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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ItemSilicon supplementation improves the nutritional and sensory characteristics of lentil seeds obtained from drought-stressed plants(Wiley, 2021-03-15)BACKGROUND Lentil is an important nutritionally rich pulse crop in the world. Despite having a prominent role in human health and nutrition, it is very unfortunate that global lentil production is adversely limited by drought stress, causing a huge decline in yield and productivity. Drought stress can also affect the nutritional profile of seeds. Silicon (Si) is an essential element for plants and a general component of the human diet found mainly in plant-based foods. This study investigated the effects of Si on nutritional and sensory properties of seeds obtained from lentil plants grown in an Si-supplied drought-stressed environment. RESULTS Significant enhancements in the concentration of nutrients (protein, carbohydrate, dietary fibre, Si) and antioxidants (ascorbate, phenol, flavonoids, total antioxidants) were found in seeds. Significant reductions in antinutrients (trypsin inhibitor, phytic acid, tannin) were also recorded. A novel sensory analysis was implemented in this study to evaluate the unconscious and conscious responses of consumers. Biometrics were integrated with a traditional sensory questionnaire to gather consumers responses. Significant positive correlations (R = 0.6–1) were observed between sensory responses and nutritional properties of seeds. Seeds from Si-treated drought-stressed plants showed higher acceptability scores among consumers. CONCLUSION The results demonstrated that Si supplementation can improve the nutritional and sensory properties of seeds. This study offers an innovative approach in sensory analysis coupled with biometrics to accurately assess a consumer's preference towards tested samples. In the future, the results of this study will help in making a predictive model for sensory traits and nutritional components in seeds using machine-learning modelling techniques.
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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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ItemClassification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms(MDPI, 2020-09-01)Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.
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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.
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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.
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ItemD-Tagatose as a Sucrose Substitute and Its E ff ect on the Physico-Chemical Properties and Acceptability of Strawberry-Flavored Yogurt(MDPI, 2019-07-01)Sugar not only provides the desirable sweetness but its reduction can also alter the physico-chemical properties of foods. The objective of this study was to evaluate the effects of tagatose as a sugar substitute on selected physico-chemical properties and sensory acceptability of strawberry-flavored yogurts. Six yogurt samples with decreasing concentrations of sucrose (8.50 to 1.70 g/100 g) and increasing concentrations of tagatose (0.00 to 9.24 g/100 g) were evaluated. Physico-chemical tests (pH, lactic acid (%), °Brix, water-holding capacity (WHC), viscosity, and color) were conducted to examine the quality and shelf-life of yogurts during 28 days of storage at 4 °C. An acceptability test (n = 55) was conducted to evaluate the sensory characteristics of yogurts. Sucrose reductions by the replacement of up to 80% tagatose showed marginal effects on the selected physico-chemical properties; however, the loss of red color (a*) and increase in yellowness (b*) of the tagatose-substituted samples were significant. Strawberry yogurts with tagatose replacements had similar acceptability scores for all attributes. Sucrose reduction showed a positive effect on the purchase intent of the strawberry yogurts (an increase of 3–30%). These findings can be used to understand the effects of tagatose/sucrose formulations on the acceptability and physico-chemical properties of yogurts.