Predicting acetic acid content in the final beer using neural networks and support vector machine

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g-1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin-Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R2) and root mean square error (RMSE), the optimal network configuration was identified as a 5-11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm

Triticale grain, a wheat-rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production.

Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification

Fish head cutting is one of the most important processes during fish pre-processing. At present, the identification of cutting positions mainly depends on manual experience, which cannot meet the requirements of large-scale production lines. In this paper, a fast and contactless identification method of cutting position was carried out by using a constructed line laser data acquisition system. The fish surface data were collected by a linear laser scanning sensor, and Principal Component Analysis (PCA) was used to reduce the dimensions of the dorsal and abdominal boundary data. Based on the dimension data, Least Squares Support Vector Machines (LS-SVMs), Particle Swarm Optimization-Back Propagation (PSO-BP) networks, and Long and Short Term Memory (LSTM) neural networks were applied for fish head cutting position identification model establishment. According to the results, the LSTM model was considered to be the best prediction model with a determination coefficient (R2) value, root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD) of 0.9480, 0.2957, 0.1933, and 3.1426, respectively. This study demonstrated the reliability of combining line laser scanning techniques with machine learning using LSTM to identify the fish head cutting position accurately and quickly. It can provide a theoretical reference for the development of intelligent processing and intelligent cutting equipment for fish.

Novel Saccharomyces cerevisiae × Saccharomyces mikatae Hybrids for Non-alcoholic Beer Production

The popularity of non-alcoholic beers has been increasing over the past few years. Maltose-negative strains of different genera are frequently used to obtain beers of low alcohol content. S. cerevisiae hybrids with other Saccharomyces species offer interesting inherited flavour characteristics; however, their use in non-alcoholic beer production is rare. In this work, we constructed six hybrids of maltose-negative S. cerevisiae parental strains (modified to produce higher amounts of organic acids) and S. mikatae (wild-type). Growth behaviour, osmotolerance and fermentation features of the offspring were compared with parental strains. One hybrid with mitochondrial DNA inherited from both parents was used to produce non-alcoholic beer in which organic metabolites were evaluated by HPLC and HS-SPME-GC-MS. This hybrid produced non-alcoholic beer (≤0.05% (v/v)) with an increased organic acid content, just as its parent S. cerevisiae, but without producing increased amounts of acetic acid. The beer had a neutral aromatic profile with no negative off-flavours, similar to the beer produced by the parent S. mikatae, which was used for the first time to produce non-alcoholic beer. Overall, both parents and hybrid yeast produced non-alcoholic beers with increased amounts of higher alcohols compared with esters.

Brewing on an industrial and a craft scale – impact on the physicochemical properties and volatile compounds profile of the pale pilsener-style lager beer analysed with HS/GC-MS

The pale Pilsener-style lager beers produced on a massive and craft scale were taken to analyse their basic physicochemical properties (alcohol content, pH, haze, real degree of fermentation) and volatile compounds profiles. The research was carried out using a beer analyser equipment and a headspace gas chromatography-mass spectrometry method (HS/GC-MS). The findings showed that in terms of physicochemical and flavour attributes, the quality of craft beers differed to a higher degree from the standard Pilsener beer quality than in the case of industrial beers.

Artificial Neural Networks and Gompertz Functions for Modelling and Prediction of Solvents Produced by the S. cerevisiae Safale S04 Yeast

The present work aims to develop a mathematical model, based on Gompertz equations and ANNs to predict the concentration of four solvent compounds (isobutanol, ethyl acetate, amyl alcohol and n-propanol) produced by the yeasts S. cerevisiae, Safale S04, using only the fermentation temperature as input data. A beer wort was made, daily samples were taken and analysed by GC-FID. The database was grouped into five datasets of fermentation at different setpoint temperatures (15.0, 16.5, 18.0, 19.0 and 21.0 °C). With these data, the Gompertz models were parameterized, and new virtual datasets were used to train the ANNs. The coefficient of determination (R2) and p-value were used to compare the results. The ANNs, trained with the virtual data generated with the Gompertz functions, were the models with the highest R2 values (0.939 to 0.996), showing that the proposed methodology constitutes a useful tool to improve the quality (flavour and aroma) of beers through temperature control.

Effect of Immobilization Support and Fermentation Temperature on Beer and Fermented Milk Aroma Profiles

The food industry increasingly produces wastes like coconut and peanut shells. In addition, low temperature fermentation is always a challenge. Therefore, in the present study, a sustainable exploitation of these by-products is proposed through the production of carriers for immobilized cells of yeast and bacteria. The immobilized cells, after thermally drying, were evaluated for their efficiency in beer and milk fermentations respectively, in various fermentation temperatures and storage for up to three months. The beers and fermented milks were evaluated for their aroma and the results showed products of high quality. Coconut shells resulted in better products with increased fruity ester content in fermented milks and reduced dimethyl sulfite and vicinal diketones and increased ratio of esters to alcohol in beers. These results reveal the possibilities of immobilized cells in coconut and peanut shells for application in food industry, however, more research is needed to evaluate their effect on sensory characteristics and possible prebiotic and probiotic potential especially in the case of fermented milks.

Craft Beers Fermented by Potential Probiotic Yeast or Lacticaseibacilli Strains Promote Antidepressant-Like Behavior in Swiss Webster Mice

This study aimed to produce a probiotic-containing functional wheat beer (PWB) by an axenic culture system with potential probiotic Saccharomyces cerevisiae var boulardii 17 and probiotic-containing functional sour beer (PSB) by a semi-separated co-cultivation system with potential probiotic Lacticaseibacillus paracasei DTA 81 and Saccharomyces cerevisiae S-04. Additionally, results obtained from in vivo behavioral tests with Swiss Webster mice treated with PWB or PSB were provided, which is scarce in the current literature. Although the use of S. boulardii to produce beers is not a novelty, this study demonstrated that S. boulardii 17 performance on sugar wort stills not completely elucidated; therefore, further studies should be considered before using the strain in industrial-scale production. Co-culture systems with lacticaseibacilli strain and S. cerevisiae have been reported in the literature for PSB production. However, lacticaseibacilli survivability in beer can be improved by semi-separated co-cultivation systems, highlighting the importance of growing lacticaseibacilli in the wort before yeast pitching. Besides, kettle hopping must be chosen as the method for hop addition to produce PSB. The dry-hopping method may prevent iso-alpha formation in the wort; however, a tendency to sediment can drag cells at the tank bottom and negatively affect L. paracasei DTA 81 viability. Despite stress factors from the matrices and the stressful conditions encountered during GI transit, potential probiotic S. boulardii 17 and potential probiotic L. paracasei DTA 81 withstood at sufficient doses to promote antidepressant effects in the mice group treated with PWB or PSB, respectively.

A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting

The development of digital tools based on artificial intelligence can produce affordable and accurate methodologies to assess quality traits and sensory analysis of beers. These new and emerging technologies can also assess new products in a near real-time fashion through virtual simulations before the brewing process. This research was based on the development of specific digital tools (four models) to assess quality traits and sensory profiles of beers produced using sonication and traditional brewing techniques. Results showed that models developed using supervised machine learning (ML) regression algorithms based on near-infrared spectroscopy (NIR) were highly accurate in the estimation of physicochemical parameters (Model 1; R = 0.94; b = 0.91). Outputs from Model 1 were then used as inputs to obtain estimations of the intensity of sensory descriptors (Model 2; R = 0.99; b = 0.98), liking of sensory attributes (Model 3; R = 0.97; b = 0.99), and the classification of fermentation treatments using supervised classification ML algorithms (Model 4; 96% accuracy). These new digital tools can aid craft brewing companies for product development at lower costs and maintain specific quality traits and sensory profiles, creating original styles of beers to get positioned in the market.

Beer Aroma and Quality Traits Assessment Using Artificial Intelligence

Increasing beer quality demands from consumers have put pressure on brewers to target specific steps within the beer-making process to modify beer styles and quality traits. However, this demands more robust methodologies to assess the final aroma profiles and physicochemical characteristics of beers. This research shows the construction of artificial intelligence (AI) models based on aroma profiles, chemometrics, and chemical fingerprinting using near-infrared spectroscopy (NIR) obtained from 20 commercial beers used as targets. Results showed that machine learning models obtained using NIR from beers as inputs were accurate and robust in the prediction of six important aromas for beer (Model 1; R = 0.91; b = 0.87) and chemometrics (Model 2; R = 0.93; b = 0.90). Additionally, two more accurate models were obtained from robotics (RoboBEER) to obtain the same aroma profiles (Model 3; R = 0.99; b = 1.00) and chemometrics (Model 4; R = 0.98; b = 1.00). Low-cost robotics and sensors coupled with computer vision and machine learning modeling could help brewers in the decision-making process to target specific consumer preferences and to secure higher consumer demands.

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

Growth Parameters and Survivability of Saccharomyces boulardii for Probiotic Alcoholic Beverages Development

The aim of this research was to optimize the growth parameters (pH, ethanol tolerance, initial cell concentration and temperature) for Saccharomyces boulardii and its tolerance to in vitro gastrointestinal conditions for probiotic alcoholic beverage development. Placket-Burman screening was used to select only statistically significant variables, and the polynomial mathematical model for yeast growth was obtained by central composite rotatable design. Confirmation experiments to determine the kinetic parameters for yeast growth were carried out by controlling the temperature and pH. Soon after, the survivability of yeast was tested under in vitro conditions mimicking the human upper gastrointestinal transit. S. boulardii had suitable resistance to alcohol and gastrointestinal conditions for probiotic alcoholic beverage development.

Study of the influence of brewing water on selected analytes in beer

Brewing water is one of the basic raw materials for beer production and knowledge of its composition and pH is essential for the proper conduct of the entire brewing process. In this study, it was observed how the composition of water influences OG values, content of B vitamins, organic acids and iso-α-acids. For brewing, synthetic water was prepared by adding chemicals to deionized water. Models of hard (pH 8.47 ±0.08) and soft (pH 7.68 ±0.23) synthetic water were used for brewing pale bottom-fermented lager beers. Samples of wort, hopped wort, young beer and beer were collected during beer production. HPLC-DAD was used for B vitamins and iso-α-bitter acids quantification. Determination of organic acids was done by ion chromatography with conductivity detector. Obtained data were statistically processed with ANOVA (Analysis of Variance) and interval of confidence was set to 95%. According to the statistical analysis, water composition affects analytes content during beer production and in the final product. Hard water seemed to be a better extraction buffer and its composition (pH) positively affected some processes during brewing technology. One of them was obtaining higher OG values compared to soft water. The beer made from hard water also contained more B vitamins. Composition of brewing water had no influence neither on concentration of organic acids nor on iso-α-acids in conditions of homebrewing.

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers

Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of sensory descriptors in beer using the physical measurements of color and foam-related parameters. A robotic pourer (RoboBEER), was used to obtain 15 color and foam-related parameters from 22 different commercial beer samples. A sensory session using quantitative descriptive analysis (QDA^® ) with trained panelists was conducted to assess the intensity of 10 beer descriptors. Results showed that the principal component analysis explained 64% of data variability with correlations found between foam-related descriptors from sensory and RoboBEER such as the positive and significant correlation between carbon dioxide and carbonation mouthfeel (R = 0.62), correlation of viscosity to sensory, and maximum volume of foam and total lifetime of foam (R = 0.75, R = 0.77, respectively). Using the RoboBEER parameters as inputs, an artificial neural network (ANN) regression model showed high correlation (R = 0.91) to predict the intensity levels of 10 related sensory descriptors such as yeast, grains and hops aromas, hops flavor, bitter, sour and sweet tastes, viscosity, carbonation, and astringency.

PRACTICAL APPLICATIONS

This paper is a novel approach for food science using machine modeling techniques that could contribute significantly to rapid screenings of food and brewage products for the food industry and the implementation of Artificial Intelligence (AI). The use of RoboBEER to assess beer quality showed to be a reliable, objective, accurate, and less time-consuming method to predict sensory descriptors compared to trained sensory panels. Hence, this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications.

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms

BACKGROUND

Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam.

RESULTS

The ANN method was able to create more accurate models (R²  = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type.

CONCLUSION

The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry.

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.