Volatile fingerprinting differentiates diverse-aged craft beers

An Overview of the Application of Multivariate Analysis to the Evaluation of Beer Sensory Quality and Shelf-Life Stability

Achieving beer quality and stability remains the main challenge for the brewing industry. Despite all the technologies available, to obtain a high-quality product, it is important to know and control every step of the beer production process. Since the process has an impact on the quality and stability of the final product, it is important to create mechanisms that help manage and monitor the beer production and aging processes. Multivariate statistical techniques (chemometrics) can be a very useful tool for this purpose, as they facilitate the extraction and interpretation of information from brewing datasets by managing the connections between different types of data with multiple variables. In addition, chemometrics could help to better understand the process and the quality of the product during its shelf life. This review discusses the basis of beer quality and stability and focuses on how chemometrics can be used to monitor and manage the beer quality parameters during the beer production and aging processes.

Impact of Non-Saccharomyces Wine Yeast Strains on Improving Healthy Characteristics and the Sensory Profile of Beer in Sequential Fermentation

The use of non-Saccharomyces yeasts in brewing is a useful tool for developing new products to meet the growing consumer demand for innovative products. Non-Saccharomyces yeasts can be used both in single and in mixed fermentations with Saccharomyces cerevisiae, as they are able to improve the sensory profile of beers, and they can be used to obtain functional beers (with a low ethanol content and melatonin production). The aim of this study was to evaluate this capacity in eight non-Saccharomyces strains isolated from Madrid agriculture. For this purpose, single fermentations were carried out with non-Saccharomyces strains and sequential fermentations with non-Saccharomyces and the commercial strain SafAle S-04. The Wickerhamomyces anomalus strain CLI 1028 was selected in pure culture for brewing beer with a low ethanol content (1.25% (v/v)) for its fruity and phenolic flavours and the absence of wort flavours. The best-evaluated strains in sequential fermentation were CLI 3 (Hanseniaspora vineae) and CLI 457 (Metschnikowia pulcherrima), due to their fruity notes as well as their superior bitterness, body, and balance. Volatile compounds and melatonin production were analysed by GC and HPLC, respectively. The beers were sensory-analysed by a trained panel. The results of the study show the potential of non-Saccharomyces strains in the production of low-alcohol beers, and as a flavour enhancement in sequential fermentation.

Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method

In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure. Towards this goal, the flavor compounds in beer from existing literature were collected. The database was classified into four groups as aromatic, bitter, sulfury, and others. The RI values on a non-polar SE-30 column and a polar Carbowax 20M column from the National Institute of Standards Technology (NIST) were investigated. The structures were converted to molecular descriptors calculated by molecular operating environment (MOE), ChemoPy and Mordred, respectively. By combining the pretreatment of the descriptors, machine learning models, including support vector machine (SVM), random forest (RF) and k-nearest neighbour (kNN) were utilized for beer flavor models. Principal component regression (PCR), random forest regression (RFR) and partial least squares (PLS) regression were employed to predict the RI. The accuracy of the test set was obtained by SVM, RF, and kNN. Among them, the combination of descriptors calculated by Mordred and RF model afforded the highest accuracy of 0.686. R² of the optimal regression model achieved 0.96. The results indicated that the models can be used to predict the flavor of a specific compound in beer and its RI value.

In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure.

Quality Characteristics of Wheat Malts with Different Country of Origin and Their Effect on Beer Brewing

The characteristics of wheat malt, wort, and beer from two Korean wheat varieties (Keumkang and Anzunbaengi; KM and AM, respectively) were compared to those of two commercial wheat malt controls from the USA and Germany (UM and GM), to examine the feasibility of Korean wheat for brewing. The quality parameters of four wheat malts, chemical properties of wort using them, volatile compounds, sensory attributes, and consumer acceptability of four final beers (KB, AB, UB, and GB) were analysed. Additionally, the relationship of each parameter was determined through multiple factor analysis. Korean wheat malts were different from control malts in free amino nitrogen (FAN), soluble and total nitrogen, and Kolbach index. The extract (81.8–83.2%) and diastatic power (407–477 WK°) of all samples were in the brewing field’s recommended range. The FAN and yeast cell population of the wort samples were similar during fermentation except on the initial day, which showed a high FAN and yeast cell population for KM wort. Eleven volatile compounds with variable importance in projection scores above 1.0 were responsible for discrimination of the beer samples by partial least squares discriminant analysis. The four beer samples’ overall acceptance scores were not significantly different. However, their acceptance trends were different depending on consumer preference segments by agglomerative hierarchical clustering analysis, implying the need for product development depending on the target consumer. Regarding sensory attributes, KB had a less fruity flavour than the other beer samples ( $p<0.05$ ) and sweetness of AB tended to be higher than the others. An association plot based on multiple factor analysis of the wheat beer samples, volatile compounds, sensory properties, and overall acceptance showed that sweetness, alcohol aroma, and fruity flavour were closely associated with AB and overall acceptance, while 2,3-butanediol and turbidity were placed opposite side of there.

Characterization of iron walnut in different regions of China based on phytochemical composition

Little is known about the phytochemical composition of iron walnuts. Differences in the geographical origin of iron walnuts associated with economic benefits should also be examined. In this study, the phytochemical composition (fatty acids, Vitamin E, total polyphenols and flavonoids, amino acids, and minerals) of iron walnuts in China was investigated. The results showed that there were significant differences (p < 0.05) in the phytochemical composition of iron walnut oils and flours from different regions. Positive (r > 0.5, p < 0.05) and negative (r < - 0.5, p < 0.05) correlations were found between amino acids/minerals and amino acids/oleic acid, with the highest correlation coefficient (r = 0.742, p < 0.05) between Cu and tyrosine. In addition, based on the 12 phytochemical fingerprints selected by random forest, a geographical-origin identification model for iron walnuts was established, with a corresponding correct classification rate of 96.6%. The top three phytochemical fingerprints for the geographical-origin identification of iron walnut were microelements, macroelements, and antioxidant composition, with contribution rates of 61.7%, 18.1%, and 9.9%, respectively.

Evaluation of multi-starter S. cerevisiae/ D. bruxellensis cultures for mimicking and accelerating transformations occurring during barrel ageing of beer

During beer ageing, endogenous barrel microbes grow spontaneously and transform wort/beer composition, being Dekkera bruxellensis and Saccharomyces cerevisiae among the main contributors to the chemical and sensory profile of aged beer. This work aims at the application of multi-starter cultures to mimic and accelerate biological modifications occurring during barrel ageing of beer, in controlled fermentation processes. Co-cultures of D.bruxellensis/S.cerevisiae were conducted under conditions commonly found in barrel aged beer production: different pitching rates, high glucose concentration and presence of ethanol and wood extracts. Selective pressures and competition between yeasts influenced microbial growth and metabolite production, namely ethanol, acetic acid and target volatile compounds (esters, alcohols, terpenols, volatile acids and volatile phenols). Metabolic profiles of co-cultures combined traits of both species, and differed from those of pure cultures. Lastly, multi-starters were successfully applied in combination with wood in a controlled and accelerated fermentation process for mimicking barrel ageing transformations.

Development of Head Space Sorptive Extraction Method for the Determination of Volatile Compounds in Beer and Comparison with Stir Bar Sorptive Extraction

A headspace sorptive extraction method coupled with gas chromatography–mass spectrometry (HSSE–GC–MS) was developed for the determination of 37 volatile compounds in beer. After optimization of the extraction conditions, the best conditions for the analysis were stirring at 1000 rpm for 180 min, using an 8-mL sample with 25% NaCl. The analytical method provided excellent linearity values (R² > 0.99) for the calibration of all the compounds studied, with the detection and quantification limits obtained being low enough for the determination of the compounds in the beers studied. When studying the repeatability of the method, it proved to be quite accurate, since RSD% values lower than 20% were obtained for all the compounds. On the other hand, the recovery study was successfully concluded, resulting in acceptable values for most of the compounds (80–120%). The optimised method was successfully applied to real beer samples of different types (ale, lager, stout and wheat). Finally, an analytical comparison of the optimised HSSE method, with a previously developed and validated stir bar sorptive extraction (SBSE) method was performed, obtaining similar concentration values by both methods for most compounds.

Beer Molecules and Its Sensory and Biological Properties: A Review

The production and consumption of beer plays a significant role in the social, political, and economic activities of many societies. During brewing fermentation step, many volatile and phenolic compounds are produced. They bring several organoleptic characteristics to beer and also provide an identity for regional producers. In this review, the beer compounds synthesis, and their role in the chemical and sensory properties of craft beers, and potential health benefits are described. This review also describes the importance of fermentation for the brewing process, since alcohol and many volatile esters are produced and metabolized in this step, thus requiring strict control. Phenolic compounds are also present in beer and are important for human health since it was proved that many of them have antitumor and antioxidant activities, which provides valuable data for moderate dietary beer inclusion studies.