Novel Techniques to Measure the Sensory, Emotional, and Physiological Responses of Consumers toward Foods

Sensory science is an evolving field that has been incorporating technologies from different disciplines [...

Análisis de compuestos fenólicos y estabilidad del color en el jugo de cáscaras de uvas Muscadinas (Vitis rotundifolia)

21 p.Torrico, D. 2006. Análisis de compuestos fenólicos y estabilidad del color en el jugo de cáscaras de uvas Muscadinas (Vitis rotundifolia). Proyecto de Graduación del Programa de Ingeniería en Agroindustria, Escuela Agrícola Panamericana, Honduras. 21 p. Las uvas muscadinas son originarias del sur-este de los Estados Unidos; están muy adaptadas a las condiciones de humedad y calor existentes en esta región del país. La cáscara y semilla poseen altos contenidos de compuestos fenólicos y antocianinas con propiedades nutracéuticas. El objetivo del estudio fue medir compuestos fenólicos y estabilidad del color en extractos de cáscaras en uvas muscadinas. Se utilizó el cultivar Noble por el color oscuro de la cáscara. El jugo fue sometido a diferentes tratamientos y procesos industriales. Los tratamientos evaluados fueron: el jugo fermentado, el jugo fermentado con 200 ppm de SO2 y el jugo sometido a una extracción de azucares con resina amberlite. Adicionalmente, se probaron dos procesos de concentración: deshidratación utilizando calor y liofilización. Se midieron compuestos fenólicos y antocianinas en un período de almacenamiento de 20 días a 30°C. Para el análisis estadístico en la medición de las propiedades de las antocianas y fenoles se empleó un diseño de BCA con un arreglo factorial de 4x3x5, además se utilizó otro diseño de BCA con un arreglo factorial de 4x3x3 para el análisis de HPLC. Los procesos de concentración incluyendo el control, no mostraron diferencias significativas entre ellos (P>0.05). Esto ocurrió en todas las variables medidas a través del tiempo en el experimento. El tratamiento de fermentación fue el que mostró significativamente una mayor reducción de los sólidos solubles iniciales del jugo en comparación con el proceso del lavado con amberlite (P0.05), mostrando la estabilidad de estos compuestos fenólicos.1. Índice de cuadros 2. Índice de figuras 3. Índice de anexos 4. Revisión de literatura 5. Introducción 6. Materiales y métodos 7. Resultados y discusión 8. Conclusiones 9. Recomendaciones 10. Bibliografía 11. Anexo

Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists

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

Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System

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 (University of Melbourne, Melbourne, Australia), 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 (mean 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

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers

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

Silicon supplementation improves the nutritional and sensory characteristics of lentil seeds obtained from drought-stressed plants

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 technique

Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers

Traditional sensory tests rely on conscious and self-reported responses from participants. The integration of non-invasive biometric techniques, such as heart rate, body temperature, brainwaves and facial expressions can gather more information from consumers while tasting a product. The main objectives of this study were i) to assess significant differences between beers for all conscious and unconscious responses, ii) to find significant correlations among the different variables from the conscious and unconscious responses and iii) to develop a model to classify beers according to liking using only the unconscious responses. For this study, an integrated camera system with video and infrared thermal imagery (IRTI), coupled with a novel computer application was used. Videos and IRTI were automatically obtained while tasting nine beers to extract biometrics (heart rate, temperature and facial expressions) using computer vision analysis. Additionally, an EEG mobile headset was used to obtain brainwave signals during beer consumption. Consumers assessed foam, color, aroma, mouthfeel, taste, flavor and overall acceptability of beers using a 9-point hedonic scale with results showing a higher acceptability for beers with higher foamability and lower bitterness. i) There were non-significant differences among beers for the emotional and physiological responses, however, significant differences were found for the cognitive and self-reported responses. ii) Results from principal component analysis explained 65% of total data variability and, along with the covariance matrix (p \u3c 0.05), showed that there are correlations between the sensory responses of participants and the biometric data obtained. There was a negative correlation between body temperature and liking of foam height and stability, and a positive correlation between theta signals and bitterness. iii) Artificial neural networks were used to develop three models with high accuracy to classify beers according to level of liking (low and high) of three sensory descriptors: carbonation mouthfeel (82%), flavor (82%) and overall liking (81%). The integration of both sensory and biometric responses for consumer acceptance tests showed to be a reliable tool to be applied to beer tasting to obtain more information from consumers physiology, behavior and cognitive responses

Influence of Label Design and Country of Origin Information in Wines on Consumers’ Visual, Sensory, and Emotional Responses

This study aimed to evaluate the influence of origin information on Pinot Noir wine labels using eye-tracking and its associations with purchase intent, and hedonic and subconscious emotional responses. Two studies were carried out on untrained university staff and students aged 20–60 years old. Study 1 was conducted to assess consumers’ (n = 55; 55% males, and 45% females) self-reported and subconscious responses towards four design labels (with and without New Zealand origin name/script or origin logo) using eye-tracking and video analysis to evaluate emotions of participants. In study 2, participants (n = 72, 56% males, and 44% females) blind-tasted the same wine sample from different labels while recording their self-reported responses. In study 1, no significant differences were found in fixations between origin name/script and origin logo. However, participants paid more attention to the image and the brand name on the wine labels. In study 2, no significant effects on emotional responses were found with or without the origin name/script or logo. Nonetheless, a multiple factor analysis showed either negative or no associations between the baseline (wine with no label) and the samples showing the different labels, even though the taste of the wine samples was the same, which confirmed an influence of the label on the wine appreciation. Among results from studies 1 and 2, origin information affected the purchase intent and hedonic responses marginally. These findings can be used to design wine labels for e-commerce

A novel emulsion coating and its effects on internal quality and shelf life of eggs during room temperature storage

Effects of mineral oil (MO), chitosan solution (CH) and their emulsions (MO:CH = 75:25, 50:50, and 25:75 ratios) as coating materials in preserving internal quality of eggs were evaluated during a 5-weeks storage at 25 °C. Consumers (n = 109) evaluated surface properties and purchase intent of freshly coated eggs. As storage time increased, Haugh unit and yolk index values decreased whereas weight loss increased. Noncoated eggs rapidly changed from AA to B and C grades after 1 and 3 weeks, respectively. However, all emulsion-coated eggs maintained their A-grade quality for 4 weeks. Compared with noncoated eggs, all emulsion coatings reduced weight loss of eggs by at least seven times (0.88-1.03% vs. 7.14%). Only MO:CH = 25:75 emulsion-coated eggs were not sensorially glossier than noncoated eggs. All emulsion-coated eggs had \u3e80% positive purchase intent and were negative for Salmonella. This study demonstrated that MO:CH emulsion coatings preserved internal quality and prolonged shelf life of eggs. © 2010 The Authors. International Journal of Food Science and Technology © 2010 Institute of Food Science and Technology