Fuzzy multivariate rule‐building expert systems: Minimal neural networks

Harrington, Peter de B.

doi:10.1002/cem.1180050506

Cited by 79 publications

(63 citation statements)

References 20 publications

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“…Two additional supervised classification methods were applied to the data from the five analytical methods (20,21). Both methods used the principal component transform as a lossless form of data compression.…”

Section: Resultsmentioning

confidence: 99%

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Discrimination Among Panax Species Using Spectral Fingerprinting

Chen¹,

Luthria²,

Harrington³

et al. 2011

j aoac int

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Spectral fingerprints of samples of three Panax species (P. quinquefolius L., P. ginseng, and P. notoginseng) were acquired using UV, NIR, and MS spectrometry. With principal components analysis (PCA), all three methods allowed visual discrimination between all three species. All three methods were able to discriminate between white and red ginseng and showed distinctive sub-groupings of red ginseng related to root quality (age/size). Analysis of variance (ANOVA) was used to evaluate the relative variance arising from the species, run, and analytical uncertainty and was used to identify the most information rich portions of the spectrum for NIR and UV. Accurate classification of the 3 species was obtained using partial least squares-discriminant analysis (PLS-DA) and fuzzy rule-building expert system (FuRES). Relatively poor accuracy was obtained using soft independent modeling of class analogy (SIMCA) when a single component was used.

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Section: Resultsmentioning

confidence: 99%

“…A home-built FuRES classifier was written that is described in detail (21). FuRES builds classification trees and has no adjustable parameters such as the number of components or latent variables of SIMCA and PLS.…”

Section: Methodsmentioning

confidence: 99%

Discrimination Among Panax Species Using Spectral Fingerprinting

Chen¹,

Luthria²,

Harrington³

et al. 2011

j aoac int

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show abstract

“…The fuzzy logistic values are the consequents of each rule, and the multivariate rules comprise the branches of the classification tree. The divide and conquer algorithm continues until all the data of each node consist of a single class [16], and the final classification tree allows the visualization of the inductive structure of the rules.…”

Section: Fuzzy Rule-building Expert Systemmentioning

confidence: 99%

Spectral Representation of Proton NMR Spectroscopy for the Pattern Recognition of Complex Materials

Harrington

Wang

2017

J. Anal. Test.

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Proton nuclear magnetic resonance (NMR) spectroscopy provides a powerful tool for chemical profiling, also known as spectral fingerprinting, because of its inherent reproducibility. NMR is now increasing in use for authentication of complex materials. Typically, the absorbance spectrum is used that is obtained as the phase-corrected real component of the Fourier transform (FT) of the free induction decay (FID). However, the practice discards half the information that is available in the dispersion spectrum obtained as the imaginary component from the FT. For qualitative analysis or quantitative analysis of small sets of absorbance peaks, the symmetric and sharp peaks of the real spectra work well. However, for pattern recognition of entire spectra, trading peak resolution for peak reproducibility is beneficial. The absolute value of the complex spectrum gives the length or magnitude of magnetization vector in the complex plane; therefore, the magnitude relates directly to the signal (i.e., induced magnetization). The magnitude spectrum is obtained as the absolute value from the real and imaginary spectral components after the FT of the FID. By breaking with tradition and using the magnitude spectrum the reproducibility of the spectra and consequent recognition rates can be improved. This study used a 500-MHz 1 H NMR instrument to obtain spectra from 4 diverse datasets; 12 tea extracts, 8 liquor samples, 9 hops extracts, and 25 Cannabis extracts. Six classifiers were statistically evaluated using 100 bootstrapped Latin partitions. The classifiers were a fuzzy rule-building expert system (FuRES) tree, support vector machine trees (SVMTreeG and SVMTreeH), a regularized linear discriminant analysis (LDA), super partial least squares discriminant analysis (sPLS-DA), and a one against all support vector machine (SVM). All classifiers gave better or equivalent results for the magnitude spectral representation than for the real spectra, except for one case of the 24 evaluations. In addition, the enhanced reproducibility of the absolute value spectra is demonstrated by comparisons of the pooled within sample standard deviations. For pattern recognition of NMR spectra, the magnitude spectrum is advocated.

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“…These include minimal neural networks, [117][118][119] radial basis functions, 114,120 self-organizing feature maps, 110,121 and autoassociative neural networks. 122,123 Another important advance has been the application of PyMS with ANNs to discriminate between methicillin-resistant and methicillin-sensitive Staphylococcus aureus.…”

Section: Identification and Differentiation Of Microbes By Pyms With mentioning

confidence: 99%