The Effect of the PhoP/PhoQ System on the Regulation of Multi-Stress Adaptation Induced by Acid Stress in Salmonella Typhimurium.

Acidic stress in beef cattle slaughtering abattoirs can induce the acid adaptation response of in-plant contaminated Salmonella. This may further lead to multiple resistance responses threatening public health. Therefore, the acid, heat, osmotic and antibiotic resistances of Salmonella typhimurium (ATCC14028) were evaluated after a 90 min adaption in a pH = 5.4 "mild acid" Luria-Bertani medium. Differences in such resistances were also determined between the ∆phoP mutant and wild-type Salmonella strains to confirm the contribution of the PhoP/PhoQ system. The transcriptomic differences between the acid-adapted and ∆phoP strain were compared to explore the role of the PhoP/Q two-component system in regulating multi-stress resistance. Acid adaptation was found to increase the viability of Salmonella to lethal acid, heat and hyperosmotic treatments. In particular, acid adaptation significantly increased the resistance of Salmonella typhimurium to Polymyxin B, and such resistance can last for 21 days when the adapted strain was stored in meat extract medium at 4 °C. Transcriptomics analysis revealed 178 up-regulated and 274 down-regulated genes in the ∆phoP strain. The Salmonella infection, cationic antimicrobial peptide (CAMP) resistance, quorum sensing and two-component system pathways were down-regulated, while the bacterial tricarboxylic acid cycle pathways were up-regulated. Transcriptomics and RT-qPCR analyses revealed that the deletion of the phoP gene resulted in the down-regulation of the expression of genes related to lipid A modification and efflux pumps. These changes in the gene expression result in the change in net negative charge and the mobility of the cell membrane, resulting in enhanced CAMP resistance. The confirmation of multiple stress resistance under acid adaptation and the transcriptomic study in the current study may provide valuable information for the control of multiple stress resistance and meat safety.

Two-Month Consumption of Orange Juice Enriched with Vitamin D3 and Probiotics Decreases Body Weight, Insulin Resistance, Blood Lipids, and Arterial Blood Pressure in High-Cardiometabolic-Risk Patients on a Westernized Type Diet: Results from a Randomized Clinical Trial.

This study examined the effects of orange juice (OJ) supplemented with vitamin D3 (2000 IU) and probiotics (Lacticaseibacillus casei Shirota and Lacticaseibacillus rhamnosus GG, 108 cfu/mL) on cardiometabolic risk factors in overweight and obese adults following a Westernized-type diet. Fifty-three high-risk individuals were randomly assigned to one of two groups. Over 8 weeks, one group consumed a vitamin D3 and probiotic-enriched OJ and the other regular OJ (control). Diets remained unchanged and were documented through food diaries. Measures of metabolic and inflammatory markers and blood pressure were measured at the start and end of the study. Post-intervention, the enriched OJ group showed the following significant metabolic improvements (without changes in triglycerides, inflammation, or central blood pressure): reduced fasting insulin, peripheral blood pressure, body weight (-1.4 kg 95% CI: -2.4, -0.4), energy (-270 kcal 95% CI: -553.2, -13.7), macronutrient (dietary fat -238 kcal 95% CI: -11.9, -1.0; carbohydrates -155 kcal 95% CI: -282.4, -27.3; sugars -16.1 g 95% CI: -11.9, -1.0) intake, and better lipid profiles (total cholesterol -10.3 mg/dL 95% CI: -21.4, 0.9; LDL-C -7 mg/dL 95% CI: -13.5, -0.5). The enriched OJ led to weight loss, less energy/macronutrient consumption, improved lipid profiles, and increased insulin sensitivity after 8 weeks in those following a Westernized diet, thus indicating potential benefits for cardiometabolic risk. This study was a part of FunJuice-T2EDK-01922, which was funded by the EU Regional Development Fund and Greek National Resources.

Study of the transfer of Shiga toxin-producing Escherichia coli during the slaughter of cattle using molecular typing combined with epidemiologic data.

Investigation on the distribution and biological characteristics of Shiga-toxin producing Escherichia coli (STEC) during beef processing is essential for in-plant critical control points and food safety risk assessment. Serogroups and subtypes of stx genes of STEC strains isolated from beef processing lines were first investigated. Identification to cross-contamination among different sampling sites was further conducted by combining multilocus sequence typing (MLST) with the previous distribution and characterization data. The PCR-positive rate for STEC in 435 samples from two slaughter plants in China was 14.3% and the isolation rate for the 62 PCR positive and the entire set of 435 samples were 26% and 3.68% respectively. The existence of serotype O157:H7 (33%) and serogroups O121 (42%) and O26 (21%) as well as the high detection rate of high pathogenic gene stx2a (68%) in these serogroups indicated potential risk to the safety of beef. Traceability analysis showed that hide plays a critical role in cross-contamination between feces, lairage pens and post-washing carcasses from a molecular perspective. Intervening measures revolves around de-hiding should be involved in the in-plant safety control policy according to the tracing analysis.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud.

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

Short-Term Effects of Fruit Juice Enriched with Vitamin D3, n-3 PUFA, and Probiotics on Glycemic Responses: A Randomized Controlled Clinical Trial in Healthy Adults.

This study aimed to determine the glycemic index (GI) of a commercial mixed fruit juice (apple, orange, grape, and pomegranate; FJ) fortified with vitamin D3 or n-3 polyunsaturated fatty acids (PUFA) or probiotics, and their combination, and their effects on glycemic responses and salivary insulin concentrations. In a randomized controlled, double-blind, crossover study, 11 healthy participants (25 ± 2 years; five women; body mass index = 23 ± 1 kg/m2) were randomly assigned to receive five types of FJs [vitD (with vitamin D3); n-3 (with n-3 PUFA); probiotics (with Lacticaseibacillus casei Shirota and Lacticaseibacillus rhamnosus GG); vitD-n-3-probiotics FJ (combination of vitD3-n-3-probiotics), control (regular FJ)], all containing 50 g available carbohydrate, and glucose as reference drink. All FJs provided low GI values (control: 54; vitD3: 52; n-3: 51; probiotics: 50; and vitD-n-3-probiotics combination: 52, on glucose scale). Compared to the FJ control, the enriched FJs produced different postprandial glycemic and insulinemic responses and affected satiety scores. All FJ types, regardless of the added biofunctional ingredients, attenuated postprandial glycemic responses, which may offer advantages to glycemic control.

Deciphering the growth responses and genotypic diversity of bioluminescent Photobacterium phosphoreum on chicken meat during aerobic refrigerated storage.

The advent of high-throughput sequencing technologies in recent years has revealed the unexpected presence of genus Photobacterium within the chicken meat spoilage ecosystem. This study was undertaken to decipher the occurrence, the growth patterns and the genotypic biodiversity of Photobacterium phosphoreum on chicken breast fillets stored aerobically at 4 °C through conventional microbiological methods and molecular techniques. Samples were periodically cultured on marine broth agar (MA; supplemented with meat extract and vancomycin) for the enumeration of presumptive bioluminescent Photobacterium spp. In total, 90 bioluminescent bacteria were recovered from the initial (time of first appearance), middle and end stages of storage. Concomitantly, 95 total psychrotrophic/psychrophilic bacteria were isolated from the same medium to assess the presence and diversity of non-luminous photobacteria. Genetic diversity between bioluminescent isolates was assessed with two PCR-based DNA fingerprinting methods, i.e. RAPD and rep-PCR. Moreover, the characterization of selected bacterial isolates at the genus and/or species level was performed by sequencing of the 16S rRNA and/or gyrB gene. Bioluminescent bacteria were scarcely encountered in fresh samples at population levels of ca. 2.0 log CFU/g, whilst total psychrotrophic/psychrophilic bacteria were found at levels of ca. 4.4 log CFU/g. As time proceeded and close to shelf-life end, bioluminescent bacteria were encountered at higher populations, and were found at levels of 5.3 and 7.0 log CFU/g in samples from the second and third batch, respectively. In the first batch their presence was occasional and at levels up to 3.9 log CFU/g. Accordingly, total psychrotrophic/psychrophilic bacteria exceeded 8.4 log CFU/g at the end of storage, suggesting the possible underestimation of bioluminescent populations following the specific cultivation conditions. Sequence analysis assigned bioluminescent isolates to Photobacterium phosphoreum, while genetic fingerprinting revealed high intra-species variability. Respectively, total psychrotrophs/psychrophiles were assigned to genera Pseudomonas, Shewanella, Psychrobacter, Acinetobacter, Vibrio and Photobacterium. Non-luminous photobacteria were not identified within the psychrotrophs/psychrophiles. Results of the present study reveal the intra- and inter-batch variability on the occurrence and growth responses of P. phosphoreum and highlight its potential role in the chicken meat spoilage consortium.

Role of Microbial Interactions across Food-Related Bacteria on Biofilm Population and Biofilm Decontamination by a TiO2-Nanoparticle-Based Surfactant.

Microbial interactions play an important role in initial cell adhesion and the endurance of biofilm toward disinfectant stresses. The present study aimed to evaluate the effect of microbial interactions on biofilm formation and the disinfecting activity of an innovative photocatalytic surfactant based on TiO2 nanoparticles. Listeria monocytogenes, Salmonella Enteritidis, Escherichia coli, Leuconostoc spp., Latilactobacillus sakei, Serratia liquefaciens, Serratia proteomaculans, Citrobacter freundii, Hafnia alvei, Proteus vulgaris, Pseudomonas fragi, and Brochothrix thermosphacta left to form mono- or dual-species biofilms on stainless steel (SS) coupons. The effectiveness of the photocatalytic disinfectant after 2 h of exposure under UV light on biofilm decontamination was evaluated. The effect of one parameter i.e., exposure to UV or disinfectant, was also determined. According to the obtained results, the microbial load of a mature biofilm depended on the different species or dual species that had adhered to the surface, while the presence of other species could affect the biofilm population of a specific microbe (p < 0.05). The disinfectant strengthened the antimicrobial activity of UV, as, in most cases, the remaining biofilm population was below the detection limit of the method. Moreover, the presence of more than one species affected the resistance of the biofilm cells to UV and the disinfectant (p < 0.05). In conclusion, this study confirms that microbial interactions affected biofilm formation and decontamination, and it demonstrates the effectiveness of the surfactant with the photocatalytic TiO2 agent, suggesting that it could be an alternative agent with which to disinfect contaminated surfaces.

Microbiota profile of filleted gilthead seabream (Sparus aurata) during storage at various conditions by 16S rRNA metabarcoding analysis.

The aim of the present work was to study the microbiota profile of gilthead seabream (Sparus aurata) fillets stored either aerobically or under Modified Atmosphere Packaging (MAP) conditions at 0, 4, 8 and 12 °C, via 16S rRNA metabarcoding sequencing. Throughout storage, sensory assessment was also applied to estimate fillets' end of shelf-life. Results indicated that storage conditions strongly influenced the shelf-life of the fillets, since the sensorial attributes of air-stored samples deteriorated earlier than that of MAP-stored fillets, while higher temperatures also contributed to a more rapid products' end of shelf-life. Metataxonomic analysis indicated that Pseudomonas was by far the dominant genus at the end of fillet's shelf-life, in the vast majority of treatments, even though a sporadic but noteworthy presence of other genera (e.g, Shewanella, Carnobacterium, Brochothrix etc.) at the middle stages of MAP-stored fillets is also worth mentioning. On the other hand, a completely different profile as well as a more abundant bacterial diversity was observed at the end of shelf-life of MAP-stored fillets at 12 °C, in which Serratia was the most dominant bacterium, followed by Kluyvera, Hafnia, Rahnella and Raoultella, while Pseudomonas was detected in traces. The findings of the present work are very important, providing useful information about the spoilage status of gilthead seabream fillets during several storage conditions, triggering in parallel the need for further studies to enrich the current knowledge and help stakeholders develop innovative strategies that delay the growth of key spoiler players and consequently, retard spoilage course.

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.

Microbiota Succession of Whole and Filleted European Sea Bass (Dicentrarchus labrax) during Storage under Aerobic and MAP Conditions via 16S rRNA Gene High-Throughput Sequencing Approach.

In the present work, the profiles of bacterial communities of whole and filleted European sea bass (Dicentrarchus labrax), during several storage temperatures (0, 4, 8 and 12 °C) under aerobic and Modified Atmosphere Packaging (MAP) conditions, were examined via the 16S rRNA High-Throughput Sequencing (HTS) approach. Sensorial attributes were also assessed to determine products' shelf-life. Results indicated that shelf-life was strongly dependent on handling, as well as on temperature and atmosphere conditions. HTS revealed the undisputed dominance of Pseudomonas from the very beginning and throughout storage period in the majority of treatments. However, a slightly different microbiota profile was recorded in MAP-stored fillets at the middle stages of storage, which mainly referred to the sporadic appearance of some bacteria (e.g., Carnobacterium, Shewanella, etc.) that followed the dominance of Pseudomonas. It is noticeable that a major difference was observed at the end of shelf-life of MAP-stored fillets at 12 °C, where the dominant microbiota was constituted by the genus Serratia, while the relative abundance of Pseudomonas and Brochothrix was more limited. Furthermore, at the same temperature under aerobic storage of both whole and filleted fish, Pseudomonas almost co-existed with Acinetobacter, while the presence of both Erwinia and Serratia in whole fish was noteworthy. Overall, the present study provides useful information regarding the storage fate and spoilage status of whole and filleted European sea bass, suggesting that different handling and storage conditions influence the shelf-life of sea bass by favoring or delaying the dominance of Specific Spoilage Organisms (SSOs), affecting in parallel to some extent the formation of their consortium that is responsible for products' sensorial deterioration. Such findings enrich the current knowledge and should be used as a benchmark to develop specific strategies aiming to delay spoilage and thus increase the products' added value.

Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses.

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers.

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: "satisfactory": 4−7 log CFU/g, "acceptable": 7−8 log CFU/g, and "unacceptable": >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

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.

Development and validation of a mathematical model for pseudomonads growth as a basis for predicting spoilage of fresh poultry breast and thigh fillets.

The growth of naturally contaminated pseudomonads on fresh breast and thigh poultry fillets during aerobic storage was studied and modeled as a function of temperature (0-30°C). A statistical comparison of the models for breast and thigh fillets showed that muscle type does not significantly affect the temperature dependence of pseudomonads growth kinetics. A unified model for breast and thigh was developed and validated against pseudomonads growth rate data under isothermal conditions extracted from literature and experimental data under dynamic temperature conditions. The validation results showed a satisfactory performance of the model with the bias and accuracy factors ranging from 0.85 to 1.09 and 1.02 to 1.21, respectively. The model was further used to predict the shelf life of fresh poultry as the time required by pseudomonads to reach the spoilage level for various scenarios of temperature, initial contamination level, and physiological state of pseudomonads demonstrating its application in a risk-based shelf-life assessment of fresh poultry products.

Metagenetic characterization of bacterial communities associated with ready-to-eat leafy vegetables and study of temperature effect on their composition during storage.

Ready-to-eat (RTE) and fresh-cut vegetables meet the current needs for healthy and easy-to-prepare food. However, raw vegetables are widely known to harbor large and diverse bacterial communities promoting spoilage and reducing their shelf-life. A better understanding of their bacterial community and the impact of various environmental factors on its composition is essential to ensure the production of high-quality fresh-cut produce. Therefore, a metagenetic amplicon approach, based on gyrB sequencing, was applied for deciphering the bacterial communities associated with the spoilage of RTE rocket and baby spinach and monitoring the changes occurring in their composition during storage at different temperatures. Our results indicated that Pseudomonas genus was the main spoilage group for both leafy vegetables. Specifically, Pseudomonas viridiflava was dominant in most samples of rocket, while a new Pseudomonas species as well as, Pseudomonas fluorescens and/or Pseudomonas fragi were highly abundant in baby spinach. A significant variability on bacterial species composition among different batches of each vegetable type was observed. In the case of baby spinach, the impact of temperature and/or storage time on bacterial microbiota was not explicitly revealed at batch-level. Concerning rocket, the storage time was the most influential factor resulting in the reduction of Pseudomonas species' abundances and the parallel increase of lactic acid bacteria abundances. The results suggest that a large-scale sampling and further investigation of the various environmental factors shaping the microbiota are needed for gaining deeper knowledge of the diverse bacterial communities on RTE leafy vegetables and thus, enhance the quality of these products.

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.

Labelling Assessment of Greek "Quality Label" Prepacked Cheeses as the Basis for a Branded Food Composition Database.

A labelling assessment study of Greek prepacked "quality label" cheeses was conducted with a view to provide an overview of the whole category. In total, 158 prepacked products belonging to 19 "quality label" cheeses were identified in the Greek market. Among them, Feta had the highest share followed by Kasseri, Graviera Kritis, Kefalograviera and Ladotyri Mitilinis with 81, 16, 15, 11 and 9 products found in the market, respectively. For the rest of the 14 cheeses, the share was limited, ranging from 1 to 4. All labelling indications, nutritional information, claims and other labelling data were recorded and analysed in relation to their compliance against European food law requirements. The results of the analysis showed that for only 6 of the 19 cheeses, all products fully complied with EU labelling legislation. Among the 14 mandatory labelling requirements, the lowest overall compliance was observed for allergens declaration (65%). The analysis of the nutritional data showed a remarkable variability between cheeses and products. Differences in the nutritional characteristics were more pronounced among soft, semi-hard, hard and whey cheese. The above data were entered into an archival database. Application of global harmonisation and standardisation guidelines and tools lead to the initialisation of a branded food composition database (BFCD), conceptualising a specialised database for "quality label" foods.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables.

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.

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.