ARTIFICIAL NEURAL NETWORKS (ANNs) APPLIED TO ATR-FTIR SPECTRA TO CLASSIFY MEDICALLY IMPORTANT Trichosporon SPECIES

2021 ◽  
pp. 51-54
Author(s):  
Abhila Parashar ◽  
Vijaylatha Rastogi ◽  
Mitanshu Sharma ◽  
Monica Bhatnagar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Ftir Spectra ◽  
Training Set ◽  
Classi Cation ◽  
Trichosporon Species

To distinguish clinically signicant fungus, Fourier transform infrared spectroscopy (FTIR) was used. In this work, 75 Trichosporon strains from ve different species were cultivated on SDA media and FTIR attenuated total reection (ATR) readings was taken. The classication (FTIR spectra) results of cluster analysis were compared to articial neural network (ANN) analysis (supervised approach). Validation of training set showed that both techniques properly categorized 100% of the spectra, at least for T. asahii (n = 62) and T. inkin (n = 8). With the addition of T. loubieri (n=1) and T. asteroids (n=1), the ANN's accuracy became reliant on the training database, resulting in 90% to 100% classication.

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.

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.

scholarly journals Efficient Discrimination of Some Moss Species by Fourier Transform Infrared Spectroscopy and Chemometrics

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Zhen Cao ◽  
Yongying Liu ◽  
Jiancheng Zhao
Keyword(s):  
Cluster Analysis ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Ftir Spectra ◽  
Chemical Components ◽  
Discrete Wavelet ◽  
Moss Species

Fourier transform infrared spectroscopy (FTIR) technique was used to classify 16 species from three moss families (Mielichhoferiaceae, Bryaceae, and Mniaceae). The FTIR spectra ranging from 4000 cm−1to 400 cm−1of the 16 species were obtained. To group the spectra according to their spectral similarity in a dendrogram, cluster analysis and principal component analysis (PCA) were performed. Cluster analysis combined with PCA was used to give a rough result of classification among the moss samples. However, some species belonging to the same genus exhibited very similar chemical components and similar FTIR spectra. Fourier self-deconvolution (FSD) was used to enhance the differences of the spectra. Discrete wavelet transform (DWT) was used to decompose the FTIR spectra ofMnium laevinerveandM. spinosum. Three scales were selected as the feature extracting space in the DWT domain. Results showed that FTIR spectroscopy combined with DWT was suitable for distinguishing different species of the same genus.

scholarly journals Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste

2021 ◽  
Author(s):  
Shengli Jiang ◽  
Zhuo Xu ◽  
Medhavi Kamran ◽  
Stas Zinchik ◽  
Sidike Paheding ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Total Reflection ◽  
Plastic Waste ◽  
Attenuated Total Reflection ◽  
Plastic Materials ◽  
Different Types

<p>We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.</p>

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 Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste

2021 ◽  
Author(s):  
Shengli Jiang ◽  
Zhuo Xu ◽  
Medhavi Kamran ◽  
Stas Zinchik ◽  
Sidike Paheding ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Total Reflection ◽  
Plastic Waste ◽  
Attenuated Total Reflection ◽  
Plastic Materials ◽  
Different Types

<p>We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.</p>

scholarly journals The use of Artificial Neural Network for lipid and glycaemic profiles quantification through infrared spectroscopy

2020 ◽  
Author(s):  
Henrique Hesse ◽  
Rejane Frozza ◽  
Valeriano Corbellini ◽  
Cézane Reuter ◽  
Miria Burgos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Infrared Spectra ◽  
Data Sets ◽  
Computational System ◽  
Artificial Neural ◽  
Nonlinear Correlations

This paper aims to look at the viability of the use of artificial neural networks to solve nonlinear correlations between infrared spectra and biochemical quantification tests, to build a computational system to predict the levels of glycaemic and lipid profiles using infrared spectroscopy. The studies of one of the parameters was modelled and showed signs of viability to quantify all parameters with the suggested methodology. Therefore, more complex and larger data sets are going to be tested with this technique.

scholarly journals APPLICATION OF PATTERN RECOGNITION TECHNIQUES FOR FAULT DETECTION OF CLUTCH RETAINER OF TRACTOR

2018 ◽  
Vol 47 (1) ◽  
pp. 31-36
Author(s):  
Mostafa Bahrami ◽  
Hossein Javadikia ◽  
Ebrahim Ebrahimi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Accurate Diagnosis ◽  
Pattern Recognition Techniques ◽  
The Neural Network ◽  
Power Spectral ◽  
Proposed Model

This study develops a technique based on pattern recognition for fault diagnosis of clutch retainer mechanism of MF285 tractor using the neural network. In this technique, time features and frequency domain features consist of Fast Fourier Transform (FFT) phase angle and Power Spectral Density (PSD) proposes to improve diagnosis ability. Three different cases, such as: normal condition, bearing wears and shaft wears were applied for signal processing. The data divides in two parts; in part one 70% data are dataset1 and in part two 30% for dataset2.At first, the artificial neural networks (ANN) are trained by 60% dataset1 and validated by 20% dataset1 and tested by 20% dataset1. Then, to more test of the proposed model, the network using the datasets2 are simulated. The results indicate effective ability in accurate diagnosis of various clutch retainer mechanism of MF285 tractor faults using pattern recognition networks.

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