The Effect of Soundwaves on Foamability Properties and Sensory of Beers with a Machine Learning Modeling Approach

The use of ultrasounds has been implemented to increase yeast viability, de-foaming, and cavitation in foods and beverages. However, the application of low frequency audible sound to decrease bubble size and improve foamability has not been explored. In this study, three treatments using India Pale Ale beers were tested, which include (1) a control, (2) the application of audible sound during fermentation, and (3) the application of audible sound during natural carbonation. Five different audible frequencies (20 Hz, 30 Hz, 45 Hz, 55 Hz, and 75 Hz) were applied daily for one minute each (starting from the lowest frequency) during fermentation (11 days, treatment 2) and carbonation (22 days, treatment 3). Samples were measured in triplicates using the RoboBEER to assess color and foam-related parameters. A trained panel (n = 10) evaluated the intensity of sensory descriptors. Results showed that samples with sonication treatment had significant differences in the number of small bubbles, alcohol, and viscosity compared to the control. Furthermore, except for foam texture, foam height, and viscosity, there were non-significant differences in the intensity of any sensory descriptor, according to the rating from the trained sensory panel. The use of soundwaves is a potential treatment for brewing to improve beer quality by increasing the number of small bubbles and foamability without disrupting yeast or modifying the aroma and flavor profile

Beer and Consumer Response Using Biometrics: Associations Assessment of Beer Compounds and Elicited Emotions

Some chemical compounds, especially alcohol, sugars, and alkaloids such as hordenine, have been reported as elicitors of different emotional responses. This preliminary study was based on six commercial beers selected according to their fermentation type, with two beers of each type (spontaneous, bottom, and top). Chemometry and sensory analysis were performed for all samples to determine relationships and patterns between chemical composition and emotional responses from consumers. The results showed that sweeter samples were associated with higher perceived liking by consumers and positive emotions, which corresponded to spontaneous fermentation beers. There was high correlation (R = 0.91; R2 = 0.83) between hordenine and alcohol content. Beers presenting higher concentrations of both, and higher bitterness, were related to negative emotions. Further studies should be conducted, giving more time for emotional response analysis between beer samples, and comparing alcoholic and non-alcoholic beers with similar styles, to separate the effects of alcohol and hordenine. This preliminary study was a first attempt to associate beer compounds with the emotional responses of consumers using non-invasive biometrics

Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification ma-chine learning (ML) modelling. Six different ML models were developed; Model 1 (M1) and M2 were developed using the NIR absorbance values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the samples into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies

Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System

Artificial neural networks (ANN) have become popular for optimization and prediction of parameters in foods, beverages, agriculture and medicine. For brewing, they have been explored to develop rapid methods to assess product quality and acceptability. Different beers (N = 17) were analyzed in triplicates using a robotic pourer, RoboBEER (University of Melbourne, Melbourne, Australia), to assess 15 color and foam-related parameters using computer-vision. Those samples were tested using sensory analysis for acceptability of carbonation mouthfeel, bitterness, flavor and overall liking with 30 consumers using a 9-point hedonic scale. ANN models were developed using 17 different training algorithms with 15 color and foam-related parameters as inputs and liking of four descriptors obtained from consumers as targets. Each algorithm was tested using five, seven and ten neurons and compared to select the best model based on correlation coefficients, slope and performance (mean squared error (MSE). Bayesian Regularization algorithm with seven neurons presented the best correlation (R = 0.98) and highest performance (MSE = 0.03) with no overfitting. These models may be used as a cost-effective method for fast-screening of beers during processing to assess acceptability more efficiently. The use of RoboBEER, computer-vision algorithms and ANN will allow the implementation of an artificial intelligence system for the brewing industry to assess its effectiveness

The effect of bubble formation within carbonated drinks on the brewage foamability, bubble dynamics and sensory perception by consumers

© 2020 Claudia Gonzalez Viejo DuranBeer and sparkling water are popular carbonated beverages within consumers as it can be evidenced by an important growth in terms of volume sales around the world. Although they are both composed of carbon dioxide (CO2), this gas is produced in different forms and the drinks present distinct performance in terms of their sensory and physicochemical characteristics. The most important factors that determine quality and consumer acceptability in all carbonated beverages are the visual aspects such as colour, bubble morphometry and dynamics and foam-related parameters as these create the first impression when consumers select the products. Currently, consumers are in a constant search for more premium or high-quality products, which has put pressure in their respective industries to remain relevant in the market. Available methods to assess physical and chemical parameters in carbonated beverages to assess quality tend to be costly, time-consuming and not reliable. Therefore, there is then a need to develop automatic, more reliable, accurate and affordable quality assessment techniques. From the sensory analysis perspective, traditional consumer tests to assess products acceptability are primarily focused on subjective conscious responses from participants which contribute a reduced amount of information, which many times it can be bias. Traditional methods to tap into the autonomic nervous system response from consumers, which is unconscious, are invasive and had not been implemented in the assessment of beer and sparkling water, which can complement and enhance objective and meaningful information for consumers from the physiological and emotional responses to these products. This research was focused on the development of novel techniques such as an automatic robotic pourer integrated with remote sensing to measure the physical dynamics of foamability and bubbles coupled with CO2, alcohol and pouring temperature to add chemical analysis. The data analysis included an artificial intelligence (AI) approach by using computer vision algorithms and machine learning modelling. The objective from these measurements was to assess color, bubble and foam-related parameters in both beer and carbonated water. Near-infrared (NIR) spectroscopy was used to obtain the chemical fingerprinting of the products and to analyze their relationship with foamability and bubble related measurements. Furthermore, an electronic nose was developed using nine gas sensors to assess aromas and beer quality to enhance the chemical (non-contact) analysis from the robotic pourer. Additionally, a new integrated system to assess consumer acceptability using a novel bio-sensory computer application (App) coupled with video and thermal cameras was developed to obtain biometric responses such as heart rate, face temperature, gaze fixations and facial expressions. This new App was used on panelists assessing different products considered in this research. The information gathered from these new techniques allowed to create different machine learning (ML) models with high accuracy (R>0.8) to classify beers according to their liking and physicochemical intrinsic characteristics. Likewise, different ML models based on regression algorithms were created using the data obtained from the robotic pourer, electronic nose, and NIR spectroscopy data to predict the products intensity of sensory descriptors, consumers acceptability and chemometry to assess beer quality. The results obtained from this research showed to be accurate, reliable, rapid and affordable tools that can be applied to the growing industries related to beverages production to monitor and increase product quality to compete in the international market

Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling

The success of the olive oil industry depends on provenance and quality-trait consistency affecting the consumers' acceptability/preference and purchase intention. Companies rely on laboratories to analyze samples to assess consistency within the production chain, which may be time-consuming, cost-restrictive, and untimely obtaining results, making the process more reactive than predictive. This study proposed implementing digital technologies using near-infrared spectroscopy (NIR) and a novel low-cost e-nose to assess the level of rancidity and aromas in commercial extra-virgin olive oil. Four different olive oils were spiked with three rancidity levels (N = 17). These samples were evaluated using gas-chromatography-mass-spectroscopy, NIR, and an e-nose. Four machine learning models were developed to classify olive oil types and rancidity (Model 1: NIR inputs; Model 2: e-nose inputs) and predict the peak area of 16 aromas (Model 3: NIR; Model 4: e-nose inputs). The results showed high accuracies (Models 1–2: 97% and 87%; Models 3–4: R = 0.96 and 0.93). These digital technologies may change companies from a reactive to a more predictive production of food/beverages to secure product quality and acceptability

Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance

Due to increased fraud rates through counterfeiting and adulteration of quality wines, it is important to develop novel non-destructive techniques to assess wine quality and provenance. Therefore, our research group developed a novel method using near-infrared (NIR) spectroscopy (1596-2396 nm) coupled with machine learning (ML) modeling to assess wine vintages and quality traits based on the intensity of sensory descriptors through the bottle. These were developed using samples from an Australian vineyard for Shirazwines. Models resulted in high accuracy 97% for classification (vintages) and R=0.95 regression (sensory quality traits). The proposed method will allow to assess authenticity and sensory quality traits of any wines in the market without the need to open the bottles, which is rapid, accurate, effective, and convenient. Furthermore, currently, there are low-cost NIR devices available in the market with the required spectral range and sensitivity, which can be affordable for winemakers and retailers that can be used with the ML models proposed here

Novel digital technologies to assess smoke taint in berries and wines due to bushfires

Due to climate change, the higher incidence and severity of bushfires is a significant challenge for wine producers worldwide as an increase in smoke contamination negatively affects the physicochemical components that contribute to the lower quality of fresh produce and final products (smoke taint in wines). This reduces prices and consumer acceptability, impacting the producers and manufacturers. Current methods available to winemakers for assessing contamination in berries and wine consist of costly laboratory analyses that require skilled personnel and are time-consuming, cost prohibitive, and destructive. Therefore, novel, rapid, cost-effective, and reliable methods using digital technologies such as the use of near-infrared (NIR) spectroscopy, electronic nose (e-nose), and machine learning (ML) have been developed by our research group. Several ML models have been developed for smoke taint detection and quantification in berries and wine from different varieties using NIR absorbance values or e-nose raw data as inputs to predict glycoconjugates, volatile phenols, volatile aromatic compounds, smoke-taint amelioration techniques efficacy, and sensory descriptors, all models with >97% accuracy. These methods and models may be integrated and automated as digital twins to assess smoke contamination in berries and smoke taint in wine from the vineyard for early decision-making

Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling

Sourdough bread (SB) has increased popularity due to health benefits and higher interest in artisan breadmaking due to social isolation during the COVID-19 pandemic. However, quality traits and consumer assessment are still limited to complex laboratory analysis and sensory trials. In this research, new and emerging digital technologies were tested to assess quality traits of SB made from six different flour sources. The results showed that machine learning (ML) models developed to classify the type of wheat used for flours (targets) from near-infrared (NIR) spectroscopy data (Model 1) and a low-cost electronic nose (Model 2) as inputs rendered highly accurate and precise models (96.3% and 99.4%, respectively). Furthermore, ML regression models based on the same inputs for NIR (Model 3) and e-nose (Model 4) were developed to automatically assess 16 volatile aromatic compounds (targets) using GC-MS as ground-truth. To reiterate, models with high accuracy and performance were obtained with correlation (R), determination coefficients (R2), and slope (b) of R = 0.97; R2 = 0.94 and b = 0.99 for Model 3 and R = 0.99; R2 = 0.99 and b = 0.99 for Model 4. The development of low-cost instrumentation and sensors could make possible the accessibility of hardware and software to the industry and artisan breadmakers to assess quality traits and consistency of SB

Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies

Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain