Dirección de Negocios Alimentarios - CampAGS
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Aguascalientes
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MX
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Item type:Publication, Physicochemical, Microbiological, Proximate, and Consumer Characterization of Traditional Tenate Cheese in Two Mexican Regions(MDPI AG, 2026); ; Tenate cheese is a traditional Mexican pressed semi-hard cheese made from raw cow’s milk and wrapped in palm fiber. The characterization of this cheese remains scarce. This study presents an exploratory characterization of a single production batch of traditional Tenate cheese obtained from one artisanal producer, providing preliminary information on its physicochemical, microbiological, and proximate analyses, combined with consumer evaluation. The latter was analyzed using hierarchical cluster analysis (HCA) as an exploratory segmentation tool. Tenate cheese was characterized as a semi-hard cheese with active lactic fermentation, a lactic aroma, acidic and umami flavors, and a firm, granular texture. Microbiological analyses showed the absence of coliforms, enterobacteria, and Staphylococcus aureus among the microorganisms evaluated, whereas yeast counts exceeded the regulatory limit. As major foodborne pathogens were not included in the microbiological assessment, the overall microbiological safety of the product could not be confirmed. A total of 318 consumers from Aguascalientes (AGS, n = 149) and the Guadalajara Metropolitan Area (GMA, n = 169) evaluated the product using hedonic and Just-About-Right scales. Consumers from AGS reported significantly higher liking scores than those from GMA. Penalty analysis identified insufficient softness as the main attribute associated with lower liking in AGS, whereas low flavor intensity and weak aftertaste reduced acceptance in GMA. Hierarchical cluster analysis (HCA) identified three consumer segments in each location, revealing distinct preference patterns linked to regional expectations. The main contributions of this study are threefold. First, it contributes to the limited scientific knowledge available on Tenate cheese by providing a comprehensive characterization of the analyzed sample. Second, it shows that consumer acceptance differed between two regional markets, comparing two university-affiliated consumer groups, highlighting the value of consumer segmentation for product positioning. Third, it proposes and applies an integrated framework combining physicochemical, microbiological, nutritional, sensory, and consumer segmentation analyses that can be applied to the study of other artisanal cheeses. © The authors © Applied Sciences © MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analyzing Food Systems to Strengthen Food Security Towards Achieving Sustainable Development Goal 2 (Zero Hunger) and Goal 12 (Responsible Consumption and Production) in Mexico: A Comparison Between Lettuce Growth in Vertical Farms and Outdoor Production Systems(Wiley, 2026-01-27); ; 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sustainability of the agricultural sector in Mexico – compliance or not - with key United Nations’ 17 Sustainable Development Goals: Aguascalientes as a case study(2024); 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scopus© Citations 2 1 26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Purchase Intention for Biopreservative-Treated Chicken and Consumer Segmentation Using Unsupervised Machine Learning(MDPI AG, 2026); ; ; Biopreservatives are emerging as a promising alternative to conventional food preservation methods. However, there is limited empirical evidence integrating validated psychometric instruments with advanced unsupervised segmentation techniques to identify heterogeneous consumer profiles regarding biopreserved chicken consumption. To address these limitations, self-organizing maps (SOMs) have been widely used as unsupervised learning tools that reduce dimensionality, preserve topological relationships, and facilitate the visualization of latent structures in multivariate datasets. This study aims to analyze the factors influencing the acceptance of biopreservation in chicken consumption through a strategic segmentation based on unsupervised machine learning and behavioral dimensions. This methodological design enables the capture of behavioral heterogeneity, the identification of strategic consumption profiles, and the generation of empirical evidence to support future research and communication strategies related to biopreservation technologies in similar consumer contexts. In this study, 323 instances were topologically organized into 16 clusters, grouping individuals with similar behavioral patterns. The descriptive analysis of clusters revealed highly differentiated consumer profiles in terms of Consumer acceptance, cold chain practices, and Knowledge of food microorganisms. The high-acceptance exploratory segment achieved the maximum score (5.00) in knowledge of beneficial microorganisms (V14), acceptance of their use in chicken preservation (V15), and familiarity with lactic acid bacteria (V16). The three-dimensional structure identified through Exploratory Factor Analysis (EFA) not only provided statistical validation of the instrument but also established a conceptual framework for interpreting the SOM’s topology. From a practical perspective, the findings highlight the relevance of consumer heterogeneity and suggest that consumer knowledge may play an important role in the acceptance of biopreservation technologies. These findings provide useful insights for future studies aimed at understanding the factors that influence consumer acceptance of biopreservation technologies. © The authors © Applied Sciences © MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sensory characterization of functional guava symbiotic petit cheese product(2023); ; 1 15Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 1 19Scopus© Citations 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products<jats:p>Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers’ acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer’s acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products.</jats:p>Scopus© Citations 41 1 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, BIOPLASTIC MADE OF ORANGE PEELS(2022); ; ; 1 27