Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks

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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Commentary on “Goodacre R, Timmins ÉM, Rooney PJ, Rowland JJ, Kell DB: Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks. FEMS Microbiol Lett 1996; 140:233-239”, the most cited paper in the Journal for that year

FEMS Microbiology Letters ◽

10.1093/femsle/fnx018 ◽

2017 ◽

pp. fnx018

Author(s):

Royston Goodacre ◽

Douglas B. Kell

Keyword(s):

Neural Networks ◽

Fourier Transform ◽

Artificial Neural Networks ◽

Infrared Spectroscopy ◽

Fourier Transform Infrared Spectroscopy ◽

Diffuse Reflectance ◽

Fourier Transform Infrared ◽

Rapid Identification ◽

Enterococcus Species ◽

Artificial Neural

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

Polymers ◽

10.3390/polym11020363 ◽

2019 ◽

Vol 11 (2) ◽

pp. 363 ◽

Cited By ~ 3

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.

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

Journal of Spectroscopy ◽

10.1155/2013/538686 ◽

2013 ◽

Vol 2013 ◽

pp. 1-3 ◽

Cited By ~ 2

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%.

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