Prediction of asphalt complex viscosity by artificial neural network based on Fourier transform infrared spectroscopy

2019 ◽  
Vol 37 (14) ◽  
pp. 1731-1737 ◽  
Author(s):  
Sen Han ◽  
Zhuang Zhang ◽  
Ye Yuan ◽  
Kang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Complex Viscosity ◽  
Artificial Neural

Identification and Quantification of Industrial Grade Glycerol Adulteration in Red Wine with Fourier Transform Infrared Spectroscopy Using Chemometrics and Artificial Neural Networks

2005 ◽  
Vol 59 (12) ◽  
pp. 1553-1561 ◽  
Author(s):  
Vivechana Dixit ◽  
Jagdish C. Tewari ◽  
Byoung-Kwan Cho ◽  
Joseph M. K. Irudayaraj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Red Wine ◽  
Radial Basis Function Network ◽  
Fourier Transform Infrared ◽  
Linear Discriminant ◽  
Industrial Grade ◽  
Artificial Neural ◽  
Propagation Network

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900–1500 cm−1. Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis ( r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

scholarly journals Artificial neural network-assisted Fourier transform infrared spectroscopy for differentiation of Salmonella serogroups and its application on epidemiological tracing of Salmonella Bovismorbificans outbreak isolates from fresh sprouts

2019 ◽  
Vol 366 (15) ◽  
Author(s):  
Helene Oberreuter ◽  
Jörg Rau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Ftir Spectroscopy ◽  
Fourier Transform Infrared ◽  
Salmonella Infections ◽  
Salmonella Serotypes ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRACT Salmonellae represent one of the most common bacterial infection reagents in both humans and animals. For detection and epidemiological elucidation of Salmonella infections, determination of Salmonella serotypes and differentiation between different Salmonella isolates is crucial. In the first part of this study, Artificial Neural Network (ANN)-assisted Fourier transform infrared (FTIR) spectroscopy was used to establish a method for subtyping Salmonella isolates according to their serogroups. For this, 290 Salmonella strains from 35 different serogroups were used to establish an ANN for differentiation between infrared spectra of 10 different Salmonella serogroups (B, C1, C2-C3, D1/D2, E1, E4, F, G, H, O:55) vs. the remaining serogroups. In the final ANN, sensitivity values ranged between 90 and 100% for most of the 10 serogroups under investigation. In the second part of this study, ANN-assisted FTIR spectroscopy was applied for epidemiological distinction of Salmonella Bovismorbificans outbreak isolates from fresh sprouts vs. isolates from other sources. Four Salmonella Bovismorbificans isolates from human and food origin in the context of a Southern German outbreak were successfully discriminated from other S. Bovismorbificans isolates from various sources. ANN-assisted FTIR spectroscopy is thus an effective tool for discrimination of Salmonella isolates at or even below serogroup level.

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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