Simultaneous spectrophotometric determination of four metals by the kernel partial least squares method

1999 ◽  
Vol 45 (1-2) ◽  
pp. 87-93 ◽  
Author(s):  
Ling Gao ◽  
Shouxin Ren
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Spectrophotometric Determination ◽  
Least Squares Method ◽  
Kernel Partial Least Squares ◽  
Partial Least Squares Method

scholarly journals Simultaneous spectrophotometric determination of manganese, zinc and cobalt by kernel partial least–squares method

1998 ◽  
Vol 20 (6) ◽  
pp. 179-183 ◽  
Author(s):  
Ling Gao ◽  
Shouxin Ren
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Spectrophotometric Determination ◽  
Least Squares Method ◽  
Rapid Determination ◽  
Data Matrix ◽  
Kernel Partial Least Squares ◽  
Relative Standard ◽  
Cetyl Pyridinium

Simultaneous spectrophotometric determination of Mn, Zn and Co was studied by two methods, classical partial least-squares (PLS) and kernel partial least-squares (KPLS), with 2-(5-bromo-2- pyridylazo)-5-diethylaminephenol (5-Br-PADAP) and cetyl pyridinium bromide (CPB). Two programs, SPGRPLS and SPGRKPLS, were designed to perform the calculations. Eight error functions were calculated for deducing the number of factors. Data reductions were performed using principle component analysis. The KPLS method was applied for the rapid determination from a data matrix with many wavelengths and fewer numbers of samples. The relative standard errors of prediction (RSEP) for all components with KPLS and PLS methods were the same (0.0247). Experimental results showed both methods to be successful even where there was severe overlap of spectra.

scholarly journals Effective fault detection in structural health monitoring systems

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987323 ◽  
Author(s):  
Marwa Chaabane ◽  
Majdi Mansouri ◽  
Kamaleldin Abodayeh ◽  
Ahmed Ben Hamida ◽  
Hazem Nounou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Random Noise ◽  
Ratio Test ◽  
Multiscale Representation ◽  
Kernel Partial Least Squares ◽  
Partial Least Squares Model ◽  
Partial Least Squares Method

A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

scholarly journals Quantitative Determination of the Multicomponents With Overlapping Ultraviolet Spectra Using Wavelet-Packed Transform and Partial Least Squares

2006 ◽  
Vol 2006 ◽  
pp. 1-6
Author(s):  
Ling Gao ◽  
Shouxin Ren
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Wavelet Packet ◽  
Noise Removal ◽  
Time Frequency ◽  
Relative Standard ◽  
Best Basis Algorithm ◽  
Partial Least Squares Method

This paper presented a novel method named wavelet packet transform-based partial least squares method (WPTPLS) for simultaneous spectrophotometric determination ofα-naphthylamine, p-nitroaniline, and benzidine. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise. The quality of the noise removal can be improved by using best-basis algorithm and thresholding operation. Partial least squares (PLS) method uses both the response and concentration information to enhance its ability of prediction. In this case, by optimization, wavelet function and decomposition level for WPTPLS method were selected as Db16 and 3, respectively. The relative standard errors of prediction (RSEP) for all components with WPTPLS and PLS were 2.23% and 2.71%, respectively. Experimental results showed WPTPLS method to be successful and better than PLS.

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