Artificial Neural Networks for the Automated Detection of Trichloroethylene by Passive Fourier Transform Infrared Spectrometry

2000 ◽  
Vol 72 (7) ◽  
pp. 1680-1689 ◽  
Author(s):  
Cheryl L. Hammer ◽  
Gary W. Small ◽  
Roger J. Combs ◽  
Robert B. Knapp ◽  
Robert T. Kroutil
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Fourier Transform Infrared ◽  
Automated Detection ◽  
Infrared Spectrometry ◽  
Fourier Transform Infrared Spectrometry ◽  
Artificial Neural

scholarly journals Classifying Degraded Three-Dimensionally Printed Polylactic Acid Specimens Using Artificial Neural Networks based on Fourier Transform Infrared Spectroscopy

2019 ◽  
Vol 9 (13) ◽  
pp. 2772
Author(s):  
Sung-Uk Zhang
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Thermal Degradation ◽  
Polylactic Acid ◽  
Fourier Transform Infrared ◽  
Ann Model ◽  
Flexible Material ◽  
3D Printed ◽  
Artificial Neural

Fused filament fabrication (FFF) is commonly employed in multiple domains to realize inexpensive and flexible material extrusion systems with thermoplastic materials. Among the several types of thermoplastic materials, polylactic acid (PLA), an environment-friendly bio-plastic, is commonly used for FFF for the sake of the safety of the manufacturing process. However, thermal degradation of three-dimensionally (3D)-printed PLA products is inevitable, and it is one of the failure mechanisms of thermoplastic products. The present study focuses on the thermal degradation of 3D-printed PLA specimens. A classification methodology using artificial neural networks (ANNs) based on Fourier transform infrared (FTIR) and was developed. Under the given experimental conditions, the ANN model could classify four levels of thermal degradation. Among the FTIR spectra recorded from 650 cm−1 to 4000 cm−1, the ANN model could suggest the best wavenumber ranges for classification.

Classification of Fourier Transform Infrared Microscopic Imaging Data of Human Breast Cells by Cluster Analysis and Artificial Neural Networks

2003 ◽  
Vol 57 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Lin Zhang ◽  
Gary W. Small ◽  
Abigail S. Haka ◽  
Linda H. Kidder ◽  
E. Neil Lewis
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Fourier Transform Infrared ◽  
Carcinoma Cells ◽  
Human Breast ◽  
Microscopic Imaging ◽  
Artificial Neural

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden–Fletcher–Goldfarb–Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Automated detection of sulfur dioxide in stack emissions by passive Fourier transform infrared spectrometry

2001 ◽  
Vol 27 (2) ◽  
pp. 97-107 ◽  
Author(s):  
Frederick W Koehler ◽  
Gary W Small ◽  
Roger J Combs ◽  
Robert B Knapp ◽  
Robert T Kroutil
Keyword(s):  
Fourier Transform ◽  
Sulfur Dioxide ◽  
Fourier Transform Infrared ◽  
Automated Detection ◽  
Infrared Spectrometry ◽  
Fourier Transform Infrared Spectrometry ◽  
Stack Emissions

Automated detection of methanol vapour by open path Fourier transform infrared spectrometry

1994 ◽  
Vol 297 (3) ◽  
pp. 387-403 ◽  
Author(s):  
Arjun S. Bangalore ◽  
Gary W. Small ◽  
Roger J. Combs ◽  
Robert B. Knapp ◽  
Robert T. Kroutil
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Automated Detection ◽  
Infrared Spectrometry ◽  
Fourier Transform Infrared Spectrometry ◽  
Open Path ◽  
Methanol Vapour

Identification of Propionibacteria to the species level using Fourier transform infrared spectroscopy and artificial neural networks

2013 ◽  
Vol 16 (2) ◽  
pp. 351-357 ◽  
Author(s):  
B. Dziuba
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Species Level ◽  
Ftir Spectra ◽  
Multilayer Perceptrons ◽  
Artificial Neural

Abstract Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANN’s) were used to identify species of Propionibacteria strains. The aim of the study was to improve the methodology to identify species of Propionibacteria strains, in which the differentiation index D, calculated based on Pearson’s correlation and cluster analyses were used to describe the correlation between the Fourier transform infrared spectra and bacteria as molecular systems brought unsatisfactory results. More advanced statistical methods of identification of the FTIR spectra with application of artificial neural networks (ANN’s) were used. In this experiment, the FTIR spectra of Propionibacteria strains stored in the library were used to develop artificial neural networks for their identification. Several multilayer perceptrons (MLP) and probabilistic neural networks (PNN) were tested. The practical value of selected artificial neural networks was assessed based on identification results of spectra of 9 reference strains and 28 isolates. To verify results of isolates identification, the PCR based method with the pairs of species-specific primers was used. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported correct identification of 93% bacteria of the genus Propionibacterium to the species level.

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