Optimal design of experiments applied to headspace solid phase microextraction for the quantification of vicinal diketones in beer through gas chromatography-mass spectrometric detection

Increasing Higher Alcohols and Acetates in Low-Alcohol Beer by Proteases

The market of non-alcoholic and low-alcohol beer has grown continuously thanks to the advocacy for healthy and responsible drinking. Non-alcoholic and low-alcohol products usually possess less higher alcohols and acetates and more aldehyde off-flavors due to the manufacturing processes. The employment of non-conventional yeasts partially mitigates this problem. In this study, we used proteases to optimize the wort amino acid profile for better aroma production during yeast fermentation. The design of experiments was applied to increase the leucine molar fraction, aiming to boost 3-methylbutan-1-ol and 3-methylbutyl acetate (banana-like aromas). This led to an increase from 7% to 11% leucine in wort after protease treatment. The aroma output in the subsequent fermentation, however, was yeast-dependent. An 87% increase of 3-methylbutan-1-ol and a 64% increase of 3-methylbutyl acetate were observed when Saccharomycodes ludwigii was used. When Pichia kluyveri was employed, higher alcohols and esters from valine and isoleucine were increased: 58% more of 2-methylpropyl acetate, 67% more of 2-methylbutan-1-ol, and 24% more of 2-methylbutyl acetate were observed. Conversely, 3-methylbutan-1-ol decreased by 58% and 3-methylbutyl acetate largely remained the same. Apart from these, the amounts of aldehyde intermediates were increased to a varying extent. The impact of such increases in aromas and off-flavors on the perception of low-alcohol beer remains to be evaluated by sensory analysis in future studies.

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning

The past decade witnessed rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve the classification and prediction of these properties. However, the barriers to collecting large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, advanced approaches (AL, SSL, and the hybrid) can greatly reduce the number of labeled samples, with some cases decreasing the number of labeled samples by more than half.

Multi-target optimization of solid phase microextraction to analyse key flavour compounds in wort and beer

Despite the literature comprises numerous studies dealing with the analysis of wort and beer flavour-related compounds by HS-SPME followed by GC-MS quantification, no generalized consensus exists regarding the optimal conditions for the extraction procedure. The complex chemistry nature of these matrices, the number of analytes, as well as the number and interactions among parameters affecting the extraction performance, requires the adoption of optimal experimental design protocols. This aspect is often overlooked and often not properly addressed in practice. Therefore, in the present work, the optimal conditions under which a range of wort and beer analytes can be extracted and quantified were analysed. The optimal extraction conditions were presented at two levels of aggregation: global (untargeted) and key-flavour analysis. Experimental data was generated by Definitive-Screening-Design, followed by model development and optimization. Both approaches were compared and critically analysed. For vicinal-diketones group, a complete validation study for the optimal conditions is presented.

A Bottom-Up Approach for Data Mining in Bioaromatization of Beers Using Flow-Modulated Comprehensive Two-Dimensional Gas Chromatography/Mass Spectrometry

In this study, we report the combination of comprehensive two-dimensional gas chromatography (GC×GC) with multivariate pattern recognition through template matching for the assignment of the contribution of Brazilian Ale 02 yeast strain to the aroma profile of beer compared with the traditional Nottingham yeast. Volatile organic compounds (VOC) from two beer samples, which were fermented with these yeast strains were sampled using headspace solid-phase microextraction (HS-SPME). The aroma profiles from both beer samples were obtained using GC×GC coupled to a fast scanning quadrupole mass spectrometer. Data processing performed through multiway principal components analysis succeeded in separating both beer samples based on yeast strain. The execution of a simple and reliable procedure succeeded and identified 46 compounds as relevant for sample classification. Furthermore, the bottom-up approach spotted compounds found exclusively in the beer sample fermented with the Brazilian yeast, highlighting the bioaromatization properties introduced to the aroma profile by this yeast strain.

A review of the analytical methods used for beer ingredient and finished product analysis and quality control

Beer is an incredibly complex beverage containing more than 3000 different compounds, including carbohydrates, proteins, ions, microbes, organic acids, and polyphenols, among others. Beer becomes even more complex during storage, for over time it may undergo chemical changes that negatively affect the flavor, aroma, and appearance. Thus, it can be expected that maintaining the quality of beer throughout its lifetime is a difficult task. Since it is such a popular drink throughout the world, being familiar with proper analytical techniques for beer evaluation is useful for researchers and brewers. These techniques include, but are not limited to, gas chromatography, liquid chromatography, matrix assisted laser desorption/ionization, capillary electrophoresis, mass spectrometry, ultraviolet-visible spectroscopy, and flame ionization detection. This review aims to summarize the various ingredients and components of beer, discuss how they affect the finished product, and present some of the analytical methods used for quality control and understanding the formation of chemicals in beer during the brewing process.

Implementation of multicriteria decision analysis in design of experiment for dispersive liquid-liquid microextraction optimization for chlorophenols determination

A novel and efficient approach to optimization of extraction step prior the chromatographic determination of nine chlorinated phenols is described. It is based on the combination of design of experiments and multicriteria decision analysis. Such an approach is used to optimize dispersive liquid-liquid microextraction procedure for the determination of 9 chlorophenols in water samples. Three parameters are optimized - sample volume, volume of disperser solvent and extraction solvent. Combination of the technique for order of preference by similarity to ideal solution with central composite design allows to perform multi-analyte procedure optimization. It gives information about the efficiency of the system for every experimental plan point in terms of closeness to ideal solution. The optimal conditions for extraction of chlorophenols are 76 μL of extraction solvent, 0.6 mL of dispersive solvent and 6.7 mL of water sample. The presented approach has the potential to be applied in variety of optimization systems.

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as "design of experiments" (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.