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

Ιn Vitro screening of γ-Aminobutyric Acid and Autoinducer-2 signalling in lactic acid bacteria exhibiting probiotic potential isolated from natural black conservolea olives

In the present study, 33 strains of lactic acid bacteria (LAB) previously isolated from natural black Conservolea olives were assessed for their probiotic potential in vitro, as well as for their autoinducer-2 (AI-2) activity under standard growth conditions and the production of γ-aminobutyric acid (GABA). The probiotic tests included the in vitro resistance to low pH and resistance to bile salts, the evaluation of bile salt hydrolase activity, as well as safety tests regarding their possible haemolytic activity and their antimicrobial activity against pathogens. The results indicated that 17 strains were able to survive in low pH and in the presence of bile, with 15 of them also exhibiting partial bile salt hydrolase activity. None of the strains exhibited haemolytic activity or inhibited the growth of any of the examined pathogens. Moreover, the strains displayed generally low AI-2 activity under the growth conditions tested, regardless of the species. Interestingly, in contrast to what has been found in most foods, none of the isolates were found to produce GABA after 48 h of growth. The results from the AI-2 activity and extracellular GABA detection were considered as unexpected for LAB with probiotic attributes

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, “MeatReg”, a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg” was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC–MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: http://elvis.misc.cranfield.ac.uk/SORF/

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

Pulmonary sarcoidosis associated with psoriasis vulgaris: coincidental occurrence or causal association? Case report

BACKGROUND: Sarcoidosis is rarely associated with a distinct disease. One disease infrequently associated with sarcoidosis is psoriasis. CASE PRESENTATION: This case study describes a 38-year-old male, who presented with chest pain, high-grade fever, arthralgias and a skin rash accompanied by bilateral hilar lymphadenopathy on his chest radiograph. Extensive investigations including fiber-optic bronchoscopy with bronchoalveolar lavage and labial and skin biopsies, demonstrated that two distinct clinical entities co-existed in the same patient: pulmonary sarcoidosis and psoriasis vulgaris. Combination therapy for both diseases was applied and the patient was greatly improved. CONCLUSION: This is the first well-documented case of sarcoidosis and psoriasis in the same patient, reported on the basis of safe and widely-used techniques that were not available until fairly recently. These disorders might share common pathogenic mechanisms that could explain their co-existence in the patient

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