Remote sensory assessment of beer quality based on visual perception of foamability and biometrics compared to standard emotional responses from affective images

Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development

In recent years, the research and development (R&D) of rice and wheat functional foods has attracted a widespread attention from food researchers, driven by the increasing global food consumption and growing consumer demand for healthier and safer food. Artificial intelligence (AI) has the potential to enhance efficiency, quality, and safety through the AI's problem-solving and decision-support capabilities. This review provides a comprehensive overview of AI-related technologies applied in food industry, including machine learning, large language models, computer vision, and intelligent sensor. It then explores AI applications in rice / wheat functional food R&D over the past five years (2020-2024), covering key topics such as crops cultivation and screening, food processing, food quality and safety, challenges and future prospects. The introduction of AI technology has led the field towards higher efficiency, non-destructive analysis, better robustness and greater stability. In practical applications, combining AI technology with various spectroscopic and sensing technologies has shown great promise in addressing critical problems such as low crop yields, insufficient functional nutrition in grains, over-processing, and ecological contamination caused by traditional detection methods. Nevertheless, the implementation of AI in this field still faces several challenges, including narrow application scope, limited data availability, high application cost, and trust-related concerns. Looking ahead, as the application scenarios and functionalities of AI continue to broaden, AI is poised to emerge as a disruptive technology that would fundamentally transform the landscape of rice / wheat functional food R&D.

Exploring consumer acceptability of leafy greens in earth and space immersive environments using biometrics

Novel research on food perception is required for long-term space exploration. There is limited research on food/beverage sensory analysis in space and space-simulated conditions, with many studies presenting biases in sensory and statistical methods. This study used univariate and multivariate analysis on data from pick-and-eat leafy greens to assess self-reported and biometric consumer sensory analysis in simulated microgravity using reclining chairs and space-immersive environments. According to ANOVA (p < 0.05), there were significant differences between interaction room × position for head movements; besides, there were non-significant differences in the interaction samples × environment. On the other hand, there were significant differences in the sample×position interaction for all liking attributes. Results from multivariate analysis showed effects on self-reported, physiological, and emotional responses of samples in space-related positions and environments related to sensory perception changes. Non-invasive biometrics could offer a powerful tool for developing digital twins to assess genetically modified plants and plant-based food/beverages for long-term space exploration.

Consumer insights into the at-home liking of commercial beers: Integrating nonvolatile and volatile flavor chemometrics

Consumer acceptability of beers is influenced by product formulation and processing conditions, which impart unique sensory profiles. This study used multivariate techniques to evaluate at-home consumer sensory acceptability of six commercial beers considering their style, fermentation type, and chemical composition. Samples included top-fermented beers (American India Pale Ale and Stout) and bottom-fermented beers (Pilsner, zero-alcohol Pilsner, Vienna Lager, and Munich Dunkel). Beer consumers (n = 50) conducted sensory hedonic, check-all-that-apply (CATA) and just-about-right (JAR) tests. Chemometric variables included iso-alpha-acids, hordenine, and volatile aromatic compounds, quantified by chromatographic methods, whereas bitterness units (IBU) were determined spectrophotometrically. Lager beers had higher acceptability than top-fermented beer (p < .05) for all attributes. Light-colored beers and medium-height foams had the highest liking scores for visual sensory attributes. Higher concentrations of bitter-tasting molecules, hordenine, and acidity decreased the liking scores of top-fermented (Ale) beers, as a sensory penalty analysis suggested. In contrast, the most favored beers (Pilsners and Munich Dunkel) contained higher fusel alcohol esters linked to fruity aromatic notes. Although a low conversion rate of fatty acids into fruity esters was noted in nonalcoholic Pilsner, its overall liking score was not statistically different from the alcoholic version. However, consumers perceived the nonalcoholic Pilsner as less bitter than its alcoholic counterpart even when IBUs were nonsignificantly different. This study emphasized the significance of understanding beer chemometrics to comprehend consumer acceptability, highlighting the crucial role of bitter molecules. Hence, hordenine, acidity, and volatile contents provided additional and valuable insights into consumer preferences.

Effects of Different Beer Compounds on Biometrically Assessed Emotional Responses in Consumers

The study of emotional responses from consumers toward beer products is an important digital tool to obtain novel information about the acceptability of beers and their optimal physicochemical composition. This research proposed the use of biometrics to assess emotional responses from Mexican beer consumers while tasting top- and bottom-fermented samples. Furthermore, a novel emotional validation assessment using proven evoking images for neutral, negative, and positive emotions was proposed. The results showed that emotional responses obtained from self-reported emoticons and biometrics are correlated to the specific emotions evoked by the visual, aroma, and taste aspects of beers. Consumers preferred bottom-fermentation beers and disliked the wheat-based and higher-bitterness samples. Chemical compounds and concentrations were in accordance to previously reported research for similar beer styles. However, the levels of hordenine were not high enough to evoke positive emotions in the biometric assessment, which opens additional research opportunities to assess higher concentrations of this alkaloid to increase the happiness perception of low or non-alcoholic beers.

Correlation of Taste Components with Consumer Preferences and Emotions in Chinese Mitten Crabs (Eriocheir sinensis): The Use of Artificial Neural Network Model

This study took a consumer sensory perspective to investigate the relationship between taste components and consumers’ preferences and emotions. Abdomen meat (M), hepatopancreas (H), and gonads (G) of Chinese mitten crabs, one from Chongming, the Jianghai 21 variety (C-JH), and two from Taixing, the Jianghai 21 (T-JH) and Yangtze II varieties (T-CJ), were used to evaluate flavor quality. The results indicated that in the abdomen meat, differences in taste components were mainly shown in the content of sweet amino acids, bitter amino acids, K+, and Ca2+; M-C-JH had the highest EUC value of 9.01 g/100 g. In the hepatopancreas, bitter amino acids were all significantly higher in H-C-JH (569.52 mg/100 g) than in the other groups (p < 0.05). In the gonads, the umami amino acid content was significantly higher in G-T-JH than in the other groups (p < 0.05) (EUC values: G-T-JH > G-C-JH > G-T-CJ). Consumer sensory responses showed that different edible parts of the crab evoked different emotions, with crab meat being closely associated with positive emotions and more complex emotional expressions for the hepatopancreas and gonads. In comparison, consumers were more emotionally positive when consuming Yangtze II crab. H-C-JH evoked negative emotions due to high bitter taste intensities. Multifactor analysis (MFA) showed arginine, alanine, glycine, proline, K+, and Ca2+ were found to have a positive correlation with consumer preference; an artificial neural network model with three neurons was built with good correlation (R2 = 0.98). This study can provide a theoretical foundation for the breeding of Chinese mitten crabs, new insights into the river crab industry, and the consumer market.

Consumer Studies: Beyond Acceptability—A Case Study with Beer

Beer is one of the most consumed alcoholic beverages in the world; its consumption and preference are evolving from traditional industrial beers of low complexity to novel craft beers with diverse flavour profiles. In such a competitive industry and considering the complexity of consumer behaviour, improvement and innovation become necessary. Consequently, consumer science, which is responsible for identifying the motivation behind customer preferences through their attitudes, perception and behaviour, has implemented strategies ranging from simple hedonic measurements to several innovative and emerging methodologies for a deeper understanding of the variables that affect the product experience: sensory, affective and cognitive. In this context, we offer a review inspired by previous research that explores some of the quantitative and qualitative methods used in consumer studies related to beer consumption, ranging from traditional approaches (acceptability, purchase intention, preference, etc.) to techniques that go beyond acceptability and allow a different understanding of aspects of consumer perception and behaviour (segmentation, expectations, emotions, representation, etc.). Also, innovative applications (contexts, immersive technologies and virtual reality, implicit measures, etc.) and current trends related to consumer science (Internet, social media, pairing, product experience, etc.) are addressed.