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Abstract

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.

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... The samples were analyzed using a robotic pourer RoboBEER and Matlab ® R2018b (Mathworks, Inc., Matick, MA, USA) to assess foam and color-related parameters and through a trained sensory panel (n = 10) to assess significant differences between the treatments in their sensory descriptors. Lastly, two machine learning models previously developed by Gonzalez Viejo et al. [29,30] were tested by feeding the inputs from the RoboBEER of the triplicates of the three treatments to (i) predict the type of fermentation and (ii) to predict the intensities of sensory descriptors by obtaining high accuracy in the testing. ...
... Two machine learning models developed using commercial beers with artificial neural networks (ANN) [29,30] were fed with the 15 parameters obtained from the RoboBEER for the control. SWF and SWC were used as inputs to predict the type of fermentation and to predict the intensity of ten sensory descriptors (AHops, AYeast, AGrain, MVisc, MAstr, MCarb, TBitt, TSweet, TSour, and FHops). ...
... The model used to predict the values had a correlation of R = 0.91 and a determination coefficient R 2 = 0.83. It can be found from Gonzalez Viejo et al. [30]. For the samples developed in this study, the correlation between the observed values obtained with the trained sensory panel and the predicted values using the model was R = 0.85 with a determination coefficient R 2 = 0.72 and a slope of 1.09. ...
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The use of ultrasound has been implemented to increase yeast viability, defoaming 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: 1) control, 2) application of audible sound during fermentation and 3) application of audible sound during natural carbonation. Five different audible frequencies (20, 30, 45, 55 and 75 Hz) were applied daily for 1 min each starting from the lowest during fermentation periods (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 and a trained panel to evaluate 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 a 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.
... ML modeling has been integrated with other technologies such as sensors, robotics, and CV to classify the samples into different categories and/or to predict different parameters. Some examples of this are the prediction of aromas and off-aromas using electronic noses (e-noses) as inputs [7,17], prediction of acetic acid using the fermentation data obtained from the process [18], the use of a robotic pourer (RoboBEER; The University of Melbourne, Parkville, Vic, Australia) and CV techniques to assess foam, color and bubble parameters to predict the sensory profile of beers [19], consumers acceptability [20], and proteins [21]. Classification models have also been developed with the use of e-noses to categorize beers according to their defects [17] and style [22][23][24], and the use of color-and foam-related parameters obtained using the RoboBEER to classify the samples according to the type of fermentation [14]. ...
... Around 95% of new food and beverage products fail in the market without proper sensory studies [25]. As previously mentioned, earlier studies have shown highly accurate ML models developed using RoboBEER parameters to predict sensory attributes [19,20] and proteins [21]. Therefore, this study aimed to develop new methods to assess the aroma profile and chemical parameters of beer using an AI approach by integrating robotics, sensors, CV, and ML techniques, which will help the industry to increase the acceptability of their products. ...
... According to the PCA, aromatic compounds such as esters (ethyl caproate, ethyl decanoate, ethyl laurate, and ethyl octanoate) had a positive relationship with the foaming parameters; this may be because the bubbles in the foam contain most aromatic volatiles, compared with the liquid phase, and when they start bursting gradually, these are released and perceived [33,34]. The relationship between Brix, o-tolualdehyde (cherry aroma), and "a" coincides with Abeytilakarathna et al. [35], who reported a correlation between Brix and red colors in red fruits, and Gonzalez Viejo et al. [19], who found a similar relationship in beer samples. ...
Article
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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 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.
... The first is mainly used for decision making as it classifies samples into two or more categories, the most publicized applications can be found in medical diagnosis [4,5], food and beverages to classify into types of brewages [6][7][8] and level of liking of brewages [8,9], in agriculture for identification of grapevine cultivars [10], and to estimate plant water status [11], among others. Fitting or regression is used to predict specific values of certain variables such as chemical compounds [7,12], sensory descriptors [13], and microbial spoilage [14] among others. ...
... The use of machine learning algorithms, especially ANN, in food and brewages has become more popular in recent years as they aid in the increase in accuracy, time and cost reduction in analytical and sensory methods to assess quality and acceptability of beverages [20]. Specifically, in beer, it has been used in the prediction of chemical compounds using near-infrared spectroscopy [7,21,22], and prediction of the intensity of sensory descriptors [13,23]. ...
... Furthermore, there is a relationship between the foam and color-related parameters, and bitterness as the iso-α-acids derived from hops are responsible for bitterness, but also contribute to foamability and foam stability due to their tensio-active properties. Furthermore, hops contribute to the development of aromas and flavors in beer, and foam aids in the release of aromas and flavors when bubbles burst [8,13,33,34]. ...
Article
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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, 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 [means 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.
... This type of sensory test requires a fixed panel of eight to sixteen participants with several training sessions for the specific product, which leads to costly and time-consuming methods, including conducting the sessions, data handling and statistical analysis [13]. Some industries (i.e., brewing), rely on one or two people, such as the master brewer, to assess the sensory quality of the product; however, this is not an objective, accurate nor reliable method, as it does not follow any structured and quantitative method, and does not involve any statistical analysis [14]. Another type of sensory test is the acceptability, which consists of gathering a minimum of 30-100 consumers, depending on the number of samples to evaluate and the target mean values expected for liking results. ...
... More recently, a robotic pourer named RoboBEER (The University of Melbourne, Melbourne, VIC, Australia) was developed using LEGO ® blocks and servo motors (The Lego Group, Billund, Denmark), and this is coupled with infrared temperature, carbon dioxide, and ethanol gas ubiquitous sensors controlled with Arduino ® boards. This robot consists of a glass chamber, a bottle holder and a pivot in the bottle neck, and may be adapted for any bottle shapes and height to pour 80 ± 10 mL; this, coupled with CV and ML, is able to predict beer quality based on color and foam-related parameters, which will be explained later in this paper [7,9,14,17,[30][31][32]. ...
... Some of the main classifier types consist of (i) decision trees, (ii) discriminant analysis, (iii) logistic regression, (iv) naïve Bayes, (v) support vector machines, (vi) nearest neighbor, (vii) ensemble and (viii) artificial neural networks (ANN) [89]. Regression or fitting learners are usually employed to predict specific attributes or parameters such as chemometrics, microbial counts, and intensities of sensory descriptors, among others, and have been used in areas such as agriculture [90], food and beverages [9,14], among others. This type of ML may be classified as (i) linear regression, (ii) regression trees, (iii) support vector machines, (iv) Gaussian process regression, (v) ensembles of trees, and (vi) ANN [89]. ...
Article
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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 to get results in real-time. Therefore, there is a need to test and implement emerging technologies 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 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 researchers about the concepts of artificial intelligence and machine learning as well as the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.
... However, there is minimal reliance on scientific tests, such as physicochemical or sensory analysis of beers produces, making the process dependent on the brewer's experience and trial and error, especially in craft breweries. Some of the larger brewing companies rely more on the familiarity of products and styles, which are maintained with sensory and physicochemical analyses commonly made in-house and using traditional methods, which are time-consuming and expensive [4][5][6][7]. ...
... One of these advances for the physicochemical assessment of beers is using the RoboBEER pourer (The University of Melbourne, Parkville, Vic, Australia), which is coupled with computer vision and non-invasive sensors to assess the gas release of beers (electronic nose) [6,16]. Outputs from this robot have been modeled using machine learning to assess the sensory properties of beers [4], consumers' acceptability [9], proteins [19], aromas [6], and the type of fermentation (top, bottom, or spontaneous [16]). ...
... Overtones at 2074 nm correspond to amines [30], which are present in beer, especially as biogenic amines [14]. On the other hand, peaks > 2250 nm correspond to overtones of proteins and carbohydrates [27], which are of high importance for beer quality, as these are responsible for foam formation and stability [4,14,16,17,22]. The correlations found between MaxVol and FHeight and between TLTF, LFT, and FStability indicate that the panelists were well-trained and are in accordance with the relationships found by Gonzalez Viejo et al. [16] using commercial beer samples and a QDA ® trained panel. ...
Article
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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.
... Aroma is among the main quality traits in foods and beverages as it is one of the first attributes that consumers assess before tasting the product [1]. Furthermore, they are usually used as indicators of bad quality issues that may be due to storage conditions, contamination during processing, and raw material, among others. ...
... This approach is valid when proper training is provided to the sensory panel, and appropriate statistics are applied to analyze the results [11]. However, due to the required expertise, costs and time involved to conduct these sessions for every batch, most breweries only rely on the assessment of one or two people, including the master brewer, which is less reliable and more subjective [1]. ...
... Most of the bottom fermentation beers were clustered close to vectors related to foam drainage (FDrain) of beers and color red, green and blue (RGB) and L; the latter may be explained due to the lighter color that these beers tend to have [49] compared to those form top and spontaneous fermentation. Furthermore, the spontaneous beers were grouped mostly close to bubble formation with the distribution of bubbles between small, medium, and large size (SmBubb, MedBubb, and LgBubb) contributing to a higher lifetime of foam (LTF), similar results have been previously reported for both physicochemical and sensory data [10,13,50,51]. On the other hand, top fermentation beers clustered mostly closer to all the gas sensors' sensitivity with a variation of foamability and bitterness which mainly influenced overall aroma, and flavor liking along with higher beer carbonation mouthfeel (Mcarb) (Figure 3a). ...
... There are significant correlations between small bubbles (SmBubb) and liking, which were directly related to the lifetime of foam (LTF) and retention of foam of beers, hence decreasing the release of gases after pouring, which can explain the absence of correlation between small bubbles and the e-nose gas sensors. Furthermore, correlations between LTF and SmBubb with 'a' from CIELab scale were found; this may be explained with the findings in other studies showing that beers and berries with more red color had higher sugar content [50,58], and at the same time sugars act as surfactant substances, which are responsible for increasing beer's viscosity and, therefore, increasing foam stability and reducing bubble size [13,17,59]. ...
Article
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Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R=0.90) and e-nose data (Model 3; R=0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.
... Furthermore, the videos were analyzed with computer vision algorithms developed in Matlab ® R2019b (Mathworks Inc., Natick, MA, USA) to obtain the maximum volume of foam (MaxVol), lifetime of foam (LTF), total lifetime of foam (TLTF), foam drainage (FDrain), color in both CIELab and RGB scales, and bubble size distribution grouped as small (SmBubb), medium (MedBubb), and large (LgBubb). A more detailed description of the technique may be obtained in the papers published by Gonzalez Viejo et al. [1,[23][24][25]. ...
... Although some studies have successfully developed predictive models for proteins using NIR data as inputs using partial least squares regression (PLS), these only predict the total protein content; therefore, it is not able to provide more specific and multitarget results [36][37][38][39]. Other results with higher accuracy and performance of ANN modeling using calculated parameters rather than raw data (such as NIR spectra) have been reported for different purposes, such as other beer quality parameters, such as sensory attributes [23] and type of fermentation, using the physical parameters (color, foam, and bubbles) [1] and from different studies related to the classification of grapevine leaves into cultivars based on morphometric and colorimetric parameters [40] and prediction of cocoa aromas from canopy architecture parameters of cacao trees obtained using remote sensing and computer vision algorithms [41]. ...
Article
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Foam-related parameters are related to beer quality and dependent among others to protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer samples were analyzed through proteomics. Furthermore, samples were analyzed using a robotic pourer, RoboBEER, to assess 15 color and foam-related parameters, and a near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW) and a matrix was developed to assess significant correlations (p<0.05) with RoboBEER parameters. Two ML models were developed using the i) NIR (Model 1), and ii) RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Model 1 was not as accurate (testing r=0.68; overall r=0.89) as Model 2 (testing r=0.90; overall r=0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.
... Likewise, a study including eye-tracking techniques was conducted to assess the acceptability and preference of different beer samples through their visual evaluation by watching videos from their pouring and they were able to develop a machine-learning model using the biometric responses from consumers, along with the objective parameters related to foam and color, maeasured using a robotic pourer RoboBEER, as inputs to classify the samples into high and low liking of the foamability with 82% accuracy [7]. The biosensory app without the video and IRTI recording has also been used in other beer studies to assess the intensity of the samples' descriptors using a 15 cm nonstructured scale based on the quantitative descriptive analysis method obtaining the expected results according to the samples tested [24,25]. ...
Article
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In sensory evaluation there have been many attempts to obtain responses from the autonomic nervous system (ANS) by analyzing heart rate, body temperature and facial expressions. However, the methods involved tend to be intrusive, which interfere with the consumers responses as they are more aware of the measurements. Furthermore, the existing methods to measure the different ANS responses are not synchronized among them as they are measured independently. This paper discusses the development of an integrated camera system paired with an Android PC application to assess sensory evaluation and biometric responses simultaneously in The Cloud, such as heart rate, blood pressure, facial expressions and skin temperature changes using video and thermal images acquired by the integrated system and analyzed through computer vision algorithms written in Matlab®, and FaceReaderTM. All results can be analyzed through customized codes for multivariate data analysis, based on principal component analysis and cluster analysis. Data collected can be also used for machine learning modelling based on biometrics as inputs and self-reported data as targets. Based on previous studies using this integrated camera and analysis system, it has shown to be a reliable, accurate and convenient technique to complement the traditional sensory analysis of both food and non-food products to obtain more information from consumers and/or trained panelists.
... Traditional lagers and pilsners, such as BC, XX and C, also use noble hop varieties and these contain lower amounts of alpha acids. Top fermentation beers usually use more than one type of hops, while spontaneous fermentation beers use dried and sometimes old oxidized hops to provide more aromas and flavors and, some of them include fruit juice such as cherry, raspberry or blackcurrant, among others (De Keersmaecker, 1996;Gonzalez Viejo, Fuentes, Torrico, Howell, & Dunshea, 2018). However, spontaneous fermentation beers present compounds such as 4-ethylguaiacol usually considered as off-aromas or off-flavors in top and bottom fermentation beers as they are not usually found in them, this compound is produced by Bretanomyces (Thompson-Witrick et al., 2015) and produces smoky, bacon and spicy aromas (Table S1). ...
Article
Identification of volatiles in beer is important for consumers acceptability. In this study, triplicates of 24 beers from three types of fermentation (top/bottom/spontaneous) were analyzed using Gas Chromatograph with Mass-Selective Detector (GC-MSD) employing solid-phase microextraction (SPME). Principal components analysis was conducted for each type of fermentation. Multiple regression analysis, and an artificial neutral network model (ANN) were developed with the peak-areas of 10 volatiles to evaluate/predict aroma, flavor and overall liking. There were no hops-derived volatiles in bottom-fermentation beers, but they were present in top and spontaneous. Top and spontaneous had more volatiles than bottom-fermentation. 4-Ethyguaiacol and trans-β-ionone were positive towards aroma, flavor and overall liking. Styrene had a negative effect on aroma, flavor and overall liking. An ANN model with high accuracy (R=0.98) was obtained to predict aroma, flavor and overall liking. The use of SPME-GC-MSD is an effective method to detect volatiles in beers that contribute to acceptability.
... In carbonated beverages, visual attributes linked to bubbles are directly related to their quality traits. This is due to the relationship between bubbles, and other sensory characteristics of the products, such as mouthfeel, release of aromas, and changes in tastes and flavors [2,[11][12][13][14][15]. The main components in carbonated beverages that determine bubble characteristics, foam formation, and stability are the CO 2 content and its source, as well as some tensioactive or surfactant substances such as proteins and sugars. ...
Article
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Quality control, mainly focused on the assessment of bubble and foam-related parameters, is critical in carbonated beverages due to their relationship with the chemical components as well as their influence on sensory characteristics such as aroma release, mouthfeel, and perception of tastes and aromas. Consumer assessment and acceptability of carbonated beverages are mainly based on carbonation, foam, and bubbles, as a flat carbonated beverage is usually perceived as low quality. This review focuses on three beverages; beer, sparkling water, and sparkling wine. It explains the characteristics of foam and bubble formation, and the traditional methods as well as emerging technologies based on robotics and computer vision to assess bubble and foam-related parameters. Furthermore, it explores the most common methods and the use of advanced techniques using an artificial intelligence approach to assess sensory descriptors both for descriptive analysis and consumers acceptability. Emerging technologies based on the combination of robotics, computer vision, and machine learning as an approach to artificial intelligence have been developed and applied for the assessment of beer and, to a less extent, sparkling wine. This, with the objective of assessing the final products quality using more reliable, accurate, affordable and less time-consuming methods. However, despite carbonated water being an important product due to its increasing consumption, more research needs to focus on exploring more efficient, repeatable, and accurate methods to assess carbonation, and bubble size, distribution and dynamics.
... Another research is conducted for beer foamability [37] where robotics and computer vision techniques are combined with non-invasive consumer biometrics to assess quality traits from beer foamability. Furthermore, in another study [19], an objective predictive model is developed to investigate the intensity levels of sensory descriptors in beer using the physical measurements of colour and foam-related parameters where a robotic pourer, was used to obtain some colour and foam-related parameters from a number of different commercial beer samples. It is claimed that this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications. ...
Preprint
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Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. We investigate the problem by using three swarm intelligence and evolutionary computation techniques that enable brewers to map physico-chemical properties to target organoleptic properties to design a specific brew. While there are several tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the pre-determined quantities of ingredients, the next step is to investigate an automated quantitative ingredient selection approach. The process is illustrated by a number of experiments designing craft beers where the results are investigated by "cloning" popular commercial brands based on their known properties. Algorithms performance is evaluated using accuracy, efficiency, reliability, population-diversity, iteration-based improvements and solution diversity. The proposed approach allows for the discovery of new recipes, personalisation and alternative high-fidelity reproduction of existing ones.
... A recent study explored the development of a machine learning model to predict the pattern and presence of 54 proteins [15]. Furthermore, in another study, an objective predictive model is developed to investigate the intensity levels of sensory descriptors in beer using the physical measurements of colour and foam-related parameters, where a robotic pourer was used to obtain some colour and foam-related parameters from a number of different commercial beer samples [18]. It is also claimed that this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line. ...
Preprint
Food production is a complex process which can benefit from many optimisation approaches. However, there is growing interest in methods that support customisation of food properties to satisfy individual consumer preferences. This paper addresses the personalisation of beer properties. Having identified components of the production process for craft beers whose production tends to be less standardised, we introduce a system which enables brewers to map the desired beer properties into ingredients dosage and combination. Previously explored approaches include direct use of structural equations as well as global machine learning methods. We introduce a framework which uses an evolutionary method supporting multi-objective optimisation. This work identifies problem-dependent objectives, their associations, and proposes a workflow to automate the discovery of multiple novel recipes based on user-defined criteria. The quality of the solutions generated by the multi-objective optimiser is compared against solutions from multiple runs of the method, and those of a single objective evolutionary technique. This comparison provides a road-map allowing the users to choose among more varied options or to fine-tune one of the favourite identified solution. The experiments presented here demonstrate the usability of the framework as well as the transparency of its criteria.
... Furthermore, in another study, an objective predictive model is developed to investigate the intensity levels of sensory descriptors in beer using the physical measurements of colour and foam-related parameters, where a robotic pourer was used to obtain some colour and foam-related parameters from a number of different commercial beer samples [8]. It is claimed that this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line. ...
Article
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Modern computational techniques offer new perspectives for the personalisation of food properties through the optimisation of their production process. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. Furthermore, this work presents a solution discovery method that could be suitable for more complex, industrial setups. An evolutionary computation technique was used to map brewers’ desired organoleptic properties to their constrained ingredients to design novel recipes tailored for specific brews. While there exist several mathematical tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the predetermined quantities of ingredients, this work investigates an automated quantitative ingredient-selection approach. The process, which was applied to this problem for the first time, was investigated in a number of simulations by “cloning” several commercial brands with diverse properties. Additional experiments were conducted, demonstrating the system’s ability to deal with on-the-fly changes to users’ preferences during the optimisation process. The results of the experiments pave the way for the discovery of new recipes under varying preferences, therefore facilitating the personalisation and alternative high-fidelity reproduction of existing and new products.
... A sensory panel of 12 participants from The University of Melbourne (Ethics ID: 1545786.2) was trained using a combination of International Standard methodology (ISO 8586-1: 1993E Sensory analysis-General guidelines for the selection, training, and monitoring of selected assessors and expert sensory assessors, and quality control procedures) [55] and the quantitative descriptive analysis method (QDA ® ). The training details are described in the study published by Gonzalez Viejo et al. [56], using panelists that were regular wine consumers and with training designed using wine samples and references related to red wine. Once the panelists were trained, a blind sensory session was conducted in the sensory laboratory at The University of Melbourne, which consists of individual booths with uniform lighting. ...
Article
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Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial Intelligence (AI) and specifically Machine Learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008-16) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Model 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1; R=0.92; slope=0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2; R=0.98; slope=0.93) and wine color (Model 3; R=0.99; slope=0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.
... Одной из основных задач науки в пивоваренной отрасли является повышение эффективности процессов через внедрение новых инновационных технологий [1][2][3][4][5][6]. Внимание ученых, участвующих в создании нового высокоэффективного оборудования, направлено на снижение энергетических затрат и сокращение продолжительности технологического процесса, сохраняя и улучшая качество и вкус напитка [7][8][9][10][11][12][13]. ...
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Introduction. New innovative technologies make food industry more effective. The present paper introduces a new method of hopped wort production based on novel mash filters. Study objects and methods. The research featured two new designs of mash filters. The study of the mashing process involved malt, hops, drinking water, and beer wort. The research included generally accepted methods of physicochemical and sensory research. Results and discussion. Both models differed from the traditional design. Mash filter I had a cylindrical filtration vat at its bottom with filters in the lower and upper parts of the vat. A pump was installed on the outer side of the steam jacket to produce forced circulation of the liquid medium flow through the vat. The steam jacket was covered with Corundum Classic superfine liquid thermal insulation. Mash filter II had a filtration bottom made of perforated sheet and provided intensive liquid circulation. It also had a regulated mixer that moved the mash, which significantly improved the mashing process. After the mashing, the mash passed through the filtration bottom, separating the liquid phase from the solid phase. The crushed material was discharged through a hatch in bottom. The physicochemical and sensory profiles of the obtained beer wort and beer samples complied with State Standard 30060-93 «Beer. Methods for determination of organoleptic indices and product’s volume». Mash filter II produced beer wort of higher quality and improved the sensory properties of the finished product. This model proved more effective in extracting proteins and digestible sugars during amylolysis due to a better mixing and circulation of liquid medium flow during the wort preparation. Conclusion. The new modified mash filter made it possible to reduce the brewing time by 28.6%. Not only was it more user friendly, but it also was less heat and electricity consuming. In addition, it reduced the production area as it combined the stages of mashing and filtering.
... A similar approach has been used in previous studies [16,17]. The number of samples used for the models is sufficient, considering that the dataset is small enough to avoid having enough power to overfit the model [53,54]. Figure 3 shows significant differences (p < 0.05) between samples in all sensors from the e-nose. ...
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Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to i) classify the samples into low, medium, and high intensity (Model 1) and ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, non-destructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on-time and ensure high-quality products.
... The main use of the PCA in this study was to find relationships between variables and samples as they are constructed using covariance methods as a parameter engineering justification for the ANN modelling presented [51][52][53][54][55]. Besides, cluster analysis helps to visualize the relative grouping of commercial rice samples according to these parameters. This type of analysis to support parameter engineering has been used in several ANN works for food and beverage applications [56][57][58] and helps non-experts in AI or machine-learning understand better the relationships between different parameters from the physicochemical point of view. This type of multivariate data analysis also helps to clarify the "black-box" properties of supervised machine learning such as ANN and to visualize that ANN correctly estimates the targets and that they are not artifacts from non-related inputs. ...
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Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess com-mercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Further-more, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.
... Garre et al. (2020) have studied the role of machine learning algorithms in predicting the deviations in food production and reducing the degree of uncertainty over waste generation. Machine learning approaches have also been studied in the prediction of food sales conditions (Tsoumakas, 2019), rapid and quantitative detection of microbial spoilage through analytical techniques (Ellis et al., 2004), raw food spectroscopic classification (Tsakanikas et al., 2020), quality assurance (Gonzalez Viejo et al., 2018), assessment of energy consumption in the food processing industry (Milczarski et al., 2020), etc. ...
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The food-processing sector is gradually shifting to the industry 4.0 concept, and the potential of disruptive computer-based techniques is indispensable. This chapter focuses on the potential of wireless sensor networks, the Internet of Things, big data, artificial intelligence systems, and other information and communication technologies (ICT) for various applications in the food processing and supply chain. The field of ICT has found numerous applications in this sector and is all set to revolutionize food procurement, manufacturing, and distribution practices. The chapter also provides insights into the scope of automation and digitalization of food systems, particularly in sustainable food processing practices. With several promising applications, the basic concepts and approaches have been explained with examples from recent studies.
... Spontaneous fermentation beers resulted in the highest values for total sugars and lowest alcohol content, bitterness (expressed as IBU), and iso-alpha acids (Figure 1). This may be due to the addition of fruit juice (cherry in LK and raspberry in LF), and dried hops, which may also be old and oxidized to provide aromas and flavors but not bitterness [23,25,40]. ...
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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 (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. 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.
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One of the most significant climatic anomalies, related to climate change that is impacting the wine-growing industry is bushfire events. Grapevine smoke contamination and smoke taint in wines are difficult to assess in the vineyards and wineries. Current assessment methods require berry or wine sample collections and specialised laboratory analysis, which can be time-consuming, cost-prohibitive, and non-representative of the real level of contamination within vineyards. Recently, the Digital Agriculture, Food and Wine group (DAFW) have implemented Artificial Intelligence (AI) based on short and proximal remote sensing and Machine Learning (ML) modelling to assess and monitor smoke contamination and smoke taint in wines. The technology developed has rendered rapid, accurate, and affordable systems to monitor smoke contamination in grapevines, berries, and potential contamination in wines for seven different cultivars. This technology applied to grapevines may be implemented using Unmanned Aerial Vehicles (UAV) and infrared thermal imagery (IRTI) to map regions of vineyards according to smoke contamination levels. Applications to berries and wines using near-infrared spectroscopy (NIR) could offer a quick assessment of the implementation of amelioration techniques to reduce smoke-related compounds in berries and taint in wines. Finally, an electronic nose (e-nose) has been recently developed to assess smoke-related gases in wines to predict smoke taint, and it can be applied to the vineyard to monitor ambient gases and levels of smoke contamination in bushfire events. Further research is required to make these AI applications available to more viticultural regions, grapevine cultivars, and bushfire scenarios.
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Foam stability and retention is an important indicator of beer quality and freshness. A full, white head of foam with nicely distributed small bubbles of CO2 is appealing to the consumers and the crown of the production process. However, raw materials, production process, packaging, transportation, and storage have a big impact on foam stability, which marks foam stability monitoring during all these stages, from production to consumer, as very important. Beer foam stability is expressed as a change of foam height over a certain period. This research aimed to monitor the foam stability of lager beers using image analysis methods on two different types of recordings: RGB and depth videos. Sixteen different commercially available lager beers were subjected to analysis. The automated image analysis method based only on the analysis of RGB video images proved to be inapplicable in real conditions due to problems such as reflection of light through glass, autofocus, and beer lacing/clinging, which make it impossible to accurately detect the actual height of the foam. A solution to this problem, representing a unique contribution, was found by introducing the use of a 3D camera in estimating foam stability. According to the results, automated analysis of depth images obtained from a 3D camera proved to be a suitable, objective, repeatable, reliable, and sufficiently sensitive method for measuring foam stability of lager beers. The applied model proved to be suitable for predicting changes in foam retention of lager beers.
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The aim of this research is to investigate the possibility of applying a laser distance meter (LDM) as a complementary measurement method to image analysis during beer foam stability monitoring. The basic optical property of foam, i.e., its high reflectivity, is the main reason for using LDM. LDM measurements provide relatively precise information on foam height, even in the presence of lacing, and provide information as to when foam is no longer visible on the surface of the beer. Sixteen different commercially available lager beers were subjected to analysis. A camera and LDM display recorded the foam behavior; the LDM display which was placed close to the monitored beer glass. Measurements obtained by the image analysis of videos provided by the visual camera were comparable to those obtained independently by LDM. However, due to lacing, image analysis could not accurately detect foam disappearance. On the other hand, LDM measurements accurately detected the moment of foam disappearance since the measurements would have significantly higher values due to multiple reflections in the glass.
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Purpose The purpose of this paper is to model the relationship between 11 frankfurter physical properties and their sensory scores to classify a release of frankfurter production batches to the market. Design/methodology/approach Data from 209 frankfurter batches were collected. Market batch release classifications were based on 11 physical properties via predictive and direct classification models. The predictive models under study included a regression, backpropagation neural network (BPN) and radial basis function neural network (RBFN) whereas the direct classification models were logistic regression, BPN and RBFN. Model performance was evaluated via correct classification rate. Findings The 11-7-4 RBFN predictive model proved superior with a 90 percent correct classification rate and 0 percent producer risk while the 11-5-1 RBFN, as a classification model, outperformed with the same level of accuracy, 90 and 0 percent, respectively. Producers prefer the less time-consuming direct classifiers for evaluation. Furthermore, the 11-5-1 RBFN direct classifier revealed that color measurement greatly influenced frankfurter batch release. Increases in redness, yellowness and brownness increased batch release probability. Originality/value This research attempts to establish a novel production batch release model for sausage manufacturing. Key factors can then be optimized for improving batch release probability for implementation throughout the sausage industry.
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In the 21st century, the application of technology in the agriculture sector is the area of attention to the researcher. Technology is applied for smart farming in all the different stages, including preparation of soil, sowing, adding manure and fertilizers, irrigation, harvesting, and storage. To date, image processing, machine learning, deep learning, the internet of things, data mining, and wireless sensor networks are employed in the agriculture sector. In this article, we perform a survey of almost 170 articles on which the latest methodologies are applied. Further, we examine the suggested methods and reported advantages and limitations. This chapter aims to provide a brief summary to the researchers who are working in this field. This study results in substantial awareness of the existing expertise gap and identifying possible future research opportunities for smart farming and precision farming.
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Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation forecasts have not yet reached the level of accuracy needed for hydrologic forecasting. Postprocessing of model output to account for local differences can enhance the accuracy and usefulness of these forecasts. Model Output Statistics have performed this important function for a number of years via regression techniques; this paper presents an alternate approach that uses artificial neural networks to produce 6-h precipitation forecasts for specific locations. Tests performed on four locations in the middle Atlantic region of the United States show that the accuracy of the forecasts produced using neural networks compares favorably with those generated using linear regression, especially for heavier precipitation amounts.
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The chemistry of beer flavor instability remains shrouded in mystery, despite decades of extensive research. It is, however, certain that aldehydes play a crucial role because their concentration increase coincides with the appearance and intensity of "aged flavors". Several pathways give rise to a variety of key flavor-active aldehydes during beer production, but it remains unclear as to what extent they develop after bottling. There are indications that aldehydes, formed during beer production, are bound to other compounds, obscuring them from instrumental and sensory detection. Because freshly bottled beer is not in chemical equilibrium, these bound aldehydes might be released over time, causing stale flavor. This review discusses beer aging and the role of aldehydes, focusing on both sensory and chemical aspects. Several aldehyde formation pathways are taken into account, as well as aldehyde binding in and release from imine and bisulfite adducts.
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The objective of this study was to assess the potential for using artificial neural networks (ANN) to predict inspired minute ventilation \( \left( {\dot{V}_{I} } \right) \) during exercise activities. Six physiological/kinematic measurements obtained from a portable ambulatory monitoring system, along with individual’s anthropometric and demographic characteristics, were employed as input variables to develop and optimize the ANN configuration with respect to reference values simultaneously measured using a pneumotachograph (PT). The generalization ability of the resulting two-hidden-layer ANN model was compared with a linear predictive model developed through partial least squares (PLS) regression, as well as other \( \dot{V}_{I} \) predictive models proposed in the literature. Using an independent dataset recorded from nine 80-min step tests, the results showed that the ANN-estimated \( \dot{V}_{I} \) was highly correlated (R 2 = 0.88) with \( \dot{V}_{I} \) measured by the PT, with a mean difference of approximately 0.9%. In contrast, the PLS and other regression-based models resulted in larger average errors ranging from 7 to 34%. In addition, the ANN model yielded estimates of cumulative total volume that were on average within 1% of reference PT measurements. Compared with established statistical methods, the proposed ANN model demonstrates the potential to provide improved prediction of respiratory ventilation in workplace applications for which the use of traditional laboratory-based instruments is not feasible. Further research should be conducted to investigate the performance of ANNs for different types of physical activity in larger and more varied worker populations.
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Beer brewing is an intricate process encompassing mixing and further elaboration of four essential raw materials, including barley malt, brewing water, hops and yeast. Particularly hops determine to a great extent typical beer qualities such as bitter taste, hoppy flavour, and foam stability. Conversely, hop-derived bitter acids account for an offending lightstruck flavour, which is formed on exposure of beer to light. These various processes are presented in detail, while due emphasis is placed on state-of-the-art hop technology, which provides brewers with efficient means to control bitterness, foam, and light-stability thereby allowing for the production of beers with consistent quality.
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The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.
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Deoxynivalenol (DON) was analysed in 313 beer samples collected from the European retail market using a commercially available immunoassay kit (enzyme-linked immunosorbent assay, ELISA). The incidence rate was about 87%, while most samples (73%) had contamination levels lower than 20 ng m(-1). The contamination ranged between 4.0 and 56.7 ng ml(-1), with an average of 13.5 ng ml(-1). A statistically significant correlation between alcohol levels and DON contamination was found, as well as a significant difference between bottom, top and spontaneous fermenting beers. Twenty-seven beer samples were compared using a second ELISA kit and a good correlation was obtained between the two kits (r = 0.93). Although when compared with gas chromatography-mass spectrometry the ELISA tended to overestimate the results, a good correlation (r=0.94) between the two methods was observed. Monitoring of DON in beer is important considering that DON production is dependent on the weather and that it can contribute significantly to the tolerable daily intake of DON, especially for frequent beer consumers.
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This study utilized eye tracking and sensory techniques to evaluate the acceptability of different thermochromic label elements that exhibited colour transitions. Participants (N = 40) evaluated a baby-formula label (baseline) with two trial versions of the same label using colour transitions [Tr1 = colour change in brand name and primary-figure (koala); Tr2 = colour change in secondary-figure (snowflake)]. Labels appeared on a computer-screen (virtual) for 10 s and participants assessed liking of label elements [areas of interest (AOI)] using a 9-point hedonic-scale. Participants also evaluated printed labels (physical). Complete fixation times (CFT) and number of fixations (NOF) from AOIs (brand name "Baby Grow", koala, product-description, snowflake, weight) were assessed. Results showed that figures had higher liking scores compared to brand name (6.47-6.88 vs. 5.76). For eye tracking measurements, the koala had the highest CFT (1622-1689 ms) and NOF (3.4-3.5) for all transitions. Liking of brand (Odd-ratio = 1.7-2.0; Tr1/Tr2) affected preference. The preference of virtual and physical labels did not differ. When colour transition was applied, fixation duration was affected, with gaze being drawn to transition elements. Eye tracking was useful for measuring reactions towards labels with changing colours. A combination of sensory data and eye tracking helped to understand preferences.
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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.
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There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.
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Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of foam quality, including foamability (the ability of the foam to form) drainability (the ability of the foam to resist drainage) and bubble count and allometry. The foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher quality scores from trained panelists were positively correlated with foam stability and negatively correlated with the velocity of foam dissipation and the height of the collar. Significant correlations were observed between the wine quality measurements of total protein, titratable acidity, pH and foam expansion. The percentage of the wine in the foam was found to promote the formation of smaller bubbles and to reduce foamability, while drainability was negatively correlated to foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their foam and bubble characteristics, quality scores and chemical parameters. The technique developed in this study objectively assessed foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and foam parameters obtained automatically, might assist in unraveling factors contributing to wine quality and directions for further research.
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In recent years, the interest on craft beer has been increasingly growing. In this work, sensory traits of five Italian artisanal beers were explored by a trained panel, through different sensory analysis methods: Quantitative Descriptive Analysis (QDA) and a sensory dynamic method, Temporal Dominance of Sensations (TDS). The sensory profiles obtained through these methods were compared to the description given by an expert beer taster. The trained panel (n = 12) evaluated five Tuscan beers, manufactured in Maremma area, Tuscany region, first through QDA. Twenty-eight sensory properties (visual, tactile, flavor and aromatic traits) were evaluated through a nine point scale. The descriptive profile was enriched by a dynamic sensory evaluation method, TDS. TDS was used by panelists to obtain a “real-time” flavor profile of the craft beers. During tasting, TDS provided information on the most striking flavor traits chosen among: floral, honey, roasted, chestnut, spicy, fruity, hoppy, and malty, of each beer. A PCA analysis showed the importance of the flavor attributes for beer profile compared to the expert taster description. Results highlighted the main traits of each beer and showed the validity of different profile methods. The interesting outcomes both provided useful profile patterns for brewers aiming at targeting specific segments of beer market and supported the development of interesting instruments for beer sensory analysis.
Book
Sensory evaluation methods are extensively used in the wine, beer and distilled spirits industries for product development and quality control, while consumer research methods also offer useful insights as the product is being developed. This book introduces sensory evaluation and consumer research methods and provides a detailed analysis of their applications to a variety of different alcoholic beverages. Chapters in part one look at the principles of sensory evaluation and how these can be applied to alcoholic beverages, covering topics such as shelf life evaluation and gas chromatography - olfactometry. Part two concentrates on fermented beverages such as beer and wine, while distilled products including brandies, whiskies and many others are discussed in part three. Finally, part four examines how consumer research methods can be employed in product development in the alcoholic beverage industry.
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Beer brewing is an intricate process encompassing mixing and further elaboration of four essential raw materials, including barley malt, brewing water, hops and yeast. Particularly hops determine to a great extent typical beer qualities such as bitter taste, hoppy flavour, and foam stability. Conversely, hop-derived bitter acids account for an offending lightstruck flavour, which is formed on exposure of beer to light. These various processes are presented in detail, while due emphasis is placed on state-of-the-art hop technology, which provides brewers with efficient means to control bitterness, foam, and light-stability thereby allowing for the production of beers with consistent quality.
Article
Fourier transform near-infrared (FT-NIR) spectroscopy can be used as a rapid method to measure the percentage of sugar and to discriminate between different must samples in terms of their free amino nitrogen (FAN) values. It can also be used as a rapid method to discriminate between Chardonnay wine samples in terms of their malolactic fermentation (MLF) status. By monitoring the conversion of malic to lactic acid, the samples could be classified on the basis of whether MLF has started, is in progress or has been completed. Furthermore, FT-NIR spectroscopy can be used as a rapid method to discriminate between table wine samples in terms of their ethyl carbamate (EC) content. It is claimed that high concentrations of ethyl carbamate in wine can pose a health threat and has to be monitored by determining the EC content in relation to the regulatory limits set by authorities. For each of the above-mentioned parameters QUANT+™ methods were built and calibrations were derived and it was found that a very strong correlation existed in the sample set for the FT-NIR spectroscopic predictions of the percentage of sugar (r = 0.99, SEP= 0.31 °Brix). However, the correlation for the FAN predictions (r = 0.602, SEP= 272.1 g.L-1), malic acid (r = 0.64, SEP= 1.02 g.L-1), lactic acid (r = 0.61, SEP= 1.35 g.L-1) and EC predictions (r = 0.47, SEP = 3.6 μg.kg·1) were not good. The must samples could be classified in terms of their FAN values when Soft Independent Modelling by Class Analogy (SIMCA) diagnostics and validation were applied as a discriminative method, with recognition rates exceeding 80% in all cases. When SIMCA diagnostics and validation were applied to the Chardonnay and EC wine samples, recognition rates exceeding 88% and 80% respectively were obtained. These results therefore confirm that this method is successful in discriminating between samples.
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An Artificial Neural Network (ANN) methodology was employed to forecast daily runoff as a function of daily precipitation, temperature, and snowmelt for the Little Patuxent River watershed in Maryland. The sensitivity of the prediction accuracy to the content and length of training data was investigated. The ANN rainfall-runoff model compared favorably with results obtained using existing techniques including statistical regression and a simple conceptual model. The ANN model provides a more systematic approach, reduces the length of calibration data, and shortens the time spent in calibration of the models. At the same time, it represents an improvement upon the prediction accuracy and flexibility of current methods.
Article
Two bottles of beer from an about 170 year old shipwreck (M1 Fö 403.3) near the Åland Islands in the Baltic Sea were analysed. Hop components and their degradation compounds showed that the bottles contained two different beers, one more strongly hopped than the other. The hops used contained higher levels of β-acids than modern varieties and were added before boiling the worts, converting α-acids to iso-α-acids and β-acids to hulupones. High levels of organic acids, carbonyl compounds and glucose indicated extensive bacterial and enzyme activity during aging. However, concentrations of yeast-derived flavor compounds were similar to those of modern beers, except that 3-methylbutyl acetate was unusually low in both beers and 2-phenylethanol and possibly 2-phenylethyl acetate were unusually high in one beer. Concentrations of phenolic compounds were similar to those in modern lagers and ales.
Article
The aim of the present work was to evaluate data aggregation when using two polarized sensory positioning (PSP) approaches for sensory characterization with consumers. Two consumer studies with different product categories (orange‐flavored powdered drinks and chocolate milk beverages) were carried out. In each study two PSP approaches were considered: PSP with scales and triadic PSP (t‐PSP). For each approach, one‐third of the consumers evaluated the whole sample set, whereas the other two‐thirds evaluated the sample set split in two subsets. Results showed that sample configurations for the evaluation of the whole and the split set by different consumer groups were relatively well correlated (RV coefficients higher than 0.79). However, agreement between the configurations differed between the studies, which can be explained by the degree of difference among samples. Besides, differences in consumers' dissimilarity scores and conclusions regarding similarities and differences among samples were identified when comparing both data sets (with and without data aggregation). Regarding the comparison of the two PSP approaches, in the two studies better agreement between sample configurations was obtained for t‐PSP. However, in one of the studies PSP with scales provided better results for the evaluation of a repeated sample by different consumer groups.
Article
Most acetic acid found in beer is produced by yeast during fermentation. It contributes significantly to beer taste, especially when its content is higher than the taste threshold in beer. Therefore, the control of its content is very important to maintain consistent beer quality. In this study, artificial neural networks and support vector machine (SVM) were applied to predict acetic acid content at the end of a commercial-scale beer fermentation. Relationships between beer fermentation process parameters and the acetic acid level in the fermented wort (beer) were modelled by partial least squares (PLS) regression, back-propagation neural network (BP-NN), radial basis function neural network (RBF-NN) and least squares-support vector machine (LS-SVM). The data used in this study were collected from 146 production batches of the same beer brand. For predicting acetic acid content, LS-SVM and RBF-NN were found to be better than BP-NN and PLS. For the comparison of RBF-NN and LS-SVM, RBF-NN had a better reliability of model, but lower reliability of prediction. SVM had better generalization, but lower reliability of model. In summary, LS-SVM was better than RBF-NN modelling for the prediction of acetic acid content during the commercial beer fermentation in this study. Copyright © 2013 The Institute of Brewing & Distilling
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Unlike many alcoholic beverages beer is inherently unstable. In chemical (as opposed to microbiological) terms this instability can be considered — and is here reviewed — in the categories of colloidal instability, foam, gushing, flavour instability and light sensitivity
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A combination of near infrared spectroscopy (NIR) instrumental measurements and sensory analysis was investigated to predict solids soluble content (SSC, assessed as Brix) and to classify preference in table grape cv Italia. SSC was monitored in each berry of whole bunches in order to evaluate intra-bunch distribution and variability. NIR spectra were recorded in the spectral region 12,000–4000 cm−1 (833–2500 nm) using a set of 682 berries. The Partial Least Square (PLS) model based on cross-validation provided acceptable value for the main statistical parameters (coefficient of determination of cross-validation, r2: 0.85; standard error of cross-validation, SECV: 1.08; residual predictive deviation, RPD: 2.6) and was confirmed by external validation performed with 115 independent berries (coefficient of determination of prediction, rp2: 0.82; standard error of prediction, SEP: 0.83). For consumer testing, the selected PLS model was used to predict the Brix value in 400 berries and Discriminant Analysis (DA) was then carried out to classify berries in terms of preference by relating NIR data to consumer judgment. The three defined preference clusters of berries were fully classified obtaining 100% membership. In cross-validation the value decreased especially for class 1 (78.5%) and 3 (75%) whereas class 2 obtained comparable values (98.7%). According to our results, NIR technology appears to be a promising technique for predicting SSC and obtaining information with regard to consumer preference in ‘Italia’ table grape for application of efficient and low cost on-line instruments in the fruit industry.
Article
The attractive flavor of beer changes rapidly upon storage and limits the shelf life of this beverage. This degradation is due to several factors that may be associated with the vulnerability of iso-alpha-acids to the light and the oxidation of iso-alpha-acids. These compounds are responsible for the beer bitterness and its characteristic flavor. Apart from adequate beer packaging, there are several methods which can minimize the degradation of iso-alpha-acids: addition of phenolic compounds with antioxidant properties, addition of pure stereoisomers cis-iso-alpha-acids or their reduced species, or use of riboflavin-binding proteins.
Article
The near-infrared set-up based on simultaneous detection of four wavelengths was applied for in-line moisture measurement during fluid bed granulation. In addition to the spectral response, several other process measurements describing the state of the granulation were evaluated. The near-infrared moisture measurement is disturbed by the variation in physical properties of the sample (e.g., temperature, particle size, bulk density). The factors explaining the non-linearity of spectral response during different phases of granulation could be extracted. Combining all this process information improved the prediction capability of the multivariate calibration models tested (partial least squares and artificial neural network (ANN)). The back-propagation neural network approach was found to have most predictive power with the independent test data.
Article
Napping® is an inexpensive and rapid method for sensory characterization, suitable for both trained and untrained subjects. In the study presented, the method was applied on 9 specialty beers. Subjects were 17 consumers without any training as sensory panelists, of whom 8 were beer experts and 9 novices. The aim was to explore the usability of the Napping® method by untrained consumers and to analyze differences between beer novices and experts in their ability to discriminate and describe the products. The method succeeded in discriminating between the beers, revealing sensory descriptors responsible for the differences. Analysis of differences between the two groups showed that the experts had higher agreement with regard to sample differences (significantly higher mean RV-coefficient, 0.61 vs. 0.41 for non-experts, p = 0.013). The results support the usability of Napping® as a fast method for sensory characterization, with the advantage of providing a product characterization based on consumer descriptions, thus better reflecting consumers' experience with the product.
Article
The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRSCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the Identification of candidate targets for therapy.
Article
The influence of hydrophobic polypeptides concentrated in beer foam, together with the composition of iso-alpha acids and the content of malto-oligosaccharides in beer on foam stability, has been investigated. The objective was to find out whether a shortage of one of these positive contributors to foam stability could be compensated for by an increased presence of another or whether optimum levels of each contributor is necessary. For that purpose, an image analysis method to evaluate beer foam quality was developed. The foam collapse time was the parameter chosen to group beers according to their foam stability. Profiles of hydrophobic polypeptides that concentrate in beer foam, iso-alpha acids, and malto-oligosaccharides of 14 beer brands were acquired by high-performance liquid chromatography. Principal component analysis (PCA) was performed to show the relationship between beer brands and its composition. Beers that contained propylene glycol alginate as a foam enhancer showed high foam stability except for one beer, which had a low content of hydrophobic polypeptides, thereby highlighting the requirement of threshold levels of hydrophobic polypeptides to obtain stable foam. The data of samples that were devoid of a foam additive were subjected to a discriminant statistical analysis. Foam stability declined in proportion to decreases in hydrophobic polypeptides and to a lesser extent to decreases in iso-alpha-acid contents. Apparently, the content of malto-oligosaccharides were found to have no major influence on foam stability. The model of discriminate analysis was found to explain 100% of the variance in data with 85.2% success in classifying all samples according to the model, suggesting that foam stability is mainly governed by the beer constituents evaluated in this study.
Article
Laboratory wheat beers were brewed with different wheat varieties of different protein content (8.7–14.4%) and with five different barley malts, varying in degree of modification (soluble protein: 3.9–6.9%). In a first series of experiments, it was investigated whether wheat positively influences the foam stability, a major characteristic of wheat beers. NIBEM and Rudin (CO2) foam analyses revealed that the effect of wheat on foam stability depended on the barley malt used for brewing. When using malt with high foaming potential, wheat exerts a negative influence. However, wheat added to over-modified malt with less foam promoting factors, ameliorates beer foaming characteristics proving that wheat contains foam active compounds. In addition, Rudin (N2) values suggested that wheat positively influences foam stability by decreasing liquid drainage, probably caused by a higher beer viscosity and/or a finer foam bubble size distribution. Furthermore, the haze in wheat beers, which is another important quality characteristic of these beers, was investigated. Permanent haze readings of the 40% wheat beers were lower than 1.5 EBC haze units. For 20% wheat beers, an inverse relation between the permanent haze (9.4–19.3 EBC haze units) and the protein content of the wheat was established. The barley malt used for brewing also influenced permanent haze readings. A positive correlation between the modification degree of the malt and the permanent haze intensity was found. It was concluded that the choice of raw materials for wheat beer brewing considerably influences the visual properties of the beer.
Article
The biochemical pathways involved in the production of ethyl caproate, a secondary product of the beer fermentation process, are not well established. Hence, there are no phenomenological models available to control and predict the production of this particular compound as with other related products. In this work, neural networks have been used to fit experimental results with constant and variable pH, giving a good fit of laboratory and industrial scale data. The results at constant pH were also used to predict results at variable pH. Finally, the application of neural networks obtained from laboratory experiments gave excellent predictions of results in industrial breweries and so could be used in the control of industrial operations. The input pattern to the neural network included the accumulated fermentation time, cell dry weight, consumption of sugars and aminoacids and, in some cases, the pH. The output from the neural network was an estimation of quantity of the ethyl caproate ester.
Article
In this study, the protein and peptide fractions of two commercial Italian barley malt beers, made with different processes by the same producer, have been analysed with a combined immunochemical and mass spectrometry approach. The “gluten” content of beer samples, measured with the R5 monoclonal antibody, was below the caution limit proposed by the Codex Alimentarius for gluten-free foods. The proteomic approach allowed to identify a 17 kDa avenin-like protein partially homologous to hordeins, that was particularly abundant in foam, in addition to the already reported barley albumins (Z4-barley and ns-LTPs) and to minor amounts of yeast glycolytic enzymes. No intact hordeins were detected, although fragments derived from γ3- and B-hordein were present. In consideration of the many implications of the protein/peptide pattern, these data provide useful information to improve quality and safety of beer.
Article
Five physically based models for predicting liquid saturation from light transmission in 2D laboratory systems containing translucent porous media were developed and tested (Models A–E). The models were based upon various simplifying assumptions concerning pore geometry, wettability, and drainage. Models A–D assumed uniform-sized pores, and liquid saturation was an explicit function of light transmission. Model E considered a distribution of pore sizes whose drainage characteristics were inferred from the Laplace equation. Mass balances were calculated using data from drainage and infiltration experiments, in four textures of silica sand with water as the fluid. Model E performed the best overall, with systematic errors of less than 2.3% saturation. Model E represents a robust easily applied new method for the determination of liquid saturation by light transmission. The other four models are presented, and compared, for reasons of historical interest and to investigate the impact of the various simplifying assumptions.
Article
Beer proteins were analysed by two-dimensional gel electrophoresis (2DE). The protein species associated with major spots on 2DE gels were identified by mass spectrometry followed by a database search to construct a comprehensive beer proteome map. As a result, 85 out of 199 protein spots examined were positively identified and categorised into 12 protein species. A total of 11 beer samples were brewed from the malt of eight cultivars having different levels of protein modification. This experiment was designed to demonstrate the influences of barley cultivar and malt modification on beer protein composition and beer quality characters. The beers produced from these brewing trails were subsequently analysed by 2DE and their proteomes were compared. Cultivars and malt modification affected the concentration of several proteins in beer. Beer protein concentration was associated with differences in the desirable beer quality trait, foam stability. In addition, expression of yeast derived proteins were observed that may also influence beer quality. Overall, the application of a comprehensive beer proteome map provides a strong platform for detection and potential manipulation of beer quality related proteins.
Article
A study of the feasibility of near infrared reflectance spectroscopy (NIRS) for analytical monitoring in wineries is presented, in which equations for the determination or screening of the commonest enological parameters are proposed. The training and validation sets to develop NIR general equations were built with samples (180) from different apellation d’origine, different wine types, etc. By the calibration step (partial least squares regression and cross-validation were used for multivariate calibration), major components such as ethanol, volumic mass, total acidity, pH, glycerol, colour, tonality and total polyphenol index are accurate determined by the proposed equations as compared with the reference data obtained by the official and standard methods—determination coefficients (R2) were higher than 0.800 (and higher than 0.900 most times) and standard error cross-validation (SECV) values were close to those of the reference methods. The proposed method also offers screening capability for components such as volatile acidity (R2 = 0.481), organic acids (R2 = 0.432 for malic acid, R2 = 0.544 for tartaric acid, R2 = 0.541 for gluconic acid)—with the exception of the accurate determination of lactic acid (0.860 and 0.35 g l−1 for R2 and SECV, respectively)—reducing sugars (R2 = 0.705) and total sulphur dioxide (R2 = 0.615). In equations validation, the correlation between the reference and NIRS methods was tested, and slope and bias values statistically not different from 1 and 0, respectively, were obtained for most parameters.
Article
The present work reviews and critically discusses the aspects that influence yeast flocculation, namely the chemical characteristics of the medium (pH and the presence of bivalent ions), fermentation conditions (oxygen, sugars, growth temperature and ethanol concentration) and the expression of specific genes such as FLO1, Lg-FLO1, FLO5, FLO8, FLO9 and FLO10. In addition, the metabolic control of loss and onset of flocculation is reviewed and updated. Flocculation has been traditionally used in brewing production as an easy and off-cost cell-broth separation process. The advantages of using flocculent yeast strains in the production of other alcoholic beverages (wine, cachaça and sparkling wine), in the production of renewal fuels (bio-ethanol), in modern biotechnology (production of heterologous proteins) and in environmental applications (bioremediation of heavy metals) are highlighted. Finally, the possibility of aggregation of yeast cells in flocs, as an example of social behaviour (a communitarian strategy for long-time survival or a means of protection against negative environmental conditions), is discussed.
Article
Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models "Trappist vs. non-Trappist beers" with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and "Rochefort 8 vs. the rest" with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach.
Article
The negative effect of fatty acids on the foam stability of beer has been assessed. Long-chain fatty acids are far more damaging than short-chain fatty acids on the foam stability of beer at the concentrations employed. Polypeptides have been isolated from an all malt beer by hydrophobic interaction chromatography. Using this technique five groups of polypeptides were isolated, group 1 being the least hydrophobic and group 5 the most hydrophobic, all of which exhibited similar polypeptide compositions by SDS-PAGE. All five hydrophobic polypeptide groups bound [(14)C]linoleic acid; however, group 5, the most hydrophobic group, bound the most linoleic acid. Groups 1 and 5 were titrated with cis-parinaric acid (CPA) to produce binding curves, which were compared with a binding curve obtained for bovine serum albumin (BSA). Groups 1 and 5 both produced binding curves that saturated at approximately 5.5 microM and 4 microM CPA and had association constants (K(a)) of 6.27 x 10(7) and 1.62 x 10(7) M(-1), respectively. In comparison, BSA produced a binding curve that saturated at 6 microM CPA and had a K(a) of 3.95 x 10(7) M(-1). Further investigation has shown that group 1 is pH sensitive and group 5 pH insensitive with respect to lipid binding. The lipid-binding activity of group 5 was also shown to be unaffected by ethanol concentration. Linoleic acid (5 microM) when added to beer resulted in unstable foam. Group 5 was added to the lipid-damaged beer and was shown to restore the foam stability to values that were obtained for the control beer. It has therefore been demonstrated that proteins isolated from beer have a lipid-binding capacity and that they can convey a degree of protection against lipid-induced foam destabilization.
Article
The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.
Article
The foam stability of beer is one of the important key factors in evaluating the quality of beer. The purpose of this study was to investigate the relationship between the level of malt modification (degradation of protein, starch, and so on) and the beer foam stability. This was achieved by examining foam-promoting proteins using two-dimensional gel electrophoresis (2DE). We found that the foam stability of beer samples brewed from the barley malts of cultivars B and C decreased as the level of malt modification increased; however, the foam stability of cultivar A did not change. To identify the property providing the increased foam stability of cultivar A, we analyzed beer proteins using 2DE. We analyzed three fractions that could contain beer foam-promoting proteins, namely, beer whole proteins, salt-precipitated proteins, and the proteins concentrated from beer foam. As a result, we found that in cultivar A, some protein spots did not change in any of these three protein fractions even when the level of malt modification increased, although the corresponding protein spots in cultivars B and C decreased. We analyzed these protein spots by peptide mass finger printing using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. As a result, all of these spots were identified as barley dimeric alpha-amylase inhibitor-I (BDAI-I). These results suggest that BDAI-I is an important contributor to beer foam stability.