In vitro digestibility of phenolic compounds from edible fruits: could it be explained by chemometrics?

The health benefits of phenolic compounds depend on the ingested amount, molecular diversity and gastrointestinal digestibility. The phenolic profile of eight fruits (blackberry, blueberry, strawberry, raspberry, mulberry, pomegranate, green and red globe grapes) was chemometrically associated with their in vitro digestibility (oral, gastric, intestinal). Extractable phenols, flavonoids and anthocyanins strongly correlated with each other (r ≥ 0.84), proanthocyanidins with anthocyanins (r = 0.62) and hydrolysable phenols with both extractable phenols (r = 0.45) and proanthocyanidins (r = ?0.54). Two principal components explained 93% of the variance [61% (free‐phenols), 32% (bounded‐phenols)], and four clusters were confirmed by hierarchical analysis, based in their phenolic richness (CLT 1‐4: low to high) and molecular diversity. In vitro digestibility of extractable phenols and flavonoids was blackberry (CLT‐4)> raspberry (CLT‐2)> red grape (CLT‐1) related to their phenolic richness (r ≥ 0.96; P < 0.001), but anthocyanins’ digestibility was pH‐dependent. Chemometrics is useful to predict the in vitro digestibility of phenolic compounds in the assayed fruits.     

Enhancement effect on apparent viscosity of aqueous tragacanth gum dispersions promoted by sugars

Flow curves of aqueous dispersions of tragacanth gum (T) with sucrose and glucose at different temperatures were determined using a controlled‐stress rheometer. The effect of sodium chloride without or with sucrose (at the highest content) on the rheology of T dispersions was evaluated. The presence of sucrose and glucose promoted a noticeable enhancement impact on the apparent viscosity of aqueous T dispersions, which depended on sugar type/content, shear rate and temperature. In all cases, the glucose addition led to the largest enhanced viscosities at low shear rates (<10 s^?1) and temperature. The joint action of sugar and salt exhibited a notable effect on apparent viscosity at low shear rates, softening the strong shear‐thinning behaviour of T samples. Flow curves of T in the presence of sugars were satisfactorily described by the Cross‐Williamson model, being semi‐empirical correlations of the model parameters with ingredients content and temperature stablished.     

The Effect of Hybrid Carrageenan on the Thermo-rheological Properties of Gluten-Free Flour Doughs Using a Modified Kneading Protocol

Thermo-rheological behaviour of chestnut flour doughs supplemented with kappa/iota-hybrid carrageenan (HC) (up to 2.0%, flour basis (f.b.)) and sodium chloride (1.8%, f.b.) was determined at both target (C1) and final (C5) mixing peaks. For this purpose, small amplitude oscillatory shear (0.01 to 100 Hz), creep–recovery (loading of 50 Pa for 60 s, 30 °C), temperature sweeps (from 30 up to 180 °C) and heating/cooling cycles (between 30 and 60 °C) were conducted on a controlled stress rheometer. Previously, the thermal-mixing behaviour at proposed mixing temperature (50 °C) was conducted on Mixolab® apparatus. Results showed that the dough stability (from 2.2 to 5.8 min) in mixing stage and starch heat resistance to dough processing were significantly improved at proposed mixing temperature, even in the absence of HC. No statistical differences in rheological properties were observed for doughs evaluated in C1; however, those analysed in C5 were significantly modified in the presence of HC, mainly in terms of viscous behaviour (from 52.1 × 10⁶ to 39.1 × 10⁶ Pa s). Creep–recovery data sets, successfully fitted using Burgers model, revealed that the elasticity (J _r/J _max from 73.3 to 87.6%) of doughs analysed in C5 improved with HC addition. Thermal tests showed that the starch transitions were significantly promoted and stabilized with HC addition.     

Predicting bull fertility using genomic data and biological information

The genomic prediction of unobserved genetic values or future phenotypes for complex traits has revolutionized agriculture and human medicine. Fertility traits are undoubtedly complex traits of great economic importance to the dairy industry. Although genomic prediction for improved cow fertility has received much attention, bull fertility largely has been ignored. The first aim of this study was to investigate the feasibility of genomic prediction of sire conception rate (SCR) in US Holstein dairy cattle. Standard genomic prediction often ignores any available information about functional features of the genome, although it is believed that such information can yield more accurate and more persistent predictions. Hence, the second objective was to incorporate prior biological information into predictive models and evaluate their performance. The analyses included the use of kernel-based models fitting either all single nucleotide polymorphisms (SNP; 55K) or only markers with presumed functional roles, such as SNP linked to Gene Ontology or Medical Subject Heading terms related to male fertility, or SNP significantly associated with SCR. Both single- and multikernel models were evaluated using linear and Gaussian kernels. Predictive ability was evaluated in 5-fold cross-validation. The entire set of SNP exhibited predictive correlations around 0.35. Neither Gene Ontology nor Medical Subject Heading gene sets achieved predictive abilities higher than their counterparts using random sets of SNP. Notably, kernel models fitting significant SNP achieved the best performance with increases in accuracy up to 5% compared with the standard whole-genome approach. Models fitting Gaussian kernels outperformed their counterparts fitting linear kernels irrespective of the set of SNP. Overall, our findings suggest that genomic prediction of bull fertility is feasible in dairy cattle. This provides potential for accurate genome-guided decisions, such as early culling of bull calves with low SCR predictions. In addition, exploiting nonlinear effects through the use of Gaussian kernels together with the incorporation of relevant markers seems to be a promising alternative to the standard approach. The inclusion of gene set results into prediction models deserves further research.     

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.     

Advances in Wine Aging Technologies for Enhancing Wine Quality and Accelerating Wine Aging Process

Wine aging is an important process to produce high-quality wines. Traditionally, wines are aged in oak barrel aging systems. However, due to the disadvantages of the traditional aging technology, such as lengthy time needed, high cost, etc., innovative aging technologies have been developed. These technologies involve aging wines using wood fragments, application of micro-oxygenation, aging on lees, or application of some physical methods. Moreover, wine bottling can be regarded as the second phase of wine aging and is essential for most wines. Each technology can benefit the aging process from different aspects. Traditional oak barrel aging technology is the oldest and widely accepted technology. The application of wood fragments and physical methods are promising in accelerating aging process artificially, while application of micro-oxygenation and lees is reliable to improve wine quality. This paper reviews recent developments of the wine aging technologies. The impacts of operational parameters of each technology on wine quality during aging are analyzed, and comparisons among these aging technologies are made. In addition, several strategies to produce high-quality wines in a short aging period are also proposed.