Color-Induced Aroma Illusion: Color Cues Can Modulate Consumer Perception, Acceptance, and Emotional Responses toward Cooked Rice

Effect of Shape, Size, and Color of the Food Plate on Consumer Perception of Energy Value, Portion Size, Attractiveness, and Expected Price of Dessert

The development of new dishes in the catering services market requires an understanding of consumers' needs, expectations, and motivations for their choices. The effect of the serving method of a dessert on customers' perceptions of its visual appeal, portion size, energy value, and expected price was evaluated. The study involved the presentation of desserts on plates of various sizes, shapes, and colors. The study was carried out among 1005 respondents using the CAWI method. Our findings revealed that along with an increasing plate size from a diameter of ϕ24-27 cm to ϕ31 cm, the ratings of the dish's perceived appearance (p ≤ 0.001), portion size (p ≤ 0.001), and energy value (p ≤ 0.01) decreased. Plate shape influenced the perceived appearance of the dessert. When placed on a square platter, round desserts were considerably (p ≤ 0.05) less appealing. The color of the plate had a significant influence (p ≤ 0.001) on the dish's perceived appearance and estimated monetary value, and it evoked more sensory-hedonic impressions. Red-plate and white-plate desserts were liked less than black-plate desserts, but color-plated desserts were perceived as more expensive than those served on white dishes. Consumers perceived bright desserts on white plates as traditional, natural, and boring; those on black plates as modern, appetizing, and aesthetic; and those served on red plates as artificial, unsightly, and unappetizing. Higher consumer food neophobia led to a lower rating related to appearance and price perceptions, but elevated perceptions of portion size and energy value appraisal. Our results may be used in the marketing of gastronomic dishes.

Physicochemical attributes and acceptability of marula wine fermented with natural Lactiplantibacillus plantarum and Saccharomyces cerevisiae

The aim of the study was to test the acceptability and physico-chemical characteristics of marula wine fermented with known cultures of natural Lactiplantibacillus plantarum and Saccharomyces cerevisiae. The LAB Lactobacillus and Saccharomyces dominate the fermentation of marula wine throughout the fermentation period. These were isolated and identified from the spontaneously fermented marula wine and re-inoculated as single cultures and as mixed cultures to ferment marula juice into wine. The Saccharomyces cerevisiae combined with Lactiplantibacillus plantarum (PYL) and Saccharomyces cerevisiae (PY) fermented wines were not significantly different (p ≥ 0.05) in all the physico-chemical characteristics and acceptability. The single culture of Lactiplantibacillus plantarum had the lowest pH of 2.8. The alcohol content of marula wine fermented with Saccharomyces cerevisiae was 6.10 ± 0.17, while the alcohol content of the spontaneously fermented wine was 3.33 ± 2.49. The oBrix of wine fermented with Saccharomyces cerevisiae only and as mixed culture was 2.07 ± 0.21 and 2.00 ± 0.00, respectively, while the control and Lactiplantibacillus plantarum had an oBrix of 6.23 ± 2.77 and 8.67 ± 0.06, respectively. The Lactiplantibacillus plantarum fermented sample and the control had significantly higher overall acceptability scores of 7.60 and 6.98, respectively. Saccharomyces cerevisiae is capable of producing ethanol as a single culture and co-cultured with Lactobacillus plantarum. The most preferred wine was that fermented by Lactiplantibacillus plantarum only because of its sweetness.

Review of technology advances to assess rice quality traits and consumer perception

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

Effect of Whole Cluster Fermentation on Phenolics in Cold-Hardy Hybrid Wines

Generally, red wines made from cold-hardy hybrid grapes have much lower tannin content than Vitis vinifera wines and they are often perceived as being of lower quality. This study evaluated the chemistry and sensory perception of whole-cluster (WC) fermented “Frontenac” and “Marquette” red wines from Iowa (F-ISU, M-ISU) and “Marquette” red wines from Minnesota (M-UMN). Three conditions were compared: Control 0% WC (w/w), 25% WC (w/w), and 50% WC (w/w). The 50% WC wines had significantly higher iron-reactive phenolics, tannin, and anthocyanin contents than control wines. The color intensity in 50% WC wines was maintained after 4 months of aging. A tasting evaluation was conducted with the “Marquette” wines from both states and untrained panelists preferred 50% WC “Marquette” wines over control wines. This study showed promising results on the use of 50% (w/w) whole clusters as a pre-fermentative technique to improve “Marquette” and “Frontenac” red wine quality.

Vegan Ice Cream Made from Soy Extract, Soy Kefir and Jaboticaba Peel: Antioxidant Capacity and Sensory Profile

Considering the need for functional foods and the use of by-products of the food industry, a potentially functional ice cream was developed, using soy extract, soy kefir and dehydrated jaboticaba peel. Five ice creams were produced using soy kefir (K) and soy extract (S): (1) GS—100% S; (2) GK1-75% S/25% K; (3) GK2-50% S/50% K; (4) GK3-25% S/75% K and (5) GK-100% K; The products were evaluated by physicochemical, microbiological and sensory (check all that apply) analyses. The addition of kefir was found to increase the acidity of the products. The concentrations of total phenolic compounds in the formulations with kefir were approximately ten times higher than the GS formulation. All products presented concentrations of thermotolerant coliforms <3 NMP/g and absence of Salmonella ssp. The viability of Lactobacillus ssp., Streptococcus spp. and Bifidobacterium ssp. was higher than 10 log CFU/g during the whole storage period. The GS and GK1 formulations had the lowest scores, while GK ice cream was preferred. The formulations showed distinct sensory profiles in the CATA, and the ice cream with 100% kefir was associated with desirable attributes. The ice creams exhibited microbiological and sensory characteristics that meet the expectations of the product’s target audience.

You Eat How You Think: A Review on the Impact of Cognitive Styles on Food Perception and Behavior

Sensory perception is understood to be a complex area of research that requires investigations from a variety of different perspectives. Although researchers have tried to better understand consumers’ perception of food, one area that has been minimally explored is how psychological cognitive theories can help them explain consumer perceptions, behaviors, and decisions in food-related experiences. The concept of cognitive styles has existed for nearly a century, with the majority of cognitive style theories existing along a continuum with two bookends. Some of the more common theories such as individualist-collectivist, left-brain-right-brain, and convergent-divergent theories each offered their own unique insight into better understanding consumer behavior. However, these theories often focused only on niche applications or on specific aspects of cognition. More recently, the analytic-holistic cognitive style theory was developed to encompass many of these prior theoretical components and apply them to more general cognitive tendencies of individuals. Through applying the analytic-holistic theory and focusing on modern cultural psychology work, this review may allow researchers to be able to answer one of the paramount questions of sensory and consumer sciences: how and why do consumers perceive and respond to food stimuli the way that they do?

Pull the Emotional Trigger or the Rational String? A Multi-Group Analysis of Organic Food Consumption

The organic food industry in China has been developing fast with the increasing consumer demand for healthier, safer, and more nutritious foods since the epidemic outbreak. It is of great significance to understand the psychological preference of consumers for organic food and adjust the marketing strategy accordingly. In this study, we adopted the multi-group structural equation model (SEM) to analyze 571 questionnaire data and explored the effects of consumers’ perception on the sensory appeal of organic food, perception on promotional stimulation, positive emotion, and perceived social value on the purchase intention of organic food. Based on the Stimulus–Organism–Response (S-O-R) model, this study divides the route affecting organic consumption behavior into the rational route and emotional route. It was proved that the emotional route (positive emotion) has a greater impact on the purchase intention of organic food than the rational route (perceived social value). In addition, there are different purchase intentions among different product types. Specifically, compared with organic tea, positive emotion has a greater effect on the purchase intention for organic rice. This study provides an important reference for the organic food-marketing strategy of enterprises.

Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis

Sensory responses dynamically change while eating foods. Temporal dominance of sensations (TDS) methods record temporal evolution and have attracted attention in the last decade. ISO 13299 recommends that different levels of attributes are investigated in separate TDS trials. However, only a few studies have attempted to link the dynamics of two different levels of sensory attributes. We propose a method to link the concurrent values of dominance proportions for primary- and multi-sensory attributes using canonical correlation analysis. First, panels categorized several attributes into primary- and multi-sensory attributes. Primary-sensory attributes included sweet, sour, fruity, green, watery, juicy, aromatic, and light. Multi-sensory attributes included refreshing, fresh, pleasurable, rich/deep, ripe, and mild. We applied the TDS methods to strawberries using these two categories of attributes. The obtained canonical correlation model reasonably represented the relationship between the sensations in a reductive manner using five latent variables. The latent variables couple multiple primary- and multi-sensory responses that covary. Hence, the latent variables suggest key components to comprehend food intake experiences. We further compared the model based on the dominance proportions and the time-derivatives of the dominance proportions. We found that the former model was better in terms of the ease of interpreting the canonical variables and the degree to which the canonical variables explain the dominance proportions. Thus, these models help understand and leverage the sensory values of food products.

Effects of Thickness Fraction Process on Physicochemical Properties, Cooking Qualities, and Sensory Characteristics of Long-Grain Rice Samples

A process of removing thinner kernels of rough rice, i.e., thickness fraction process, has been suggested as a method for increasing milling yields in the rice industry. This study aimed at determining whether physicochemical properties, cooking qualities, and sensory characteristics of rice samples could be changed by the addition of a thickness fraction into the rice process stream. Each of four long-grain rice cultivar lots was assigned into two groups: unfractionated and thickness-fractionated. For the thickness-fractionated group, thin rice kernels (<1.9 mm) of rough rice were discarded from unfractionated rice samples. Unfractionated and thickness-fractionated rice samples were compared with respect to physicochemical properties, cooking qualities, and sensory characteristics. The results showed that the removal of such thin kernels decreased the breakage and chalkiness rates and increased head rice yields. Fractionated rice samples exhibited lower amylose contents and crude protein contents but higher gelatinization temperatures than unfractionated rice samples. While the optimum cooking duration and width–expansion ratios of thickness-fractionated rice samples were higher than those of unfractionated ones, there was a negligible impact of the thickness fraction process on sensory characteristics of long-grain rice samples. In conclusion, the thickness fraction process affects physicochemical properties and cooking qualities more than the sensory characteristics of rice samples.

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.