Technological and sensorial properties of liquid nitrogen ice cream enriched with protein from brewing waste (trub)

Debittered trub (brewing waste) is an important source of protein source (70.26%). Trub and whey protein were used for 5% protein enrichment of ice cream frozen by liquid nitrogen. Three formulations were elaborated: ice cream standard (ICS), ice cream with whey protein (ICW) and ice cream with trub (ICT). Chemical composition, rheological properties, texture, overrun, melting rate, scanning electron microscopy and a sensorial test were performed. Results showed that ICT presented a higher viscosity, obtained on the upward curve up to 6.76 Pa s⁻¹, consistency index (22.96 (Pa s⁻¹)ⁿ), hysteresis area (140.40 mPa s⁻¹) and hardness (31113.33 g) but a lower melting rate (0.38 g min⁻¹), overrun (13.92%) and sensorial acceptability than the other formulations. The addition of trub debittered for protein enrichment improved ice cream properties and demonstrated that it could be used as a food ingredient.     

Interdiffusion inα solid solutions of the Cu-Zn-Sn system

The interdiffusion coefficients in the f c c phase of Cu-Zn-Sn alloys, , have been determined at 1073 K. The concentration profiles indicate that the diffusion rate of tin is greater than that of zinc in the Cu-Zn-Sn alloy. The diffusion paths show the typical S-shaped curves. All of the four interdiffusion coefficients are positive and they are very sensitive to the solute concentration. The atomic mobilities of the three diffusing elements in Kirkendall planes increase in the order of Cu, Zn, Sn. The interaction energy of the Cu-Sn bond is much larger than that of the Zn-Sn bond. From the results of the present work it seems that the Onsager reciprocal relation holds in the a phase of the Cu-Zn-Sn system.     

Synthesis of cationic flocculant by radiation-induced copolymerization of methyl chloride salt of N,N-dimethylaminoethyl methacrylate with acrylamide in aqueous solution

The radiation-induced copolymerization of the methyl chloride salt of N,N-dimethylaminoethyl methacrylate (DMAEM·MC) with acrylamide (AAm) was used to prepare a cationic polymer flocculant. The polymerization rate increased with increasing dose rate, polymerization temperature, monomer concentration and mole fraction of AAm in the monomer mixture. The molecular weight of the copolymer was also found to increase with monomer concentration and mole fraction of AAm, but at high concentration and fraction of AAm, intermolecular crosslinking tends to occur during the polymerization to form water-insoluble copolymer. A water-soluble copolymer having various molecular weights and cationic strengths can be synthesized by selecting suitable reaction conditions; i.e., this radiation process can provide a much higher molecular weight copolymer with a wide range of cationic strength. The flocculation effect was evaluated using sludge from wastewater of sugar manufacture. It was found that the radiation-polymerized copolymer DMAEM·MC–AAm has an excellent flocculation effect.     

Determination of γ-glutamyl-valyl-glycine in raw scallop and processed scallop products using high pressure liquid chromatography–tandem mass spectrometry

The determination of the kokumi peptide, γ-glutamyl-valyl-glycine (γ-Glu-Val-Gly) in raw scallop and processed scallop products was carried out using high pressure liquid chromatography–tandem mass spectrometry (LC/MS/MS). The detection of γ-Glu-Val-Gly was achieved using a multiple reaction monitoring (MRM) method. The optimised condition enabled the precise determination of γ-Glu-Val-Gly. Raw scallop contained 0.08 μg/g γ-Glu-Val-Gly, and the γ-Glu-Val-Gly levels in processed scallop products, such as dried-scallop and scallop extract, were measured to be 0.64 and 0.77 μg/g, respectively. This is the first report to confirm the existence of γ-Glu-Val-Gly in foodstuff.     

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.     

Boroxine Nanotubes: Moisture‐Sensitive Morphological Transformation and Guest Release

Boroxines, (R‐BO)₃, which can be easily synthesized via a dehydration reaction of boronic acids, R–B(OH)₂, selectively self‐assemble in toluene into nanofibers, nanorods, nanotapes, and nanotubes, depending on the aromatic substituent (R). Spectroscopic measurements show that the nanotube consists of a J‐aggregate of the boroxine. Humidification converts the morphology from the nanotube to a sheet as a result of the hydrolysis of the boroxine components and subsequent molecular‐packing rearrangement from the J‐aggregate to an H‐aggregate. Such a transformation leads to the compulsive release of guest molecules encapsulated in the hollow cylinder of the nanotube. The hydrolysis and the molecular‐packing rearrangement described above are suppressed by coordination of pyridine to the boron atom, with the resulting moiety acting as a Lewis acid of the boroxine component. The pyridine‐coordinated nanotube is transformed into a helical coil by humidification. Guest release during the nanotube‐to‐helical‐coil transformation is much slower than during the nanotube‐to‐sheet transformation, but faster than from a nanotube that did not undergo morphological transformation. The storage and release of guest molecules from the boroxine nanotubes can be precisely controlled by adjusting the moisture level and the concentration of Lewis bases, such as amines.     

Beneficial effect of carbon–PVA colloid additives for lead–acid batteries

Based on the previous success of adding ultra-fine carbon–PVA (polyvinyl alcohol) colloid solution to restore performance of weak batteries, a large scale test programme of practical batteries including those used in forklift, golf cart, taxi cab, truck and bus applications from 150 companies was carried out. More than 95% of the tested batteries exhibited excellent recovery in performance parameters including the specific gravity (S.G.), voltage and operation time (Ah capacity). The work reported here includes basic research studies to understand better the beneficial action of the carbon–PVA colloid additive.     

Microstructures and Vickers hardness of rapidly solidified Al-Cu alloys near the Al-Al2Cu equilibrium eutectic composition

Al-Cu alloys near the Al-Al₂Cu equilibrium eutectic composition were rapidly solidified by the single-roller method. Two-zone structures were observed in transverse sections of Al-24.8, 30.0, 32.7, 33.2, 37.0 and 40.3 mass % Cu alloy ribbons, which were interpreted on the basis of the difference in cooling rates and of the temperature gradients between the chilled and free-surface sides of the ribbons. Non-equilibrium eutectic structures, cellular and irregular lamellar, were seen at the chilled side because the alloy liquids were directionally solidified with coupled eutectic growth. The coupled zone of the Al-Cu alloy expanded over the composition range 24.8–40.3 mass % Cu by rapid solidification. Dendritic structures were formed at the unchilled side of Al-24.8, 30.0, 37.0 and 40.3 mass % Cu alloys. Vickers hardness values at the chilled side differed from those at the unchilled side due to the difference in the microstructure.