Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method

2010 ◽  
Vol 4 (3) ◽  
pp. 475-480 ◽  
Author(s):  
Wanwimol Klaypradit ◽  
Soraya Kerdpiboon ◽  
Rakesh K. Singh
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Fish Oil ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Spectroscopy Method ◽  
Menhaden Fish Oil ◽  
Artificial Neural

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.

scholarly journals Prediction of Thermal Exposure and Mechanical Behavior of Epoxy Resin Using Artificial Neural Networks and Fourier Transform Infrared Spectroscopy

Polymers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 363 ◽  
Author(s):  
Audrius Doblies ◽  
Benjamin Boll ◽  
Bodo Fiedler
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Data Processing ◽  
Epoxy Resin ◽  
Fourier Transform Infrared Spectroscopy ◽  
Thermal Exposure ◽  
Artificial Neural

Thermal degradation detection of cured epoxy resins and composites is currently limited to severe thermal damage in practice. Evaluating the change in mechanical properties after a short-time thermal exposure, as well as estimating the history of thermally degraded polymers, has remained a challenge until now. An approach to accurately predict the mechanical properties, as well as the thermal exposure time and temperature of epoxy resin, using Fourier-transform infrared spectroscopy (FTIR)-spectroscopy, data processing, and artificial neural networks, is presented here. Therefore, an epoxy resin has been fully cured and exposed to elevated temperatures for different time periods. A FTIR-spectrometer was used to measure molecular changes, using mid-IR (MIR)-FTIR for film samples and near-IR (NIR)-FTIR for bulk samples. A quantitative analysis of the thermally degraded film samples shows oxidation, chain-scission, and dehydration in the FTIR spectra in the MIR-range. Using NIR spectroscopy for the bulk samples, only minor changes in the FTIR spectra could be detected. However, using data processing, molecular information was extracted from the NIR range and a degradation model, using an artificial neural network, has been trained. Even though the changes due to thermal exposure were small, the presented model is capable of accurately predicting the time, temperature, and residual strength of the polymer.

scholarly journals Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy and Artificial Neural Networks Applied to DifferentiateEscherichia colipapG+/papG-Strains

2013 ◽  
Vol 2013 ◽  
pp. 1-3 ◽  
Author(s):  
Łukasz Lechowicz ◽  
Wioletta Adamus-Białek ◽  
Wiesław Kaca
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Attenuated Total Reflectance ◽  
Total Reflectance ◽  
Ft Ir

Fimbriae are an important pathogenic factor ofEscherichia coliduring development of urinary tract infections. Here, we describe a new method for identification ofEscherichia colipapG+frompapG-strains using the attenuated total reflectance Fourier transform infrared Spectroscopy (ATR FT-IR). We applied artificial neural networks to the analysis of the ATR FT-IR results. These methods allowed to discriminateE. colipapG+frompapG-strains with accuracy of 99%.

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.

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