Exploring yeast diversity of dry-salted naturally black olives from Greek retail outlets with culture dependent and independent molecular methods.

In the present study, the physicochemical (pH, water activity, moisture content, salt concentration) classical plate counts (total viable counts, yeasts, lactic acid bacteria, Staphylococcus aureus, Pseudomonas spp., Enterobacteriaceae) and amplicon sequencing of naturally black dry-salted olives obtained from different retail outlets of the Greek market were investigated. According to the results, the values of the physicochemical characteristics presented great variability among the samples. Specifically, pH and water activity (aw) values ranged between 4.0 and 5.0, as well as between 0.58 and 0.91, respectively. Moisture content varied between 17.3 and 56.7 % (g Η2Ο/100 g of olive pulp), whereas salt concentration ranged from 5.26 to 9.15 % (g NaCl/100 g of olive pulp). No lactic acid bacteria, S. aureus, Pseudomonas spp. and Enterobacteriaceae were detected. The mycobiota consisted of yeasts that were further characterized and identified by culture-dependent (rep-PCR, ITS-PCR, and RFLP) and amplicon target sequencing (ATS). Pichia membranifaciens, Candida sorbosivorans, Citeromyces nyonsensis, Candida etchelsii, Wickerhamomyces subpelliculosus, Candida apicola, Wickerhamomyces anomalus, Torulaspora delbrueckii and Candida versatilis were the dominant species according to ITS sequencing (culture-dependent), while ATS revealed the dominance of C. etchelsii, Pichia triangularis, P. membranifaciens, and C. versatilis among samples. The results of this study demonstrated considerable variability in quality attributes among the different commercial samples of dry-salted olives, reflecting a lack of standardization in the processing of this commercial style. However, the majority of the samples were characterized by satisfactory microbiological and hygienic quality and complied with the requirements of the trade standard for table olives of the International Olive Council (IOC) for this processing style in terms of salt concentration. In addition, the diversity of yeast species was elucidated for the first time in commercially available products, increasing our knowledge on the microbial ecology of this traditional food. Further investigation into the technological and multifunctional traits of the dominant yeast species may result in better control during dry-salting and enhance the quality and shelf-life of the final product.

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.

Fermentation of cv. Kalamata Natural Black Olives with Potential Multifunctional Yeast Starters.

The purpose of this study was to explore the inoculated fermentation of cv. Kalamata natural black olives using selected strains of yeast cultures with multifunctional potential. For this purpose, five yeast starters belonging to Candida boidinii (four starters) and Saccharomyces cerevisiae (one starter), previously isolated from table olive fermentation of the same variety and screened for their technological characteristics and probiotic potential, were inoculated in brines at the beginning of fermentation. Microbial populations (lactic acid bacteria, yeasts, and Enterobacteriaceae), pH, titratable acidity, organic acids, and ethanol were monitored during fermentation for a period of 5 months. At the same time, the survival of each starter was assessed by culture-dependent molecular identification at the beginning (0 days), middle (75 days), and final stages (150 days) of fermentation in the brines and olives (at the end of the process only). The results revealed the coexistence of yeasts and lactic acid bacteria (LAB) throughout fermentation in most processes and also the absence of Enterobacteriaceae after the first 20 days of brining. The population of yeasts remained 2 log cycles below LAB counts, except for in the inoculated treatment with C. boidinii Y28, where the yeast starter prevailed from day 60 until the end of the fermentation, as well as in the inoculated treatment with C. boidinii Y30, where no LAB could be detected in the brines after 38 days. At the end of the process, LAB ranged between 4.6 and 6.8 log10 CFU/mL, while yeasts were close to 5.0 log10 CFU/mL, except for the inoculated fermentation with C. boidinii Y27 and spontaneous fermentation (control), in which the yeast counts were close to 3.5 log10 CFU/mL. At the end of fermentation, the recovery percentage of C. boidinii Y27 was 50% in the brines and 45% in the olives. C. boidinii Y28 and S. cerevisiae Y34 could be recovered at 25% and 5% in the brine, respectively, whereas neither starter could be detected in the olives. For C. boidinii Y30, the recovery percentage was 25% in the brine and 10% in the olives. Finally, C. boidinii Y31 could not be detected in the brines and survived at a low percentage (10%) in the olives.

Exploring microbial communities of Spanish-style green table olives of Conservolea and Halkidiki cultivars during modified atmosphere packaging in multi-layered pouches through culture-dependent techniques and metataxonomic analysis.

Τhe aim of this study was to investigate the microbiological and physicochemical changes of pitted green olives (cvs. Conservolea and Halkidiki) elaborated in the Spanish style method and packaged in two types of multi-layered pouches under nitrogen atmosphere (100% N2) for a period of 12 months. Moreover, the evolution of microbial consortia at the beginning (0 days), middle (180 days) and final (360 days) time points of storage was elucidated by plating, genotyped and identified through RAPD-PCR and ITS region amplicon sequencing, respectively, and subsequently metataxonomic analysis (for fungal communities only). Results showed that no enterobacteria could be detected on olive drupes, whereas the dominant microbiota from the onset of storage in both pouches consisted of LAB in populations ranging between ca. 4.2-6.6 log CFU/g. Although yeasts were initially enumerated at ca. 5.5 log CFU/g, they declined rapidly and could not be detected by plate counting after 30 days. The pH values increased from 4.11 to 4.24 and 4.03 to 4.12 at the beginning and end of storage for cvs. Halkidiki and Conservolea green olives, respectively. The total color difference index (ΔΕ*) presented a perceivable change in visual color (ΔΕ* > 2.0) only in cv. Conservolea olives during storage. Finally, molecular fingerprinting RAPD-PCR and 16S amplicon based identification revealed the dominance of five LAB species, namely Pediococcus ethanolidurans, Lactiplantibacillus pentosus, Lentilactobacillus rapi, Lentilactobacillus parafarraginis and Lentilactobacillus buchneri. Regarding the identification of yeasts, the metagenetic amplicon sequencing approach revealed fungal complexity in the olive samples. Pichia manshurica and Pichia membranifaciens prevailed during the first and middle stages of storage, whereas at the final stage higher complexity was noticeable.

Application of Fourier Transform Infrared (FT-IR) Spectroscopy, Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets.

The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO2, 19% O2, 48% N2) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R2) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSEC) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R2 values of 0.79 and 0.77, and RMSEC values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R2 (0.34 and 0.17) and the highest RMSEC (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets.

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.

Fruit Juice Spoilage by Alicyclobacillus: Detection and Control Methods-A Comprehensive Review.

Fruit juices have an important place in humans' healthy diet. They are considered to be shelf stable products due to their low pH that prevents the growth of most bacteria. However thermo-acidophilic endospore forming bacteria of the genus Alicyclobacillus have the potential to cause spoilage of commercially pasteurized fruit juices. The flat sour type spoilage, with absence of gas production but presence of chemical spoilage compounds (mostly guaiacol) and the ability of Alicyclobacillus spores to survive after pasteurization and germinate under favorable conditions make them a major concern for the fruit juice industry worldwide. Their special characteristics and presence in the fruit juice industry has resulted in the development of many isolation and identification methods based on cell detection (plating methods, ELISA, flow cytometry), nucleic acid analysis (PCR, RAPD-PCR, ERIC-PCR, DGGE-PCR, RT-PCR, RFLP-PCR, IMS-PCR, qPCR, and 16S rRNA sequencing) and measurement of their metabolites (HPLC, GC, GC-MS, GC-O, GC-SPME, Electronic nose, and FTIR). Early detection is a big challenge that can reduce economic loss in the industry while the development of control methods targeting the inactivation of Alicyclobacillus is of paramount importance as well. This review includes a discussion of the various chemical (oxidants, natural compounds of microbial, animal and plant origin), physical (thermal pasteurization), and non-thermal (High Hydrostatic Pressure, High Pressure Homogenization, ultrasound, microwaves, UV-C light, irradiation, ohmic heating and Pulse Electric Field) treatments to control Alicyclobacillus growth in order to ensure the quality and the extended shelf life of fruit juices.

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.

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.

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.

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% Ν2,) 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., H2S 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 H2S-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.

Spoilage potential of Bacillus subtilis in a neutral-pH dairy dessert.

The objective of this study was the characterization of the microbiota associated with spoilage of vanilla cream pudding during storage at different temperatures. Commercial cream samples were stored aerobically at 4, 8, 12 and 15 °C for a maximum time period of 40 days. At appropriate time intervals, cream samples were subjected to: (i) microbiological analyses, and (ii) high-performance liquid chromatography (HPLC). Furthermore, the spoilage microbiota was identified through repetitive extragenic palindrome-PCR, while selected isolates were further characterized based on sequencing of the V1-V3 region of the 16S rRNA gene. Microbial growth was observed only during storage of cream samples at 12 and 15 °C, with the applied genotypic analysis demonstrating that Bacillus subtilis subsp. subtilis was the dominant spoilage microorganism of this product. Based on the HPLC analysis results, citric acid and sucrose were the most abundant organic acid and sugar, respectively throughout storage of cream pudding, whereas notable changes mainly included: (i) increase in the concentration of lactic acid and to a lesser extent of formic and acetic acids, and (ii) increase in the concentration of glucose and fructose at the expense of sucrose and lactose. The results of this study should be useful for the dairy industry in detecting and controlling microbiological spoilage in cream pudding and other chilled, neutral-pH dairy desserts.

A machine learning workflow for raw food spectroscopic classification in a future industry.

Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure quality and affordability, reducing at the same time the costs of keeping the food fresh and increase productivity. A system, as the one presented herein, for raw food categorization is needed in future food industries to automate food classification according to type, the process of algorithm approaches that will be applied to every different food origin and also for serving disabled people. The purpose of this work was to develop a machine learning workflow based on supervised PLS regression and SVM classification, towards automated raw food categorization from FTIR. The system exhibited high efficiency in multi-class classification of 7 different types of raw food. The selected food samples, were diverse in terms of storage conditions (temperature, storage time and packaging), while the variability within each food was also taken into account by several different batches; leading in a classifier able to embed this variation towards increased robustness and efficiency, ready for real life applications targeting to the digital transformation of the food industry.

Spoilage Potential of Pseudomonas (P. fragi, P. putida) and LAB (Leuconostoc mesenteroides, Lactobacillus sakei) Strains and Their Volatilome Profile During Storage of Sterile Pork Meat Using GC/MS and Data Analytics.

The aim of the present study was to investigate the evolution of the volatile compounds of aerobically stored sterile pork meat as a consequence of the metabolic activities of inoculated specific spoilage microorganisms. Thus, Pseudomonas fragi, Pseudomonas putida, Lactobacillus sakei and Leuconostoc mesenteroides were inoculated in monocultures, dual cultures and a cocktail culture of all strains on sterile pork meat stored aerobically at 4 and 10 °C. Microbiological and sensory analyses, as well as pH measurements, were performed, along with headspace solid-phase microextraction gas chromatography/mass spectroscopy (headspace SPME-GC/MS) analysis. Data analytics were used to correlate the volatile compounds with the spoilage potential of each stain using multivariate data analysis. The results for the sensory discrimination showed that the volatiles that dominated in spoiled samples consisted mostly of alcohols, ketones and two esters (butyl acetate and ethyl acetate), while at fresh samples, dimethyl sulfide, furans, acetoin and ethyl lactate were detected. On the other hand, 2-butanone, diacetyl and acetaldehyde were among the volatile compounds that were mainly correlated with the inoculated meat during storage. In addition, P. fragi was positively correlated with a higher number of volatiles compared to the other strains, strengthening the hypothesis that volatile compound production is strain-dependent.

Unraveling the Microbiota of Natural Black cv. Kalamata Fermented Olives through 16S and ITS Metataxonomic Analysis.

Kalamata natural black olives are one of the most economically important Greek varieties. The microbial ecology of table olives is highly influenced by the co-existence of bacteria and yeasts/fungi, as well as the physicochemical parameters throughout the fermentation. Therefore, the aim of this study was the identification of bacterial and yeast/fungal microbiota of both olives and brines obtained from 29 cv. Kalamata olive samples industrially fermented in the two main producing geographical regions of Greece, namely Aitoloakarnania and Messinia/Lakonia. The potential microbial biogeography association between certain taxa and geographical area was also assessed. The dominant bacterial family identified in olive and brine samples from both regions was Lactobacillaceae, presenting, however, higher average abundances in the samples from Aitoloakarnania compared to Messinia/Lakonia. At the genus level, Lactobacillus, Celerinatantimonas, Propionibacterium and Pseudomonas were the most abundant. In addition, the yeasts/fungal communities were less diverse compared to those of bacteria, with Pichiaceae being the dominant family and Pichia, Ogataea, and Saccharomyces being the most abundant genera. To the best of our knowledge, this is the first report on the microbiota of both olives and brines of cv. Kalamata black olives fermented on an industrial scale between two geographical regions of Greece using metagenomics analysis.

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.

Competitive yeast action against Aspergillus carbonarius growth and ochratoxin A production.

The aim of the present study was to investigate the interaction between 67 different yeast isolates and 3 wild isolates of Aspergillus carbonarius originated from Greek vineyards and characterized by high ochratoxigenic potential. The selected fungi were used either as single cultures or combined in a mixed culture. Yeasts and fungi were grown as mono-cultures and co-cultures in solid (MEA, CYA) and liquid (CY broth) media, grape berries and sterilized grape juice. Fungal growth was monitored by means of colony area measurements. The model of Baranyi and Roberts was further fitted to growth data to provide estimates of the colony area growth rate (cm2/day). Moreover, OTA analysis was undertaken for CY broth and agar as well as for grape berries and juice, on the 8th and 15th days of incubation at 25 °C. A significant reduction in fungal growth rate, final colony size and toxin production was observed in both liquid and solid media by the different yeast species assayed. The most competitive strains belonged to Saccharomyces, Pichia, Metschnikowia, Dekkera and Rhodotorula genera. Similar results were obtained from inoculated grape berries and grape juice. Specifically, Saccharomyces cerevisiae Y33 resulted in a decrease in fungal colony area of >90% and 93% after 3 and 4 days of co-culture, respectively. Similar results were obtained for OTA, where toxin concentration of the highest producer (A. carbonarius F3) was reduced from 14,983 and 31,565 ng/mL at 8 and 15 days, respectively, to 5 ng/mL and below detection limit (1 ng/g) when co-cultured with S. cerevisiae Y33. The results of this study could provide a pool of yeast species that must be further investigated for potential application as biological control agents at pre- and post-harvest level in wine and grape juice processing.

Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging.

The performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 °C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC ≤ 2.0 log CFU/g (fresh samples) and TVC ≥ 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2-6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2-5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.