu Xu ◽  
Chen-Bo Cai ◽  
Yuan-Bin She ◽  
Li-Juan Chen
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Discrimination Analysis ◽  
Protected Designation Of Origin ◽  
Chinese White

The traceability of a Chinese white lotus seed (WLS) with Protected Designation of Origin (PDO) was investigated using near-infrared (NIR) spectroscopy and chemometrics. Three chemometrics methods, discrimination analysis (DA), class modeling, and a newly proposed strategy, the fusion of DA and class modeling, were investigated to compare their capacity to trace the geographical origins of WLS. Least squares support vector machine (LS-SVM) was developed to distinguish the PDO WLS from non-PDO WLS of four main producing areas. A class modeling technique, one-class partial least squares (OCPLS), was developed only using the data of PDO WLS. By the fusion of LS-SVM and OCPLS, the best prediction sensitivity and specificity were 0.900 and 0.973, respectively. The results indicate that fusion of DA and class modeling can enhance the specificity for detection of non-PDO products. The conclusion is that DA and class modeling should be combined for tracing food geographical origins.

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