Identification and functional traits of lactic acid bacteria isolated from Ciauscolo salami produced in Central Italy

Lactic acid bacteria (LAB) from Ciauscolo salami produced in Marche Region of Central Italy, and LAB strains belonging to our laboratory collection were examined for their capability to survive at low pH and bile, to adhere to Caco-2 cells, and for antibiotic resistance. LAB from Ciauscolo were identified by ARDRA and RAPD-PCR. Our study showed that all LAB strains had good adaptation to gastric juice and moderate tolerance to bile. The adhesiveness was variable among strains but significantly lower in LAB from food. Antibiotic resistance was broadly spread among food strains, with level of resistance exceeding 15% for all the antibiotics tested. The resistance determinants erm(B) and tet(M) were found in nine strains of food origin (21.4%) while tet(L) in one strain of our collection (5%). Our work suggests that fermented foods are valuable sources of bacterial strains with functional traits of intestinal lactobacilli. These bacteria may be further studied for their use in probiotic applications.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Chemical composition,sensory and cooking quality evaluation of durum wheat spaghetti enriched with pea flour

The goal of the study was to obtain spaghetti enriched with pea flour with sensory properties close to conventional pasta. To this aim, the study has been organised in two subsequent trials. In the first, the pea flour amount added to the spaghetti was continuously increased until the overall sensory quality reached its threshold value (pea flour concentration = 15%). In fact, the spaghetti samples supplemented with 15% pea flour (15%S‐P) showed less elasticity, unpleasant colour and higher firmness compared to the control sample (CTRL). The second step was aimed to improve the overall sensory quality of the 15%S‐P spaghetti by means of guar gum (GUAR). The sample with GUAR (15%S‐P/GUAR) showed a pleasant colour, odour and taste. Furthermore, the 15%S‐P/GUAR sample recorded a low starch digestibility value (i.e. 54) and a higher soluble fibre content with respect to the 15%S‐P sample.     

Fresh-Cut Kiwifruit Structure and Firmness as Affected by Heat Pre-treatments and Post-cut Calcium Dips

The effect of heat pre-treatment (45 °C/25 min), applied to whole fruit, and post-cut calcium dips in 1 and 2 g/L CaCl₂ solutions on respiration rate and texture preservation of kiwifruit slices was studied. During a 9-day period, packages’ atmosphere composition, slices’ firmness, pectin content and sensory scores were evaluated. Histological observations of samples were also performed. Neither heat pre-treatment nor calcium dips alone were effective in diminishing respiration rate of the slices but the application of both treatments revealed a synergistic effect on respiration rate reduction. Calcium-dipped fruit slices showed better firmness preservation. Post-cut calcium dips, alone or combined with heat pre-treatments, yielded slices presenting higher insoluble/total galacturonic acid ratio, indicating formation of calcium pectates. Slices from heat-pre-treated fruits, even in the absence of calcium, also revealed insoluble/total galacturonic acid ratio similar to calcium-treated slices, supporting, despite the lower firmness value, a more structured tissue, as observed in SEM microphotographs. Sensory and physical–chemical parameters obtained were correlated. The effectiveness of calcium treatment was equally observed for both tested concentrations during a 9-day shelf life period.     

Essential oils: their antibacterial properties and potential applications in foods--a review

In vitro studies have demonstrated antibacterial activity of essential oils (EOs) against Listeria monocytogenes, Salmonella typhimurium, Escherichia coli O157:H7, Shigella dysenteria, Bacillus cereus and Staphylococcus aureus at levels between 0.2 and 10 microl ml(-1). Gram-negative organisms are slightly less susceptible than gram-positive bacteria. A number of EO components has been identified as effective antibacterials, e.g. carvacrol, thymol, eugenol, perillaldehyde, cinnamaldehyde and cinnamic acid, having minimum inhibitory concentrations (MICs) of 0.05-5 microl ml(-1) in vitro. A higher concentration is needed to achieve the same effect in foods. Studies with fresh meat, meat products, fish, milk, dairy products, vegetables, fruit and cooked rice have shown that the concentration needed to achieve a significant antibacterial effect is around 0.5-20 microl g(-1) in foods and about 0.1-10 microl ml(-1) in solutions for washing fruit and vegetables. EOs comprise a large number of components and it is likely that their mode of action involves several targets in the bacterial cell. The hydrophobicity of EOs enables them to partition in the lipids of the cell membrane and mitochondria, rendering them permeable and leading to leakage of cell contents. Physical conditions that improve the action of EOs are low pH, low temperature and low oxygen levels. Synergism has been observed between carvacrol and its precursor p-cymene and between cinnamaldehyde and eugenol. Synergy between EO components and mild preservation methods has also been observed. Some EO components are legally registered flavourings in the EU and the USA. Undesirable organoleptic effects can be limited by careful selection of EOs according to the type of food.     

Evaluation of freshness decay of minced beef stored in high-oxygen modified atmosphere packaged at different temperatures using NIR and MIR spectroscopy

Meat freshness has been monitored by various microbiological, chemical and sensorial indices. However, these methods are slow and not suited to automation. Infrared spectroscopy is one of the most convenient analytical tools which could be used to monitor the evolution of food quality. The aim of this work was to investigate the ability of both NIR (Near Infrared) and MIR (Mid Infrared) spectroscopy to follow meat freshness decay. The minced beef was packaged in high-oxygen modified atmosphere (30% CO₂ and 70% O₂) and stored at three temperatures. Spectra were collected by Fourier-Transformation (FT)-NIR and FT-IR instruments. PCA, applied to the data, was able to discriminate samples on the basis of storage time and temperature. The modelling of PC scores versus time allowed the setting of the time of initial freshness decay for the samples (6–7 days at 4.3 °C, 2–3 days at 8.1 °C and less than 1 day at 15.5 °C).     

Effect of fermentation temperature on microbial population evolution using culture-independent and dependent techniques

The population dynamics of micro-organisms during grape-must fermentation has been thoroughly studied. However, the main approach has relied on microbiological methods based on plating. This approach may overlook micro-organisms that (i) grow slowly or do not grow well on artificial media or (ii) whose population size is small enough to be detected by regular sampling. Culture-independent methods have been used and compared with the traditional plating method during wine fermentations performed at two different temperatures (13 °C and 25 °C). These methods include a qualitative technique, the DGGE; a semi-quantitative technique, the direct cloning of amplified DNA; and a quantitative technique, the QPCR. The biodiversity observed in the must and at the beginning of fermentation was much higher when DGGE or direct cloning were used. Quantification of the most frequent non-Saccharomyces yeast, Hanseniaspora uvarum and Candida zemplinina, showed that they survived throughout the fermentation process and, specifically, it revealed the quantitatively relevant presence of C. zemplinina until the end of fermentation.     

Rapid real-time PCR detection of Listeria monocytogenes in enriched food samples based on the ssrA gene, a novel diagnostic target

A real-time PCR assay was designed to detect a 162-bp fragment of the ssrA gene in Listeria monocytogenes. The specificity of the assay for L. monocytogenes was confirmed against a panel of 6 Listeria species and 26 other bacterial species. A detection limit of 1-10 genome equivalents was determined for the assay. Application of the assay in natural and artificially contaminated culture enriched foods, including soft cheese, meat, milk, vegetables and fish, enabled detection of 1-5 CFU L. monocytogenes per 25g/ml of food sample in 30h. The performance of the assay was compared with the Roche Diagnostics 'LightCycler foodproof Listeria monocytogenes Detection Kit'. Both methods detected L. monocytogenes in all artificially contaminated retail samples (n=27) and L. monocytogenes was not detected by either system in 27 natural retail food samples. The method developed in this study has the potential to enable the specific detection of L. monocytogenes in a variety of food types in a time-frame considerably faster than current standard methods. The potential of the ssrA gene as a nucleic acid diagnostic (NAD) target has been demonstrated in L. monocytogenes. We are currently developing NAD tests based on the ssrA gene for a range of common foodborne and clinically relevant bacterial pathogens.     

Anaerobic oxidation of methane: mechanisms, bioenergetics, and the ecology of associated microorganisms

Microbially mediated anaerobic oxidation of methane (AOM) moderates the input of methane, an important greenhouse gas, to the atmosphere by consuming methane produced in various marine, terrestrial, and subsurface environments. AOM coupled to sulfate reduction has been most extensively studied because of the abundance of sulfate in marine systems, but electron acceptors otherthan sulfate are more energetically favorable. Phylogenetic trees based on 16S rRNA gene clone libraries derived from microbial communities where AOM occurs show evidence of diverse, methanotrophic archaea (ANME) closely associated with sulfate-reducing bacteria, but these organisms have not yet been isolated as pure cultures. Several biochemical pathways for AOM have been proposed, including reverse methanogenesis, acetogenesis, and methylogenesis, and both culture-dependent and independent techniques have provided some clues to howthese communities function. Still, questions remain regarding the diversity, physiology, and metabolic restrictions of AOM-related organisms.