A neural network approach to infrared spectrum interpretation

Robb, Ernest W.; Munk, Morton E.

doi:10.1007/bf01244838

Cited by 96 publications

(48 citation statements)

References 50 publications

(25 reference statements)

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“…The recent use of multilayered feed forward neural networks for the interpretation of spectral data shows good promise: the internal representations used by these networks are non-linear and are built up by a learning process based on examples. After the seminal works by Robb and Munk [2][3], many researchers explored the possibilities of these networks for the interpretation of infrared spectra [4][5][6][7][8]. However, the learning method used is a supervised learning process, and relies upon an existing structural classification.…”

Section: Introductionmentioning

confidence: 99%

Clustering of infrared spectra with Kohonen networks

Cleva

Cachet²,

Cabrol‐Bass³

1999

Analusis

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Abstract. The design of systems for spectral data interpretation requires clustering of chemical compounds based on their spectral characteristics. Kohonen networks have been shown to be efficient tools to achieve this clustering. These auto-organising systems perform a mapping between a high-dimensional variable space and a two-dimensional one. An application to infrared spectra of organic compounds is presented here. The non-supervised learning algorithm used allows classification of compounds by spectral characteristics without a priori knowledge. An analysis of the distribution of spectra on the resulting maps is used to build models for predicting the presence or absence of specific structural features. The performance of the models in recognising structural features is discussed and compared with the prediction of a multilayered feed forward network (MLFFN). Localisation of compounds wrongly classified by the MLFFN on the Kohonen maps allows to establish a link between the supervised and the unsupervised approaches.

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Section: Introductionmentioning

confidence: 99%

Clustering of infrared spectra with Kohonen networks

Cleva

Cachet²,

Cabrol‐Bass³

1999

Analusis

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“…Robb and Munk were among the first to use a neural network to process infrared spectra to determine the presence of various functional groups in a sample [ 10,8 ] . Because neural networks have the ability to learn, there is no requirement for a priori knowledge of the relationship between spectral peaks and the chemical compounds.…”

Section: Developing the Neural Networkmentioning

confidence: 99%

Using Neural Networks and NIR Spectrophotometry to Identify Fibers

Jasper

Kovacs

1994

Textile Research Journal

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A qualitative nondestructive technique for fiber identification was developed using near infrared (NIR) spectroscopy. A neural network was trained to identify 17 different fiber types using the NIR absorbance spectra from a library of 390 samples. The neural network model was verified by testing untrained samples. It was not only able to identify single fibers, but was also able to correctly identify blends of fibers from fabrics

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“…Meyer and co-workers have used the pattern recognition capabilities of neural networks to identify alditols [20] and oligosaccharides [21] from NMR spectra. Robb and Munk used linear neural networks for automated functional group analysis of infrared spectra [22] . Wythoff and coworkers developed a neural network application for automatic peak detection [23] .…”

Section: Introductionmentioning

confidence: 99%

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

Liu

2005

SPIE Proceedings

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The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

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A neural network approach to infrared spectrum interpretation

Cited by 96 publications

References 50 publications

Clustering of infrared spectra with Kohonen networks

Clustering of infrared spectra with Kohonen networks

Using Neural Networks and NIR Spectrophotometry to Identify Fibers

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

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