scholarly journals Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Hongtu Xie ◽  
Jinsong Zhao ◽  
Qiubing Wang ◽  
Yueyu Sui ◽  
Jingkuan Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Soil Type ◽  
Near Infrared

Prediction of saponin content in soapnut (Sapindus mukorossi Gaertn.) fruits by near infrared spectroscopy

2018 ◽  
Vol 26 (2) ◽  
pp. 95-100 ◽  
Author(s):  
Yanjie Li ◽  
Wenhao Shao ◽  
Ruxiang Dong ◽  
Jingmin Jiang ◽  
Songfeng Diao
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Multivariate Correlation ◽  
Saponin Content

In this study, near infrared spectroscopy has been demonstrated to quickly determine the saponin content in soapnut fruits. Partial least squares analysis combined with pre-processing methods and significance multivariate correlation variable selection was introduced to develop a statistical model calibrated for saponin content in soapnut fruits. The results showed that the first derivative yielded the best partial least squares calibration models with spectra of both the surface of dried fruits and the powder of dry seeded fruits with root mean square error of calibration values of 0.85% and 0.59%, respectively. The surface model presented less accuracy than the powder model. However, when the significance multivariate correlation variable selection method was applied to select the best variables from the spectra, the partial least squares models using spectra of surface and powder samples became similar, with higher R2 values (0.84 and 0.90), lower root mean square error of calibration values of 0.23% and 0.39%. It was suggested that near infrared spectroscopy could be a promising and rapid method for predicting the saponin content in the soapnut fruits without grinding them into powder.

scholarly journals Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lianqing Zhu ◽  
Haitao Chang ◽  
Qun Zhou ◽  
Zhongyu Wang
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Salvia Miltiorrhiza ◽  
Classification Model ◽  
Local Variable

In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection.

scholarly journals Near-Infrared Spectroscopy (NIRS) as a Tool for Classification of Pre-Sliced Iberian Salchichón, Modified Atmosphere Packaged (MAP) According to the Official Commercial Categories of Raw Meat

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1865
Author(s):  
Alberto Ortiz ◽  
Lucía León ◽  
Rebeca Contador ◽  
David Tejerina
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Raw Material ◽  
Modified Atmosphere ◽  
Least Squares Regression ◽  
Linear Discriminant

This study evaluates near-infrared spectroscopy (NIRS) feasibility in combination with various pre-treatments and chemometric approaches for pre-sliced Iberian salchichón under modified atmosphere (MAP) classification according to the official commercial category (defined by the combination of genotype and feeding regime) of the raw material used for its manufacturing (Black and Red purebred Iberian and Iberian × Duroc crossed (50%) pigs, respectively, reared outdoors in a Montanera system and White Iberian × Duroc crossed (50%) pigs with feed based on commercial fodder) without opening the package. In parallel, NIRS feasibility in combination with partial least squares regression (PLSR) to predict main quality traits was assessed. The best-fitting models developed by means of partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) yielded high discriminant ability and thus offered a tool to support the assignment of pre-sliced MAP Iberian salchichón according to the commercial category of the raw material. In addition, good predictive ability for C18:3 n-3 was obtained, which may help to support quality control.

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