Dual Updating Strategy for Moving-Window Partial Least-Squares Based on Model Performance Assessment

2015 ◽  
Vol 54 (19) ◽  
pp. 5273-5284 ◽  
Author(s):  
Ouguan Xu ◽  
Jinfeng Liu ◽  
Yongfeng Fu ◽  
Xianghua Chen
Keyword(s):  
Performance Assessment ◽  
Least Squares ◽  
Partial Least Squares ◽  
Model Performance ◽  
Moving Window

Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods

2007 ◽  
Vol 15 (5) ◽  
pp. 291-297 ◽  
Author(s):  
Hai-Yan Fu ◽  
Shuang-Yan Huan ◽  
Lu Xu ◽  
Li-Juan Tang ◽  
Jian-Hui Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Geographical Origin ◽  
Quality Analysis ◽  
Moving Window ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

Selection of near infrared spectral information based on section combination moving window partial least squares

2017 ◽  
Vol 38 (1) ◽  
pp. 590-594
Author(s):  
Chen Yueyang ◽  
Gao Zhishan ◽  
Yu Xiaohui ◽  
Zhu Dan ◽  
Chen Ming ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Spectral Information ◽  
Moving Window ◽  
Infrared Spectral ◽  
Selection Of

Rapid Detection of Adulteration in Dendrobium huoshanense Using NIR Spectroscopy Coupled with Chemometric Methods

2020 ◽  
Author(s):  
Jing-Wen Hao ◽  
Yue Chen ◽  
Nai-Dong Chen ◽  
Chao-Feng Qin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Quantitative Model ◽  
Chemometric Methods ◽  
Chinese Market ◽  
Scattering Correction ◽  
Dendrobium Huoshanense

Abstract Background Dendrobium huoshanense (DHS) is a typical traditional Chinese medicine with unique medical and high economic values; however, it may easily be adulterated with cheaper alternatives (e.g. Dendrobium henanese, DHN), because of their similar appearances and tastes. Objective In this study, adulteration of DHN in DHS was detected by near infrared (NIR) spectroscopy combined with chemometric methods. Methods By performing partial least squares (PLS) analysis, PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure DHS samples and those adulterated with DHN. Results The R2 value obtained for the PLS-DA model was 0.4898 with an RMSEP error of 0.1554, resulting in a 100% accuracy of validation sample sets. Similarly, a PLSR model was also developed to quantify the amount of DHN adulterant in DHS samples. Experimental results indicated that the good performance of the multiplicative scattering correction (MSC) model is the better model showing a prediction performance of RMSEP of 2.38 and R2 of 0.9946. Conclusions These results suggest that the combination of NIR spectroscopy and chemometric method provides a fast, simple and reliable method for detecting adulteration of DHS. Highlights The method of classification allowed identification of both authentic and adulterated DHS samples. Comparison of six different techniques for spectra preprocessing to improve quantitative model performance was obtained with MSC derivative spectra. The method can detect most of the current DHS adulterations in the Chinese market.

Determination of sulfur dioxide content in osmotically dehydrated papaya and its classification by near infrared spectroscopy

2018 ◽  
Vol 26 (6) ◽  
pp. 359-368 ◽  
Author(s):  
Bumrungrat Rongtong ◽  
Thongchai Suwonsichon ◽  
Pitiporn Ritthiruangdej ◽  
Sumaporn Kasemsumran
Keyword(s):  
Infrared Spectroscopy ◽  
Sulfur Dioxide ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Moving Window ◽  
Least Squares Regression ◽  
Near Infrared Spectra

Sulfur dioxide (SO2) is used as a preservative in osmotically dehydrated papaya to improve product quality and extend shelf-life. The potential of near infrared spectroscopy, as a rapid method, was investigated to determine sulfur dioxide in osmotically dehydrated papaya. Commercial and laboratory osmotically dehydrated papaya samples were selected to determine the sulfur dioxide content using the Monier–Williams method. From the total of 350 samples, subsets were selected randomly for the calibration set (n=250) and validation set (n = 100). Near infrared spectra in the region 800–2400 nm were measured on the samples of osmotically dehydrated papaya. Quantitative analyses of sulfur dioxide in the osmotically dehydrated papaya and their qualitative analyses were carried out using multivariate analysis. Before developing models, a second derivative spectral pretreatment was applied to the original spectral data. Subsequently, two wavelength interval selection methods, namely moving window partial least squares regression (MWPLSR) and searching combination moving window partial least squares (SCMWPLS), were applied to determine the suitable input wavelength variables. For quantitative analysis, three linear models (partial least squares regression, MWPLSR and SCMWPLS) and a non-linear artificial neural network model were applied to develop predictive models. The results showed that the artificial neural network model produced the best performance, with correlation coefficient (R) and root mean square error of prediction values of 0.937 and 114.53 mg SO2 kg−1, respectively. Qualitative models were developed using partial least squares-discriminant analysis and soft independent modeling of class analogy (SIMCA) for the optimized combination of informative regions of the near infrared spectra to classify osmotically dehydrated papaya into three groups based on sulfur dioxide. The SIMCA in combination with SCMWPLS model had the highest correct classification rate (96%). The study demonstrated that near infrared spectroscopy combined with SCMWPLS is a powerful procedure for both quantitative and qualitative analyses of osmotically dehydrated papaya. Therefore, it was demonstrated that near infrared spectroscopy could be effective tools for food quality and safety evaluation in food industry.

Sign in / Sign up

Export Citation Format

Share Document