Assessment of the Microbial Spoilage and Quality of Marinated Chicken Souvlaki through Spectroscopic and Biomimetic Sensors and Data Fusion.

Fourier-transform infrared spectroscopy (FT-IR), multispectral imaging (MSI), and an electronic nose (E-nose) were implemented individually and in combination in an attempt to investigate and, hence, identify the complexity of the phenomenon of spoilage in poultry. For this purpose, marinated chicken souvlaki samples were subjected to storage experiments (isothermal conditions: 0, 5, and 10 °C; dynamic temperature conditions: 12 h at 0 °C, 8 h at 5 °C, and 4 h at 10 °C) under aerobic conditions. At pre-determined intervals, samples were microbiologically analyzed for the enumeration of total viable counts (TVCs) and Pseudomonas spp., while, in parallel, FT-IR, MSI, and E-nose measurements were acquired. Quantitative models of partial least squares-Regression (PLS-R) and support vector machine-regression (SVM-R) (separately for each sensor and in combination) were developed and validated for the estimation of TVCs in marinated chicken souvlaki. Furthermore, classification models of linear discriminant analysis (LDA), linear support vector machine (LSVM), and cubic support vector machines (CSVM) that classified samples into two quality classes (non-spoiled or spoiled) were optimized and evaluated. The model performance was assessed with data obtained by six different analysts and three different batches of marinated souvlaki. Concerning the estimation of the TVCs via the PLS-R model, the most efficient prediction was obtained with spectral data from MSI (root mean squared error-RMSE: 0.998 log CFU/g), as well as with combined data from FT-IR/MSI (RMSE: 0.983 log CFU/g). From the developed SVM-R models, the predictions derived from MSI and FT-IR/MSI data accurately estimated the TVCs with RMSE values of 0.973 and 0.999 log CFU/g, respectively. For the two-class models, the combined data from the FT-IR/MSI instruments analyzed with the CSVM algorithm provided an overall accuracy of 87.5%, followed by the MSI spectral data analyzed with LSVM, with an overall accuracy of 80%. The abovementioned findings highlighted the efficacy of these non-invasive rapid methods when used individually and in combination for the assessment of spoilage in marinated chicken products regardless of the impact of the analyst, season, or batch.

Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective.

Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.

Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis.

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken's surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

Quality and Safety Assessment of Edible Seaweeds Alaria esculenta and Saccharina latissima Cultivated in Scotland.

Within Europe over the last 10 years, there has been an increase in seaweeds cultivated for human consumption. For food safety reasons, it is important to assess the microbiological and nutritional quality of the biomass. The fresh and dried edible seaweeds Alaria esculenta and Saccharina latissima were assessed over two consecutive years for the presence of microorganisms. Seaweed samples supplied from Scotland were stored under isothermal conditions for specific time intervals depending on the sample's condition (fresh, dried or rehydrated). During storage, microbiological analyses were performed for the enumeration of Total Viable Counts (TVC), Pseudomonas spp., Enterobacteriaceae and Bacillus spp., as well as yeasts and molds. Additionally, bacterial colonies from the Marine Agar growth medium were isolated and subjected to PCR-RAPD analysis for characterization of the bacterial diversity of seaweeds. Bacterial isolates with different fingerprint patterns were further subjected to sequencing (16S rDNA, V1-V4 region). The presence of human pathogenic bacteria was also investigated. Results showed that the initial population of TVC was differentiated depending on the year of seaweed harvest, being closer to the enumeration limit (1.0 log CFU/g) in fresh samples from 2020 and higher in samples from 2019 (6.7 and 3.9 log CFU/g in A. esculenta and S. latissima, respectively). DNA-based analysis revealed the presence of Psychrobacter, Cobetia and Pseudomonas species in A. esculenta, while Psychrobacter and Micrococcus species were present in S. latissima.

Spoilage assessment of chicken breast fillets by means of fourier transform infrared spectroscopy and multispectral image analysis.

The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n â€‹= â€‹215) were conducted at 0, 5, 10, and 15 â€‹°C for up to 480 â€‹h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

Antifungal activity of Saccharomyces cerevisiae and assessment of ochratoxigenic load on currants by means of Real Time PCR.

Currants are prone to contamination by ochratoxin during cultivation, processing and storage conditions. Saccharomyces cerevisiae is considered to be among the main species of grape yeast flora able to control antagonistic fungi. In this study, the potential of S. cerevisiae Y33 was investigated to inhibit the growth of several fungal species indigenous to the microbiota of grapes. Moreover, the efficacy of this yeast species was investigated to inhibit OTA by toxin producing fungi both in vitro and in situ. For this purpose thirty-five different fungal species, belonging to the genera Aspergillus, Penicillium, Cladosporium, Fusarium and Alternaria interacted in vitro with S. cerevisiae on Malt Extract agar plates, stored at 25 °C for 14 days. Results showed that the highest OTA producer A. carbonarius F71 was inhibited more than 99% from day 7, in contrast to A. niger strains that presented enhanced OTA production at day 14 due to interaction with S. cerevisiae Y33. Additionally, the antifungal potential of the selected yeast was also studied in situ on currants subjected to different treatments and stored at 25 °C for 28 days. Microbiological analysis was undertaken for the enumeration of the bacterial and fungal flora, together with OTA determination at 7 and 21 days. To quantify A. carbonarius on all treated currant samples, molecular analysis with Real Time PCR was employed. A standard curve was prepared with A. carbonarius DNA. The efficiency of the curve was estimated to 10.416, the slope to -3.312 and the range of haploid genome that could be estimated was from 1.05 to 105∙105. The amount of A. carbonarius DNA in all treated currants samples, where the fungus was positively detected, ranged from as low as 0.08 to 562 ng DNA/g currants. The antifungal activity of S. cerevisiae Y33 was observed in all studied cases, causing inhibition of fungal growth and OTA production.

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products.

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

Assessment of the microbiological quality and safety of marinated chicken products from Greek retail outlets.

The prevalence of three pathogens in marinated chicken products and the evaluation of their quality by microbiological and sensory analysis were assessed. Eighty (80) samples obtained from several meat retail markets in Greece were analyzed for the presence of Campylobacter spp., Salmonella and Listeria monocytogenes. Concerning Campylobacter, rep-PCR and species specific PCR were applied for the differentiation and identification of isolates, respectively. The samples were subsequently stored aerobically at 4 °C for 5 days. Microbiological analysis, sensory assessment and HPLC analysis were carried out for the evaluation of spoilage microorganisms, sensory quality and the presence of preservatives (potassium sorbate and sodium benzoate). Τhe prevalence of Campylobacter spp., Salmonella, and Listeria monocytogenes was 50%, 11% and 44%, respectively. In the case of Campylobacter, from a total of 40 isolates, 27 were identified as Campylobacter coli, 4 as Campylobacter jejuni, whereas the remaining 9 belonged to unidentified Campylobacter species. Pseudomonas spp. was the dominant spoilage microbial genus in 43% of the samples, while in 31% of them a co-dominance of Pseudomonas spp. and Brochothrix thermosphacta was observed. Total aerobic counts increased to 7.0 log CFU/g at the 1st, 2nd or 3rd day of storage in 71% of the samples, while sensory analysis showed that 80% of the samples were characterized as spoiled after 3, 4 or 5 days. The presence of preservatives was confirmed in 31% of the samples and slightly affected the microbiological profile. In conclusion, the obtained data demonstrated the occurrence of foodborne pathogens and allowed the acquisition of an overall view about the microbiological quality of marinated chicken products.