Effect of temperature and water activity on growth and ochratoxin A production boundaries of two Aspergillus carbonarius isolates on a simulated grape juice medium

Aims: To develop and validate a logistic regression model to predict the growth and ochratoxin A (OTA) production boundaries of two Aspergillus carbonarius isolates on a synthetic grape juice medium as a function of temperature and water activity (aw)

Novel approaches for food safety management and communication

The current safety and quality controls in the food chain are lacking or inadequately applied and fail to prevent microbial and/or chemical contamination of food products, which leads to reduced confidence among consumers. On the other hand to meet market demands food business operators (producers, retailers, resellers) and regulators need to develop and apply structured quality and safety assurance systems based on thorough risk analysis and prevention, through monitoring, recording and controlling of critical parameters covering the entire product's life cycle. However the production, supply, and processing sectors of the food chain are fragmented and this lack of cohesion results in a failure to adopt new and innovative technologies, products and processes. The potential of using information technologies, for example, data storage, communication, cloud, in tandem with data science, for example, data mining, pattern recognition, uncertainty modelling, artificial intelligence, etc., through the whole food chain including processing within the food industry, retailers and even consumers, will provide stakeholders with novel tools regarding the implementation of a more efficient food safety management system

Monitoring the growth of Salmonella enterica serovar typhimurium in silico and in situ with a view in gene expression

In the present study, the ability of S. Typhimurium to develop a biofilm community on rocket tissue was investigated at 20°C. The differences on expression of genes associated with several functional roles during growth of S. Typhimurium on rocket extract and rocket tissue regarding a laboratory growth medium (Luria – Bertani broth, LB) was also monitored. The findings of the present study could show that Salmonella reacts as exposed to different types of stress when inoculated to a heat sterile plant extract and plant tissue. However, further studies are needed to better determine the survival and / or growth of these as “real” biofilm cells on plant tissues

Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools

Recently, rapid, non-invasive analytical methods relying on vibrational spectroscopy and hyper/multispectral imaging, are increasingly gaining popularity in food science. Although such instruments offer a promising alternative to the conventional methods, the analysis of generated data demands complex multidisciplinary approaches based on data analytics tools utilization. Therefore, the objective of this work was to (i) assess the predictive power of different analytical platforms (sensors) coupled with machine learning algorithms in evaluating quality of ready-to-eat (RTE) pineapple (Ananas comosus) and (ii) explore the potentials of The Unscrambler software and the online machine-learning ranking platform, SorfML, in developing the predictive models required by such instruments to assess quality indices. Pineapple samples were stored at 4, 8, 12 °C and dynamic temperatures and were subjected to microbiological (total mesophilic microbial populations, TVC) and sensory analysis (colour, odour, texture) with parallel acquisition of spectral data. Fourier-transform infrared, fluorescence (FLUO) and visible sensors, as well as Videometer instrument were used. For TVC, almost all the combinations of sensors and Partial-least squares regression (PLSR) algorithm from both analytics tools reached values of root mean square error of prediction (RMSE) up to 0.63 log CFU/g, as well as the highest coefficient of determination values (R2). Moreover, Linear Support Vector Machine (SVM Linear) combined with each one of the sensors reached similar performance. For odour, FLUO sensor achieved the highest overall performance, when combined with Partial-least squares discriminant analysis (PLSDA) in both platforms with accuracy close to 85%, but also with values of sensitivity and specificity above 85%. The SVM Linear and MSI combination also achieved similar performance. On the other hand, all models developed for colour and texture showed poor prediction performance. Overall, the use of both analytics tools, resulted in similar trends concerning the feasibility of the different analytical platforms and algorithms on quality evaluation of RTE pineapple

Table olives volatile fingerprints: Potential of an electronic nose for quality discrimination.

In the present work, the potential of an electronic nose to differentiate the quality of fermented green table olives based on their volatile profile was investigated. An electronic gas sensor array system comprising a hybrid sensor array of 12 metal oxide and 10 metal ion-based sensors was used to generate a chemical fingerprint (pattern) of the volatile compounds present in olives. Multivariate statistical analysis and artificial neural networks were applied to the generated patterns to achieve various classification tasks. Green olives were initially classified into three major classes (acceptable, unacceptable, marginal) based on a sensory panel. Multivariate statistical approach showed good discrimination between the class of unacceptable samples and the classes of acceptable and marginal samples. However, in the latter two classes there was a certain area of overlapping in which no clear differentiation could be made. The potential to discriminate green olives in the three selected classes was also evaluated using a multilayer perceptron (MLP) neural network as a classifier with an 18–15–8–3 structure. Results showed good performance of the developed network as only two samples were misclassified in a 66-sample training dataset population, whereas only one case was misclassified in a 12-sample test dataset population. The results of this study provide promising perspectives for the use of a low-cost and rapid system for quality differentiation of fermented green olives based on their volatile profile

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

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

A machine learning strategy in the form of a multilayer perceptron (MLP) neural network was employed to correlate Fourier transform infrared (FTIR) spectral data with beef spoilage during aerobic storage at chill and abuse temperatures. Fresh beef fillets were packaged under aerobic conditions and left to spoil at 0, 5, 10, 15, and 20 °C for up to 350 hours. FTIR spectra were collected directly from the surface of meat samples, whereas total viable counts of bacteria were obtained with standard plating methods. Sensory evaluation was performed during storage and samples were attributed into three quality classes namely fresh, semi-fresh, and spoiled. A neural network was designed to classify beef samples to one of the three quality classes based on the biochemical profile provided by the FTIR spectra, and in parallel to predict the microbial load (as total viable counts) on meat surface. The results obtained demonstrated that the developed neural network was able to classify with high accuracy the beef samples in the corresponding quality class using their FTIR spectra. The network was able to classify correctly 22 out of 24 fresh samples (91.7%), 32 out of 34 spoiled samples (94.1%), and 13 out of 16 semi-fresh samples (81.2%). No fresh sample was misclassified as spoiled and vice versa. The performance of the network in the prediction of microbial counts was based on graphical plots and statistical indices (bias and accuracy factors, standard error of prediction, mean relative and mean absolute percentage residuals). Results demonstrated good correlation of microbial load on beef surface with spectral data. The results of this work indicated that the biochemical fingerprints during beef spoilage obtained by FTIR spectroscopy in combination with the appropriate machine learning strategy have significant potential for rapid assessment of meat spoilage

Evolution of Yeast Consortia during the Fermentation of Kalamata Natural Black Olives upon Two Initial Acidification Treatments

The objective of this study was to elucidate the yeast consortia structure and dynamics during Greek-style processing of Kalamata natural black olives in different brine solutions. Olives were subjected to spontaneous fermentation in 7% (w/v) NaCl brine solution (control treatment) or brine acidified with (a) 0.5% (v/v) vinegar, and (b) 0.1% (v/v) lactic acid at the onset of fermentation. Changes in microbial counts, pH, acidity, organic acids, sugars, and alcohols were analyzed for a period of 187 days. Yeast consortia diversity was evaluated at days 4, 34, 90, 140, and 187 of fermentation. A total of 260 isolates were characterized at sub-species level by rep-PCR genomic fingerprinting with the oligo-nucleotide primer (GTG)5. The characterization of yeast isolates at species level was performed by sequencing of the D1/D2 domain of 26S rRNA gene. Results showed that yeasts dominated the process presenting a relatively broad range of biodiversity composed of 11 genera and 21 species. No lactic acid bacteria (LAB) or Enterobacteriaceae could be enumerated after 20 and 10 days of fermentation, respectively. The dominant yeast species at the beginning were Aureobasidium pullulans for control and vinegar acidification treatments, and Candida naeodendra for lactic acid treatment. Between 34 and 140 days the dominant species were Candida boidinii, Candida molendinolei and Saccharomyces cerevisiae. In the end of fermentation the dominant species in all processes were C. boidinii and C. molendinolei, followed by Pichia manshurica and S. cerevisiae in lactic acid acidification treatment, P. manshurica in vinegar acidification treatment, and Pichia membranifaciens in control fermentation

A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints

A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10, 15, and 20°C) using the dataset presented by Argyri etal. (2010). The performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. The purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. The results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. The PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. The ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. The performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN