Novel Contactless Sensors for Food, Beverage and Packaging Evaluation.

The use of traditional methods to evaluate food, beverages, and packaging tends to be time-consuming, labour-intensive, and usually involves high costs due to the need for expensive equipment, regular refill of consumables, skilled personnel and, in the case of sensory evaluation, incentives or payments involved for participants recruitment and/or panelists training and participation [...].

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.

Digital technologies to assess yoghurt quality traits and consumers acceptability.

BACKGROUND: Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self-reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses. RESULTS: Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness and near-infrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies sample. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98; Model 2: R = 0.99). CONCLUSION: The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Digital Integration and Automated Assessment of Eye-Tracking and Emotional Response Data Using the BioSensory App to Maximize Packaging Label Analysis.

New and emerging non-invasive digital tools, such as eye-tracking, facial expression and physiological biometrics, have been implemented to extract more objective sensory responses by panelists from packaging and, specifically, labels. However, integrating these technologies from different company providers and software for data acquisition and analysis makes their practical application difficult for research and the industry. This study proposed a prototype integration between eye tracking and emotional biometrics using the BioSensory computer application for three sample labels: Stevia, Potato chips, and Spaghetti. Multivariate data analyses are presented, showing the integrative analysis approach of the proposed prototype system. Further studies can be conducted with this system and integrating other biometrics available, such as physiological response with heart rate, blood, pressure, and temperature changes analyzed while focusing on different label components or packaging features. By maximizing data extraction from various components of packaging and labels, smart predictive systems can also be implemented, such as machine learning to assess liking and other parameters of interest from the whole package and specific components.

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.

Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), decanoic acid, ethyl ester (r = -0.94), and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.

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 techniques. © 2020 Society of Chemical Industry.

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data.

Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial intelligence (AI) and specifically machine learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008-2016) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Models 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1; R = 0.92; slope = 0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2; R = 0.98; slope = 0.93) and wine color (Model 3; R = 0.99; slope = 0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.

Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms.

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.

Consumer rejection threshold, acceptability rates, physicochemical properties, and shelf-life of strawberry-flavored yogurts with reductions of sugar.

BACKGROUND: There is an increasing demand for reduced-sugar products due to the worldwide prevalence of obesity, diabetes, and cardiovascular diseases. The aim of this study was to evaluate the effects of sugar (sucrose) reductions on the acceptability, preference, and quality of strawberry-flavored yogurts. A consumer rejection threshold test and an acceptability test (N = 53) were conducted using six yogurt samples with decreasing concentrations of sugar (12-5/100 g). Additional physicochemical tests (pH, °Brix, water-holding-capacity, viscosity, and color) were conducted to examine the quality and shelf-life of strawberry-flavored yogurts with reductions of sucrose during 28 days of storage at 4 °C. RESULTS: Reduction of sucrose affected the acceptability and physicochemical characteristics of yogurts. The consumer rejection threshold showed that sucrose in strawberry-flavored yogurts could be reduced to 5.25/100 g from an initial concentration of 12/100 g without affecting the preferences of consumers. The 71%-sucrose (8.50/100 g of yogurt) was perceived as the most liked (6.27 using a nine-point hedonic scale) and the most preferred (rank sum = 127.50) yogurt sample. For the physicochemical properties of yogurts, the viscosity (3263-5473 cP) decreased, and the color lightness (80.98-85.44) increased during 28 days of storage at 4 °C. CONCLUSION: Physicochemical properties and preferences were affected by the reduction of sugar. The consumer rejection threshold analysis showed that sucrose can be reduced to less than half of the initial concentration. These findings are useful to understand consumers' acceptability and shelf-life of yogurts with reduced-sugar formulations in the developing of new products. © 2020 Society of Chemical Industry.

Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application.

Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.

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.

Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate.

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.

The Application of Biometric Approaches in Agri-Food Marketing: A Systematic Literature Review.

A challenge in social marketing studies is the cognitive biases in consumers' conscious and self-reported responses. To help address this concern, biometric techniques have been developed to obtain data from consumers' implicit and non-verbal responses. A systematic literature review was conducted to explore biometric applications' role in agri-food marketing to provide an integrated overview of this topic. A total of 55 original research articles and four review articles were identified, classified, and reviewed. It was found that there is a steady growth in the number of studies applying biometric approaches, with eye-tracking being the dominant method used to investigate consumers' perceptions in the last decade. Most of the studies reviewed were conducted in Europe or the USA. Other biometric techniques used included facial expressions, heart rate, body temperature, and skin conductance. A wide range of scenarios concerning consumers' purchase and consumption behaviour for agri-food products have been investigated using biometric-based techniques, indicating their broad applicability. Our findings suggest that biometric techniques are expanding for researchers in agri-food marketing, benefiting both academia and industry.

A critical review of social media research in sensory-consumer science.

The collection and analysis of digital data from social media is a rapidly growing methodology in sensory-consumer science, with a wide range of applications for research studying consumer attitudes, preferences, and sensory responses to food. The aim of this review article was to critically evaluate the potential of social media research in sensory-consumer science with a focus on advantages and disadvantages. This review began with an exploration into different sources of social media data and the process by which data from social media is collected, cleaned, and analyzed through natural language processing for sensory-consumer research. It then investigated in detail the differences between social media-based and conventional methodologies, in terms of context, sources of bias, the size of data sets, measurement differences, and ethics. Findings showed participant biases are more difficult to control using social media approaches, and precision is inferior to conventional methods. However, findings also showed social media methodologies may have other advantages including an increased ability to investigate trends over time and easier access to cross-cultural or global insights. Greater research in this space will identify when social media can best function as an alternative to conventional methods, and/or provide valuable complementary information.

Effects of Tasting and Ingredient Information Statement on Acceptability, Elicited Emotions, and Willingness to Purchase: A Case of Pita Chips Containing Edible Cricket Protein.

Sustainable and nutritious alternatives are needed to feed the ever-increasing world population. The successful incorporation of edible-cricket protein (ECP) into foods needs deeper consumer insights. Treatments (plain, Italian, and Cajun pita chips containing 6.9% w/w ECP) were evaluated by subjects for overall liking (OL), emotions, and purchase intent (PI) in three different moments: (1) before tasting, (2) after tasting/before ECP statement, and (3) after tasting/after ECP statement. Attributes' liking scores were evaluated only after tasting/before ECP statement. Liking scores (mixed-effects ANOVA), emotions, and PI across moments within treatments/across treatments within moments were evaluated. Emotion-based penalty-lift analyses for OL within moments were assessed using two-sample t-tests (p < 0.05). Random forest model analyzed after-tasting informed PI and variables' importance. Although formulations' OL and PI were similar across moments, plain and Italian chips had higher after-tasting (before and after ECP statement) OL than the Cajun chips. Moments indirectly affected OL via emotions elicitation. Valence and activation/arousal emotions discriminated across moments for the plain treatment whereas valence and mostly activation/arousal terms discriminated across moments for the Italian and Cajun treatments, respectively. For either formulation or moment, "interested" and "adventurous" positively affected OL. Before and after-tasting attribute liking, "satisfied," and "enthusiastic" emotions were critical in predicting after-tasting informed PI.

Effects of extrusion processing on the bioactive constituents, in vitro digestibility, amino acid composition, and antioxidant potential of novel gluten-free extruded snacks fortified with cowpea and whey protein concentrate.

This study aimed to develop novel gluten-free snacks from rice flour, cowpea and whey protein concentrate (WPC) enriched with bioactive and antioxidant properties. The effects of extrusion on bioactive compounds (sucrose-galactosyl oligosaccharides, insoluble dietary fibre, resistant starch, phytic acid, trypsin inhibitory activity), protein digestibility, amino acid composition, total phenolic content and antioxidant properties were evaluated by comparing raw formulations and extruded snacks. Rice flour (100%) was used as a control. Extrusion increased the oligosaccharides (2-3 fold) and resistant starch (1-3 fold), whereas the insoluble fibre content was not significantly affected. Extrusion increased in vitro protein digestibility (p < 0.05) and amino acid composition in snacks. Extruded and raw samples enriched with cowpea and WPC had an increase in total phenolic content (TPC) and antioxidant activity. Extrusion significantly reduced the TPC and antioxidant properties of extruded snacks compared to their raw counterparts. The results obtained in this study respond to the growing interest of the food industry to cater to consumer demand for healthy novel gluten-free expanded snacks with bioactive compounds.

Plant and Dairy-Based Yogurts: A Comparison of Consumer Sensory Acceptability Linked to Textural Analysis.

Yogurt, readily available in plant and dairy-based formulations, is widely consumed and linked with health benefits. This research is aimed to understand the sensory and textural spectrum of commercially available dairy and plant-based yogurts. In a preliminary study, qualitative focus group discussions (4 groups; n = 32) were used to determine perceptions of 28 dairy and plant-based yogurts, identifying positive consumer perceptions of plant-based yogurts. A smaller subset of five spoonable and one drinkable yogurts-(Reference, Soy, Coconut, Cookies, Berry, and Drinkable) was subsequently selected for rheological and structural measurements, showing wide variations in the microstructure and rheology of selected yogurt samples. A quantitative blind sensory tasting (n = 117) showed varying yogurt acceptability, with Berry being the least-liked and Cookies being the most-liked yogurt, in terms of overall liking. The multi-factor analysis confirmed that compositional and textural elements, including protein content, gel firmness, and consistency coefficient, displayed a positive relationship with overall liking. In contrast, fat, sugar, and calories were negatively correlated to the overall liking. This research showed that texture and other compositional factors are significant determinants of the consumer acceptability of yogurt products and are essential properties to consider in product development.

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 [...].

Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins.

Increased meat consumption has been associated with the overuse of fresh water, underground water contamination, land degradation, and negative animal welfare. To mitigate these problems, replacing animal meat products with alternatives such as plant-, insect-, algae-, or yeast-fermented-based proteins, and/or cultured meat, is a viable strategy. Nowadays, there is a vast amount of information regarding consumers' perceptions of alternative proteins in scientific outlets. Sorting and arranging this information can be time-consuming. To overcome this drawback, text mining and Natural Language Processing (NLP) are introduced as novel approaches to obtain sensory data and rapidly identify current consumer trends. In this study, the application of text mining and NLP in gathering information about alternative proteins was explored by analyzing key descriptive words and sentiments from n = 20 academic papers. From 2018 to 2021, insect- and plant-based proteins were the centers of alternative proteins research as these were the most popular topics in current studies. Pea has become the most common source for plant-based protein applications, while spirulina is the most popular algae-based protein. The emotional profile analysis showed that there was no significant association between emotions and protein categories. Our work showed that applying text mining and NLP could be useful to identify research trends in recent sensory studies. This technique can rapidly obtain and analyze a large amount of data, thus overcoming the time-consuming drawback of traditional sensory techniques.

Effect of Serving Plate Types and Color Cues on Liking and Purchase Intent of Cheese-Flavored Tortilla Chips.

Foods' overall liking (OL) and purchase intent (PI) are influenced by visual inputs, such as color cues and serving plate types. Cheese-flavored tortilla chips (CFTC) from two formulations (A and B) with a noticeable color difference (∆E = 4.81) were placed on different serving plates (plastic, foam, and paper) and presented monadically to N = 83 consumers using a randomized/balanced block design in two sessions. Consumers evaluated likings of overall visual quality, color, crunchiness, saltiness, overall flavor (OF), and OL using a 9-point-hedonic scale, attribute appropriateness on a 3-point-just-about-right (JAR) scale, and PI using a binomial (Yes/No) scale. Color differences between A and B influenced crunchiness and saltiness liking and perception, which together with OF liking and formulation, mainly determined OL of CFTC. Although having similar fracturability (N) and sodium content, formulation A had higher crunchiness and saltiness likings. PI was influenced by crunchiness, saltiness, and OF liking with 37, 49, and 60% increases in PI odds per liking-unit increase, respectively. Plate type had minimal effect on the sensory liking of CFTC. The brighter and less-yellow color of CFTC could positively influence liking of crunchiness and saltiness, which significantly contributed to OL and PI. These findings are useful to understand consumers' acceptability and perception of foods when varying visual inputs.