Maiorca wheat malt: A comprehensive analysis of physicochemical properties, volatile compounds, and sensory evaluation in brewing process and final product quality

This study explores the potential of Maiorca wheat malt as an alternative ingredient in beer production, investigating its impact on the brewing process and beer quality at different recipe contents (50 %, 75 %, 100 %). The study encompasses a comprehensive analysis of key malt parameters, revealing Maiorca malt's positive influence on maltose, glucose, filterability, extract, free amino nitrogen, and fermentability. Notably, the malt exhibited heightened levels of α-amylase and β-amylase enzymes compared to conventional commercial malt. Furthermore, the analysis of aroma compounds and subsequent sensory evaluations unveiled a significant correlation between the proportion of Maiorca malt in the formulation and intensified estery, fruity, malty, honey, complemented by a reduction in attributes such as aromatic compounds, phenolic, yeasty, sulfury, oxidized, and solvent-like odors. This research underscores the favorable contribution of Maiorca wheat malt to enhancing both the brewing process and final beer quality, highlighting its potential as an innovative ingredient in brewing practices.

Mathematical modeling and optimization of pasteurization for the internal pressure and physical quality of canned beer

Globally, beer is the most popular alcoholic beverage. To accomplish microbial stabilization and extend the shelf life of beer, it is typically subjected to in-package pasteurization using a tunnel pasteurizer. However, high internal pressure can cause can bulging during pasteurization, leading to significant product loss. In this study, an empirical mathematical model was constructed to describe the effects of can thickness (0.245-0.270 mm), fill volume (320-338 mL), carbon dioxide content (5.70-6.10 g/L), and pasteurization temperature (59-66 °C) on the internal pressure inside canned beer. A laboratory-scale pasteurization setup was used to pasteurize samples based on the worst-case scenario of commercial pasteurization. The mathematical model (R2 = 0.90) showed that all parameters significantly influenced the internal pressure of pasteurized canned beer (p < 0.05). Additionally, the physical, chemical, and biological properties of pasteurized canned beer were assessed. All values fell within an acceptable range of industrial standards. A simplified 2nd-order polynomial equation (R2 = 0.90) was created and verified for industrial use. The data are well represented by the simplified model, which suggests that it could be used for optimization of product- and process parameters to reduce the occurrence of can bulging in commercial pasteurization of canned beer.

Wheat craft beer made from AFB1-contaminated wheat malt contains detectable mycotoxins, retains quality attributes, but differs in some fermentation metabolites

Levels of aflatoxin B1 (AFB1) were measured during the production of wheat craft beer made with wheat malt contaminated with AFB1 (1.23 µg/kg). A wheat craft beer made with non-contaminated wheat malt was produced for comparison purposes. AFB1 was measured after mashing (malt after the mashing process), and in spent grain (spent grains are filtered to collect the wort - remaining sugar-rich liquid), sweet wort, green beer, spent yeast, and in beer. Physicochemical parameters (pH, titratable acidity, color parameters, total soluble solids), sugars, organic acids, alcohols, and phenolics were evaluated after mashing, and in sweet wort, green beer, and beer samples. Density and yeast counts were determined over 120 h of sweet wort fermentation every 24 h. The AFB1 levels in the final beer were 0.22 µg/L, while the spent grains and spent yeasts contained 0.71 ± 0.17 and 0.11 ± 0.03 µg/kg of AFB1, respectively. AFB1 contamination did not influence the final product's physicochemical parameters, density during fermentation, fructose, or glycerol content. Higher yeast counts were observed during the first 48 h of non-contaminated wheat craft beer fermentation, with higher ethanol, citric acid, and propionic acid contents and lower glucose, malic acid, and lactic acid contents compared with beer contaminated with AFB1. Non-contaminated wheat craft beer also had higher concentrations of gallic acid, chlorogenic acid, catechin, procyanidin A2, and procyanidin B1. AFB1 contamination of wheat malt may not affect basic quality parameters in wheat craft beer but can influence the final product's organic acid and phenolic contents. Our findings show that if wheat craft beer is made with contaminated malt, AFB1 can remain in the final product and may pose a risk to consumers.

Evaluation of craft beers through the direct determination of amino acids by capillary electrophoresis and principal component analysis

The composition of beer wort in terms of amino acid (AA) content affects the final product quality, once it is related to the vitality of yeast during the initial exponential growth phase and throughout fermentation. The objective of this work was the use of a capillary zone electrophoresis method with UV-vis detection in association with Principal Component (Data) Analysis for craft beer classification. Cysteine, histidine, phenylalanine, lysine, tryptophan and arginine were the monitored AAs in wort and finished beer, which were extracted through cation exchange resin. Good differentiation among samples according to their production was obtained, showing a profile of AAs (<LQ-99 µg mL^-1) for each wort, which can be used as indicator of the quality of a beer. One of the samples had the mashing step monitored, showing an increasing profile in the concentrations of AAs (6.5-55 µg mL^-1), which can be explained by the protein cleavage.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality.