Extraction/oxidation kinetics of low molecular weight compounds in wine brandy resulting from different ageing technologies

This study provides innovative information on the influence of new technologies of ageing (stainless steel tanks with wood staves or wood tablets of chestnut or Limousin oak), in comparison with traditional technology (oak wooden barrels), on the extraction/oxidation kinetics of low molecular weight compounds of wine brandy. The brandy was sampled and analysed by HPLC during the first year of ageing. The results show that most of the compounds tend to increase over the time, but their extraction/oxidation kinetics depend on the ageing technology. The wooden barrels promote greater enrichment in the majority of the compounds. However, gallic acid, ellagic acid and syringaldehyde, and vanillin and 5-methylfurfural, which are strong antioxidants and key-odourant compounds, respectively, present higher contents in the brandy aged with the alternative technologies. Chestnut proves to be a suitable alternative to Limousin oak for the ageing of brandy in all the studied technologies, inducing faster evolution and high quality.     

Comparison between free radical scavenging capacity and oxidative stability of nut oils

Several works have measured free radical scavenging capacity of nut oils, since they may become a significant source of dietary fat. However, they have not considered kinetic parameters, what was the first aim of this work. Also, it was studied the possible relation between values of free radical scavenging capacity DPPH and oxidative stability (Rancimat method) in different nut (hazelnut, peanut, pistachio, walnut and almond) oils. The ranking of antioxidant capacity of nut oils, by both assays, was: pistachio > hazelnut > walnut > almond > peanut. A significant correlation was found between DPPH and Rancimat methods assays. Tocopherols appear to be the responsible compounds of this antioxidant capacity being neglictible the contribution of polyphenols. An interference effect of phospholipids, present in methanolic fraction of nut oils, was observed in the determination of polyphenols in nut oils by Folin and ortho-diphenols assays.     

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.