•A low-cost portable e-nose was developed integrating nine different gas sensors.•The e-nose was able to effectively discriminate different beer samples from gases.•An accurate machine learning model ...was developed to predict beer aromas.•Another accurate machine learning model was able to predict sensory characteristics.•This method is cost-effective, rapid, reliable and accurate applicable for other foods.
The assessment of aromas in beer is critical to assess its quality since it could be used as an indicator of contamination or faults, which will directly influence consumers’ acceptability. Traditional techniques to evaluate aromas are time-consuming, require special training, costly equipment, and trained personnel. Therefore, this study aimed to develop a portable, low-cost electronic nose (e-nose) coupled with machine learning modeling to predict aromas in beer. Nine different gas sensors were used i) ethanol, ii) methane, iii) carbon monoxide, iv) hydrogen, v) ammonia/alcohol/benzene, vi) hydrogen sulfide, vii) ammonia, viii) benzene/alcohol/ammonia and ix) carbon dioxide. Output data were assessed for significant differences using ANOVA and least significant differences as post hoc test (α = 0.05). Two artificial neural network (ANN) models were also developed to predict i) the peak area of 17 different volatile aromatic compounds (Model 1) obtained from gas chromatography–mass spectroscopy (GC–MS) and ii) the intensity of ten sensory descriptors acquired from a sensory session with 12 trained panelists. Results from the ANOVA showed that there were significant differences between the samples used, which showed that the e-nose was able to discriminate samples. The resulting ANN models were highly accurate with correlation coefficients of R = 0.97 (Model 1) and R = 0.93 (Model 2). The combined method using the developed e-nose and the ANN models could be used by the industry as a low-cost, rapid, reliable and effective technique for beer quality assessment within the production line. This may also be calibrated for its use in other foods and beverages.
The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on ...the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94-96%; 92-97%, respectively) and white wines (96-97%; 90-97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.
This research evaluated the effects of product familiarity on the sensory acceptability and physiological responses of consumers toward different food stimuli using two populations (Asian vs. ...Western). Two studies were conducted: (1) an online questionnaire and (2) a tasting session. For (1), n = 102 (60% Asians and 40% Westerners) evaluated 31 food items visually for familiarity and liking whereas for (2), participants (n = 60; 48% Asians and 52% Westerners) evaluated 10 different foods (tortoise jelly, chili slices, beef jerky, dried tofu, Vegemite®, durian cake, octopus chips, chocolate, corn chips, and wasabi coated peas) by tasting for familiarity and liking (visual/aroma/taste/texture/overall). A novel Android® app (Bio-sensory App) was used to capture sensory and non-invasive physiological responses (temperature, heart rate and facial expressions) of consumers. In (1), Asian and Western participants differed in their familiarity scores, visual liking ratings, and the selection of emotion terms for the stimuli. In (2), cultural differences affected familiarity and the liking scores of appearance, aroma, taste and texture of the products. While food stimuli marginally affected the physiological responses of consumers for both cultures, Asian participants elicited higher temperature values compared to those of Westerners. Both studies (1 and 2) showed that familiarity of food products was positively associated to sensory liking for both cultural groups. These findings are useful to understand consumers acceptability based on both sensory and physiological responses.
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•Asians and Westerners differed in their familiarity scores and visual liking of foods.•Asians expressed neutral to negative emotions toward familiar foods.•Western participants expressed positive emotions toward familiar foods.•Cultural differences and familiarity affected the liking of the tasted products.•Asians elicited higher temperature values when tasting than Westerners.
Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up ...to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.
Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ...ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities' green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (
= 172 trees) to obtain tree growth in the form of effective leaf area index (
) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016-2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of
and TWSI to monitor the effects of climate change on the GI of cities.
•No hops-derived volatiles were found in bottom fermented beer samples.•Top and bottom fermented beers presented more volatiles than spontaneous.•4-Ethyguaiacol and trans-β-ionone were positive ...towards overall liking of beers.•Styrene had a negative and significant effect on consumer liking of beers.•A highly accurate machine learning model to predict acceptability was obtained.
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.
Foam-related parameters greatly influence other sensory attributes of beer such as aromas, flavors and mouthfeel; therefore, the visual assessment of beers is one of the most important quality ...traits, since it creates the first impression of consumers in determining the willingness to taste the product and perceived quality. Sensory analysis has been extensively used to assess consumers acceptability; however, this can only obtain their self-reported conscious responses. Therefore, biometric techniques have been used to assess the subconscious responses, which provide more information from consumers when integrated with sensory evaluation questionnaires. In this study, non-invasive biometrics along with a sensory questionnaire were used to assess consumers perception to visual attributes of 15 beers from pouring videos obtained using the RoboBEER (automatic robotic pourer). The sensory session was conducted with 30 participants using an integrated camera system, which consists of an infrared thermal camera and video recording coupled with a Bio-sensory computer application (App) and an eye tracking device. Objective physical parameters from beer pouring were obtained using the RoboBEER and computer vision algorithms. Results from the Just About Right (JAR) and acceptance tests showed that consumers preferred top fermentation beers, which have a medium foam height and stability, and tend to highly penalize bottom fermentation beers with lower foam. The principal components analysis explained a total of 52% of data variability. A correlation matrix was developed to assess significant correlations between the conscious, subconscious and physical data such as the positive correlation between perceived quality and heart rate, and the negative correlation between foam stability liking and foam drainage. Furthermore, an artificial neural network model (ANN) with 82% accuracy was developed using 28 parameters from the subconscious and objective physical data as inputs to classify beers per participant according to their level of liking of foam height (low and high). The combined use of these techniques showed to be an accurate and rapid tool to assess the visual sensory perception of beers based on the RoboBEER and biometric outputs from consumers with significant potential applications for fast screening within the beer industry.
•Performed sensory evaluation of beer visual acceptability based on foam parameters.•Non-invasive biometrics applied to assess consumers visual perception of beer.•Biometrics and RoboBEER data used to classify foam liking with machine learning.•Significant correlations found between subconscious, conscious and objective data.
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