School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemAssessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers(WILEY, 2018-05)UNLABELLED: 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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ItemEffects of Three Irrigation Strategies on Gas Exchange Relationships, Plant Water Status, Yield Components and Water Productivity on Grafted Carmenere Grapevines(FRONTIERS MEDIA SA, 2018-07-12)In the Chilean viticultural industry, Carménère is considered an emblematic cultivar that is cultivated mainly in arid and semi-arid zones. For this reason, it is necessary to use precise irrigation scheduling for improving water use efficiency (WUE), water productivity (WP), yield and wine quality. This study evaluated the effects of three deficit irrigation strategies on gas exchange variables, WUE, WP and yield components in a drip-irrigated Carménère vineyard growing under semi-arid climatic conditions during two consecutive seasons (2011/12 and 2012/13). The irrigation strategies were applied in completely randomized design from fruit set (S) to harvest (H). The first irrigation strategy (T1) involved continuous irrigation at 100% of actual evapotranspiration (ETa) from S to the veraison (V) period and at 80% of ETa from V to H. The second irrigation strategy (T2) involved irrigation at 50% of ETa from S to H and the third one (T3) involved no-irrigation from S to V and at 30% of ETa from V to H. The results indicated that there was a significant non-linear correlation between net CO2 assimilation (AN) and stomatal conductance (gs), which resulted in three zones of water stress (zone I = gs > 0.30 mol H2O m-2s-1; zone II = between 0.06 and 0.30 mol H2O m-2s-1; and zone III = gs < 0.06 mol H2O m-2s-1). The use of less water by T2 and T3 had a significant effect on yield components, with a reduction in the weight and diameter of grapes. A significant increase in WP (7.3 kg m-3) occurred in T3, which resulted in values of WUE that were significantly higher than those from T1 and T2. Also, a significant non-linear relationship between the integral water stress (SIΨ) and WP (R2 = 0.74) was established. The results show that grafted Carménère vines were tolerant to water stress although differences between cultivars/genotypes still need to be evaluated.
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ItemThe Influence of Apical and Basal Defoliation on the Canopy Structure and Biochemical Composition of Vitis vinifera cv. Shiraz Grapes and Wine(FRONTIERS MEDIA SA, 2017-07-07)Defoliation is a commonly used viticultural technique to balance the ratio between grapevine vegetation and fruit. Defoliation is conducted around the fruit zone to reduce the leaf photosynthetic area, and to increase sunlight exposure of grape bunches. Apical leaf removal is not commonly practiced, and therefore its influence on canopy structure and resultant wine aroma is not well-studied. This study quantified the influences of apical and basal defoliation on canopy structure parameters using canopy cover photography and computer vision algorithms. The influence of canopy structure changes on the chemical compositions of grapes and wines was investigated over two vintages (2010-2011 and 2015-2016) in Yarra Valley, Australia. The Shiraz grapevines were subjected to five different treatments: no leaf removal (Ctrl); basal (TB) and apical (TD) leaf removal at veraison and intermediate ripeness, respectively. Basal leaf removal significantly reduced the leaf area index and foliage cover and increased canopy porosity, while apical leaf removal had limited influences on canopy parameters. However, the latter tended to result in lower alcohol level in the finished wine. Statistically significant increases in pH and decreases in TA was observed in shaded grapes, while no significant changes in the color profile and volatile compounds of the resultant wine were found. These results suggest that apical leaf removal is an effective method to reduce wine alcohol concentration with minimal influences on wine composition.
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ItemNon-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate(MDPI, 2018-06)Traditional methods to assess heart rate (HR) and blood pressure (BP) are intrusive and can affect results in sensory analysis of food as participants are aware of the sensors. This paper aims to validate a non-contact method to measure HR using the photoplethysmography (PPG) technique and to develop models to predict the real HR and BP based on raw video analysis (RVA) with an example application in chocolate consumption using machine learning (ML). The RVA used a computer vision algorithm based on luminosity changes on the different RGB color channels using three face-regions (forehead and both cheeks). To validate the proposed method and ML models, a home oscillometric monitor and a finger sensor were used. Results showed high correlations with the G color channel (R² = 0.83). Two ML models were developed using three face-regions: (i) Model 1 to predict HR and BP using the RVA outputs with R = 0.85 and (ii) Model 2 based on time-series prediction with HR, magnitude and luminosity from RVA inputs to HR values every second with R = 0.97. An application for the sensory analysis of chocolate showed significant correlations between changes in HR and BP with chocolate hardness and purchase intention.
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ItemChocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling(MDPI AG, 2019-10-01)Chocolates are the most common confectionery and most popular dessert and snack across the globe. The quality of chocolate plays a major role in sensory evaluation. In this study, a rapid and non-destructive method was developed to predict the quality of chocolate based on physicochemical data, and sensory properties, using the five basic tastes. Data for physicochemical analysis (pH, Brix, viscosity, and color), and sensory properties (basic taste intensities) of chocolate were recorded. These data and results obtained from near-infrared spectroscopy were used to develop two machine learning models to predict the physicochemical parameters (Model 1) and sensory descriptors (Model 2) of chocolate. The results show that the models developed had high accuracy, with R = 0.99 for Model 1 and R = 0.93 for Model 2. The thus-developed models can be used as an alternative to consumer panels to determine the sensory properties of chocolate more accurately with lower cost using the chemical parameters
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ItemNon-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach(MDPI, 2019-08-01)Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).
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ItemConsumer Acceptability, Eye Fixation, and Physiological Responses: A Study of Novel and Familiar Chocolate Packaging Designs Using Eye-Tracking Devices(MDPI, 2019-07-01)Eye fixations on packaging elements are not necessarily correlated to consumer attention or positive emotions towards those elements. This study aimed to assess links between the emotional responses of consumers and the eye fixations on areas of interest (AOI) of different chocolate packaging designs using eye trackers. Sixty participants were exposed to six novel and six familiar (commercial) chocolate packaging concepts on tablet PC screens. Analysis of variance (ANOVA) and multivariate analysis were performed on eye tracking, facial expressions, and self-reported responses. The results showed that there were significant positive correlations between liking and familiarity in commercially available concepts (r = 0.88), whereas, with novel concepts, there were no significant correlations. Overall, the total number of fixations on the familiar packaging was positively correlated (r = 0.78) with positive emotions elicited in people using the FaceReader™ (Happy), while they were not correlated with any emotion for the novel packaging. Fixations on a specific AOI were not linked to positive emotions, since, in some cases, they were related to negative emotions elicited in people or not even associated with any emotion. These findings can be used by package designers to better understand the link between the emotional responses of consumers and their eye fixation patterns for specific AOI.
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ItemDevelopment of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists(MDPI, 2018-09)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 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 modeling 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 nonfood products to obtain more information from consumers and/or trained panelists.
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ItemEffects of packaging design on sensory liking and willingness to purchase: A study using novel chocolate packaging(ELSEVIER SCI LTD, 2019-06)Packaging is the first impression consumers have of food products which determines likelihood of purchasing. Therefore, the objective of this study was to evaluate the effect of chocolate packaging design on sensory liking and willingness to purchase (WTP) of consumers (n = 75) under three conditions:(1) blind [product], (2) packaging, and (3) informed [product and packaging]. The same chocolate tasted in (1) was wrapped in six different packaging concepts (bold, fun, every day, special, healthy, premium) developed based on TNS NeedScope™ model for (3). There were significant differences in liking towards taste based on packaging. Liking scores for (3) reduced when expectations created by packaging were not met. Regression analysis explained, taste had strongest association (r = 0.73) towards WTP. Cochran's Q and McNemar tests showed significant differences in frequencies of emotion-based terms between packaging and informed conditions. These findings can be used in product design to evaluate product attributes by enhancing emotional attachment towards chocolate.