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.

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms.

BACKGROUND: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. RESULTS: The ANN method was able to create more accurate models (R2 = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type. CONCLUSION: The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry.

Bacterial cellulose infusion: A comprehensive investigation into textural, tribological and temporal sensory evaluation of ice creams.

The study examines how adding bacterial cellulose also referred to as Symbiotic Culture of Bacteria and Yeast (SCOBY) to ice cream affects the textural, tribological, and sensory attributes, particularly texture and mouthfeel perception. Analytical assessments were performed on three types: SCOBY-added ice cream and two reference samples (control and guar gum-added ice creams). Evaluations included physicochemical properties, textural and tribological characteristics, and dynamic sensory mouthfeel using the temporal dominance of sensation (TDS) methodology. SCOBY ice cream showed higher probiotics content, lower pH, and higher acidity than reference samples. The addition of SCOBY increased hardness and altered the textural properties. TDS analysis highlighted distinct temporal dominance patterns, with guar gum ice cream presenting a pronounced mouth/residual coating pre-swallowing, while SCOBY and control ice cream exhibited a thin/fluid perception. The frictional factor at 37 °C was positively correlated with the melting rate, graininess, and thin/fluid perception while negatively correlated with firmness, smoothness and mouthfeel liking. Additionally, the mouthfeel liking was higher with firm, smooth and mouth/residual coating sensations and lower with grainy and thin/fluid perception. In summary, incorporating SCOBY in ice cream formulations can provide health benefits and meet consumer preferences for natural ingredients, while ensuring careful optimization of mouthfeel.

The Scoop on SCOBY (Symbiotic Culture of Bacteria and Yeast): Exploring Consumer Behaviours towards a Novel Ice Cream.

With the growing demand for sustainable practises, the food industry is increasingly adopting circular economy approaches. One example is recycling the symbiotic culture of bacteria and yeast (SCOBY) used in kombucha fermentation to create value-added products. However, consumer acceptance of such novel products remains unclear. To address this, the present study examined consumer attitudes towards ice cream made with SCOBY as an ingredient and how this affected their intention to consume it. Drawing on the theory of planned behaviour (TPB) and additional constructs such as emotions and food neophobia, an online survey was conducted with New Zealand consumers (N = 170). Results showed that the TPB constructs significantly predicted the intention to consume SCOBY ice cream. Moreover, by adding emotions to the constructs, the model's explanatory power was enhanced. Attitudes, subjective norms, and emotions were the main predictors of intention, which in turn was found to be the main predictor of behaviour. Participants' beliefs about the safety and taste of SCOBY ice cream were significantly correlated with their intention and behaviour, as were the opinions of nutritionists/dietitians, friends, and family. The model accounted for 21.7% of the variance in behaviour and 57.4% of the variance in intention. These findings can be used to plan marketing strategies related to waste-to-value-added products such as SCOBY ice cream.

Exploring the textural dynamics of dairy and plant-based yoghurts: A comprehensive study.

The mouthfeel and texture of dairy and non-dairy yoghurts play a critical role in food acceptance and liking. The present study aimed to understand the oral perception of commercially available dairy and non-dairy yoghurts. Four dairy and four non-dairy yoghurts with different levels of protein and fat were analyzed to understand the impact of particle size, textural properties and frictional coefficient on the dynamic sensory mouthfeel characteristics measured by the temporal dominance of sensations (TDS) method. Differences in friction coefficients of dairy and non-dairy yoghurts were observed. The friction factor was lower for high-fat dairy yoghurts than for non-dairy yoghurts. The particle size d90 in yoghurts was positively related to graininess perception (r=0.81) and negatively associated with mouthfeel liking (r=-0.87) and overall liking (r=-0.80). For the TDS results, "creaminess" and "thickness" were significantly dominant for dairy yoghurts, while "melty" and "easy to dissolve" were dominant attributes for non-dairy yoghurts. Creaminess perception improves the mouthfeel liking (r=0.72) and overall liking (r=0.59) of yoghurts and is the driver of liking. The findings of this study help understand the intrinsic mouthfeel properties of commercial dairy and non-dairy yoghurts, which will provide valuable insight to product developers during the new product formulation.

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.

Understanding consumer liking of beef patties with different firmness among younger and older adults using FaceReader™ and biometrics.

Sensorial perceptions change as people age and biometrics analysis can be used to explore the unconscious consumer responses. Investigation was conducted of effects of consumer age (younger, 22-52 years; older, 60-76 years) on facial expression response (FER) during consumption of beef patties with varying firmness (soft, medium, hard) and taste (±plum sauce). Video images were collected and FERs analysed using FaceReader™. Younger people exhibited higher intensity for happy/sad/scared and lower intensity for neutral/disgusted, relative to older people. Interactions between age and texture/sauce showed little FER variation in older people, whereas younger people showed considerable FER variation. Younger people, but not older people, had lowest intensity of happy FER and highest intensity of angry FER for the hard patty. Sauce addition resulted in higher intensity of happy/contempt in younger consumers, but not older consumers. FER collected using FaceReader™ was successfully used to differentiate between the unconscious responses of younger and older consumers.

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.

Consumer Characterization of Wet- and Dry-Aged Mutton Flavor Profile Using Check-All-That-Apply.

The aim of this study was to assess if consumers could characterize wet- and dry-aged mutton flavor profiles using CATA (check-all-that-apply). A flavor lexicon was developed for mutton, and consumers assessed wet- and dry-aged mutton patties against this lexicon using CATA methodology. Results indicate that consumers most often associated caramel and roasted flavors with dry-aged patties, and "sheepy" and metallic flavors with wet-aged patties. Volatile analysis supported the consumer characterization as there were more Maillard reaction products, including pyrazines, which are associated with roasted and cooked flavors, found in the dry-aged patty volatile profile. More 1-octen-3-one, which is associated with metallic flavors, was found in the wet-aged patty volatile profile. These results provide validation that the lexicon utilized in this study (i) is suitable for the characterization of mutton flavor and (ii) will have applications for future investigations into the flavor components driving consumer liking for mutton.