Chromatography pattern recognition of Aroclors using iterative probabilistic neural networks

Magelssen, G. R.; Elling, John W.

doi:10.1016/s0021-9673(97)00052-6

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…The PNNs architecture is distinct from that of a standard back-propagation neural network and provides superior performance in classification applications [3,4,15]. The PNNs operates by defining a probability density function (PDF) for each data class based on the training set data and an optimized kernel width parameter (σ) [16][17][18][19]. The basic architecture of PNNs is shown in Fig.…”

Section: Theory Of Pnnsmentioning

confidence: 99%

Study of probabilistic neural networks to classify the active compounds in medicinal plants

Xue

Zhang

Liu

et al. 2005

Journal of Pharmaceutical and Biomedical Analysis

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Section: Theory Of Pnnsmentioning

confidence: 99%

Study of probabilistic neural networks to classify the active compounds in medicinal plants

Xue

Zhang

Liu

et al. 2005

Journal of Pharmaceutical and Biomedical Analysis

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“…PNNs are a class of neural networks that combine some of the best attributes of statistical pattern recognition methods and feed-forward neural networks. , They have been described as the neural network implementation of kernel discriminant analysis and were first introduced into the neural network literature by Specht in the late 1980s . Orginially developed for radar classification, in recent years the use of PNN has spread to other applications, , including chemistry. ,,− The PNN operates by defining a probability density function (PDF) for each data class. During the training phase, the pattern vectors (chemical fingerprints) in the training set are simply copied to the hidden layer of the PNN.…”

Section: Theorymentioning

confidence: 99%

Improved Probabilistic Neural Network Algorithm for Chemical Sensor Array Pattern Recognition

Shaffer

Rose-Pehrsson

1999

Anal. Chem.

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An improved probabilistic neural network (IPNN) algorithm for use in chemical sensor array pattern recognition applications is described. The IPNN is based on a modified probabilistic neural network (PNN) with three innovations designed to reduce the computational and memory requirements, to speed training, and to decrease the false alarm rate. The utility of this new approach is illustrated with the use of four data sets extracted from simulated and laboratory-collected surface acoustic wave sensor array data. A competitive learning strategy, based on a learning vector quantization neural network, is shown to reduce the storage and computation requirements. The IPNN hidden layer requires only a fraction of the storage space of a conventional PNN. A simple distance-based calculation is reported to approximate the optimal kernel width of a PNN. This calculation is found to decrease the training time and requires no user input. A general procedure for selecting the optimal rejection threshold for a PNN-based algorithm using Monte Carlo simulations is also demonstrated. This outlier rejection strategy is implemented for an IPNN classifier and found to reject ambiguous patterns, thereby decreasing the potential for false alarms.

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“…Gas chromatography/mass spectrometry (GC/MS) is currently considered the "gold standard" analysis system for chemical analysis, and is especially useful to analyze complex chemical samples. Due to GC/MS popularity as a chemical analysis tool, various chemometrics algorithms have been utilized to classify chromatograms from the instrument output [1][2][3]. We have used examples from this common analysis system to illustrate a pattern recognition approach based on autoregressive modeling, which can be broadly applied to many categories of solid-state chemical sensors.…”

Section: Introductionmentioning

confidence: 99%

Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model

2008

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This paper introduces autoregressive (AR) modeling as a novel method to classify outputs from gas chromatography (GC). The inverse Fourier transformation was applied to the original sensor data, and then an AR model was applied to transform data to generate AR model complex coefficients. This series of coefficients effectively contains a compressed version of all of the information in the original GC signal output. We applied this method to chromatograms resulting from proliferating bacteria species grown in culture. Three types of neural networks were used to classify the AR coefficients: backward propagating neural network (BPNN), radial basis function-principal component analysis (RBF-PCA) approach, and radial basis function-partial least squares regression (RBF-PLSR) approach. This exploratory study demonstrates the feasibility of using complex root coefficient patterns to distinguish various classes of experimental data, such as those from the different bacteria species. This cognition approach also proved to be robust and potentially useful for freeing us from time alignment of GC signals.

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Chromatography pattern recognition of Aroclors using iterative probabilistic neural networks

Cited by 10 publications

References 13 publications

Study of probabilistic neural networks to classify the active compounds in medicinal plants

Study of probabilistic neural networks to classify the active compounds in medicinal plants

Improved Probabilistic Neural Network Algorithm for Chemical Sensor Array Pattern Recognition

Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model

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