Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk

The aim of the present work was to investigate the applicability of a Wavelet Neural Network to describe the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in ultra high temperature (UHT) whole milk, and evaluate its performance against models used in predictive microbiology such as the re-parameterized Gompertz and modified Weibull equations. A comparative study with linear partial least squares regression (PLS-R) as well as neural network (NN) models demonstrated on the same dataset has been also considered. Milk was artificially inoculated with an initial population of the pathogen of ca. 107 CFU/ml and exposed to a range of high pressures (350, 450, 550, 600 MPa) for up to 40 min at ambient temperature (ca. 25 °C). Typical survival curves were obtained including a shoulder, a log-linear and a tailing phase. Increasing the magnitude of the applied pressure resulted in increasing levels of inactivation. Modelling approaches provided good fit to experimental training data as inferred by the low values of the root mean squared error (RMSE) and the high values of regression coefficient (R2). Models were validated at 400 and 500 MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas the wavelet network as well as the PLS and NN models were utilised as a one-step modelling approach. The prediction performance of the proposed learning-based network was better at both validation pressures. The development of accurate models to describe the survival curves of micro-organisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process

Quest of intelligent research tools for rapid evaluation of fish quality: FTIR spectroscopy and multispectral imaging versus microbiological analysis

The aim of the present study was to assess the microbiological quality of farmed sea bass (Dicentrarchus labrax) fillets stored under aerobic conditions and modified atmosphere packaging (MAP) (31% CO2, 23% O2, 46% N2,) at 0, 4, 8, and 12◦ C using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with data analytics, taking into account the results of conventional microbiological analysis. Fish samples were subjected to microbiological analysis (total viable counts (TVC), Pseudomonas spp., H2 S producing bacteria, Brochothrix thermosphacta, lactic acid bacteria (LAB), Enterobacteriaceae, and yeasts) and sensory evaluation, together with FTIR and MSI spectral data acquisition. Pseudomonas spp. and H2 S-producing bacteria were enumerated at higher population levels compared to other microorganisms, regardless of storage temperature and packaging condition. The developed partial least squares regression (PLS-R) models based on the FTIR spectra of fish stored aerobically and under MAP exhibited satisfactory performance in the estimation of TVC, with coefficients of determination (R2) at 0.78 and 0.99, respectively. In contrast, the performances of PLS-R models based on MSI spectral data were less accurate, with R2 values of 0.44 and 0.62 for fish samples stored aerobically and under MAP, respectively. FTIR spectroscopy is a promising tool to assess the microbiological quality of sea bass fillets stored in air and under MAP that could be effectively employed in the future as an alternative method to conventional microbiological analysis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland