School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemIncreasing Oil Concentration Affects Consumer Perception and Physical Properties of Mayonnaise-type Spreads Containing KCl(WILEY, 2017-08)Reducing sodium intakes remains a global challenge for the food industry. KCl is a potential salt substitute but imparts bitterness when used at high concentrations. Little is known about how oil concentrations (OC) affect consumers' perception of saltiness and bitterness in emulsion products such as mayonnaise containing KCl. We evaluated consumers' perception and physical properties of mayonnaise-type spreads at various oil and tastant (NaCl or KCl) concentrations. Consumers (N = 306) evaluated saltiness, bitterness, overall taste liking (OTL) and purchase intent (PI). Viscosity, pH, water activity, and consistency/texture were also measured. Oil and tastant (NaCl or KCl) concentrations had significant effects on saltiness, viscosity, and pH. As OC increased, saltiness intensity slightly decreased for spreads. Increasing oil concentration increased viscosity. Generally, spreads containing KCl had higher bitterness and pH than spreads containing NaCl. All spreads containing KCl were penalized for being "too bitter." PI was affected by OTL for all spreads but OC was also a significant factor in the purchase decision of spreads containing NaCl. This study demonstrated that increasing OC affected consumers' taste perception (saltiness and bitterness) and spreads' physical properties including pH and viscosity.
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ItemTaste perception and purchase intent of oil-in-water spreads: effects of oil types and salt (NaCl or KCl) concentrations(WILEY, 2017-10)Associations of sodium intake with heart‐related problems are creating awareness towards reducing sodium. Potassium chloride (KCl), a substitute for sodium chloride (NaCl), has the disadvantage of imparting bitterness at high concentrations. We evaluated physical characteristics, taste perception and purchase intent of KCl and NaCl in oil‐in‐water spreads/emulsions composed by olive, rice bran and soya bean oils. Consumers (N = 300) evaluated saltiness/bitterness of emulsions prepared with 65% oil, and NaCl (0.5% and 1.0%) or KCl (0.75% and 1.5%). Olive oil spreads (104.07–107.43 Pa s) had higher viscosity compared to other spreads (59.16–74.96 Pa s). Type of oil had significant effects on bitterness, overall taste liking and viscosity. Taste liking decreased due to bitterness of olive oil spreads (mean drop=1.72–2.43). Purchase intent was positively associated with saltiness and pH and increased with oil claims (increase = 1.3%–22.1%) compared to sodium claims (increase = 0.0%–12.9%). These findings are useful for understanding taste perception of emulsions.
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ItemAssessment of the ability of five culture media for the detection of Escherichia coli O157(WILEY-BLACKWELL, 2016-08)Summary Cattle are a common reservoir for Escherichia coli O157:H7. Prior to confirming its presence in a sample, proper isolation of E. coli O157 is necessary. Consequently, this study evaluated the ability of five commercial plating media to isolate E. coli O157 from 138 samples of fresh cattle faeces, water from water trough and pond, and surfaces of water trough and hay bunk. For the isolation of E. coli O157, samples were enriched in tryptic soya broth, followed by immunoseparation and then plating on SMAC, CT‐SMAC, CHROMagar™ O157, Tellurite CHROMagar™ O157 and Vancomycin Cefixime Cefsoludin CHROMagar™ O157. Real‐time PCR targeting genes stx1, stx2 and wzyO157 was used to confirm selected isolates. When analysed together, CT‐SMAC and CHROMagar™ O157 were the best combination for isolating E. coli O157, giving 79% true‐positive results and only 0.05% false‐negative results.
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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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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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ItemNovel Modelling Approaches to Characterize and Quantify Carryover Effects on Sensory Acceptability(MDPI, 2018-11)Sensory biases caused by the residual sensations of previously served samples are known as carryover effects (COE). Contrast and convergence effects are the two possible outcomes of carryover. COE can lead to misinterpretations of acceptability, due to the presence of intrinsic psychological/physiological biases. COE on sensory acceptability (hedonic liking) were characterized and quantified using mixed and nonlinear models. N = 540 subjects evaluated grape juice samples of different acceptability qualities (A = good, B = medium, C = poor) for the liking of color (C), taste (T), and overall (OL). Three models were used to quantify COE: (1) COE as an interaction effect; (2) COE as a residual effect; (3) COE proportional to the treatment effect. For (1), COE was stronger for C than T and OL, although COE was minimal. For (2), C showed higher estimates (-0.15 to +0.10) of COE than did T and OL (-0.09 to +0.07). COE mainly took the form of convergence. For (3), the absolute proportionality parameter estimate (λ) was higher for C than for T and OL (-0.155 vs. -0.004 to -0.039), which represented -15.46% of its direct treatment effect. Model (3) showed a significant COE for C. COE cannot be ignored as they may lead to the misinterpretation of sensory acceptability results.
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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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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.