Han-Seok Seo

Food color is commonly quantified using colorimeters; however, these instruments may be impractical in home‐based or small‐scale production settings. More accessible alternatives, such as digital image analysis and computer vision, may offer reliable color quantification when conventional instrumentation is unavailable. The objective of this study was to identify the color‐measurement method and sampling strategy that most accurately and reliably predict consumers’ perceived cookie color. Three approaches to characterizing the color of chocolate chip cookies were compared and evaluated for concordance with human perception: instrumental colorimetry (HunterLab colorimeter), digital image analysis (Adobe Photoshop), and computer‐vision analysis (OpenCV/Python). One hundred consumers evaluated the perceived lightness, redness, and yellowness of 15 cookie samples (3 cookies × 5 commercial brands) using 15‐cm line scales designed to approximate L *, a *, and b * color constructs. Instrumental and image‐based measurements were collected under two sampling conditions: (1) four standardized surface locations on each cookie (selected point) and (2) whole‐surface averaging (whole cookie). Across methods, selected‐point sampling generally produced higher L * values than whole‐cookie sampling. Multivariate analyses showed that all methods differentiated among brands. However, agreement with sensory configurations was strongest for instrumental colorimetry under selected‐point sampling and for computer vision under whole‐cookie sampling. Predictive modeling further indicated that selected‐point instrumental colorimetry most consistently predicted perceived lightness and yellowness, whereas whole‐cookie computer‐vision generated the best‐performing predictions of perceived redness. In conclusion, selected‐point colorimetry showed the closest correspondence with perceived cookie color in this dataset, while computer vision demonstrated strong promise as a practical alternative for color quantification in resource‐limited settings.

Limited research has systematically compared the detailed sensory profiles of commercially available gluten-containing (C) and gluten-free (F) cookies using trained panelists. This study aimed to develop a comprehensive sensory lexicon for C and F chocolate chip cookies and identify key sensory attributes that differentiate them. Seven professionally trained panelists created a lexicon of 33 attributes spanning aroma, flavor, basic taste, texture, and residual property. Using this lexicon, a descriptive analysis was conducted on 12 C and 12 F cookie samples. Multivariate analysis of variance revealed significant differences between the two groups across the 33 sensory attributes (p < 0.05). A mixed model analysis showed that C cookies had higher intensities of chocolate-related and sweet aroma complex notes, while F cookies exhibited stronger nutty, artificial, and off-note flavors. In terms of texture, F cookies were higher in toothpack and powdery mouthcoat, while C cookies displayed more melt-in-mouth characteristics. Principal component analysis and agglomerative hierarchical clustering revealed three distinct clusters of test samples within both crispy and chewy cookie types, with some F cookies closely aligning with C profiles. These findings, along with the developed lexicon, provide a valuable foundation for enhancing the sensory appeal and quality of gluten-free chocolate chip cookies.

The gluten‐free food market is expanding rapidly. However, consumer acceptance and emotional responses to gluten‐free food, especially commercially available products, have received relatively underexplored. This study aimed to determine how consumer perceptions, acceptance, and emotional responses differ between gluten‐containing (C) and gluten‐free (F) chocolate chip cookies from three brands under both blind and labeled conditions. In the labeled condition, 84 participants without celiac disease evaluated 3 F cookies labeled “gluten‐free” and 3 C cookies presented without claims on a plain white card. In the blind condition, they tested the six cookies without labels. Participants provided hedonic ratings, Just‐About‐Right (JAR) ratings, and checked their emotional responses for each cookie. Results showed that participants liked the texture and overall aspects of the C cookies more than their F counterparts. Significant variations were observed in hedonic ratings of appearance, flavor, texture, and overall impression between C and F cookies, with brand playing a notable role. Although the gluten condition had a minimal effect on emotional responses to C and F cookies, this impact varied by brand. The influence of gluten‐free labeling on sensory and emotional responses to C and F cookies was limited. In conclusion, this study demonstrated that sensory and emotional responses to gluten‐containing versus gluten‐free cookies can differ significantly across brands, and gluten‐free claims had little impact on these responses. Practical Application: Our study revealed that gluten‐free cookies from some brands can rival their gluten‐containing counterparts. However, there is still room for improvement in the sensory quality of gluten‐free products, particularly regarding texture.

The demand for gluten‐free products has increased significantly over the past few decades. This study aimed to determine differences in consumer acceptance, emotional responses, and purchase intent between gluten‐containing versus gluten‐free chocolate chip cookies. Twelve commercially available chocolate chip cookie products from 11 brands were evaluated, including 7 gluten‐free and 5 gluten‐containing options. Eighty‐nine participants without celiac disease took part in this 2‐day study, evaluating six cookies per day. Participants rated sensory acceptance (appearance, flavor, texture, and overall liking) using a 9‐point hedonic scale and evaluated specific sensory attributes (chocolate flavor, sweetness, chewiness, and hardness) using a 5‐point just‐about‐right (JAR) scale. Emotional responses were measured using a circumplex‐inspired emotion questionnaire, and purchase intent was rated on a 9‐point scale. Results showed that gluten‐free cookies received significantly lower hedonic ratings for flavor, texture, and overall impression compared to gluten‐containing cookies ( p  < 0.05). Among gluten‐free samples, 44.14%, 34.03%, 21.99%, and 43.02% of participants rated chocolate flavor, sweetness, hardness, and chewiness, respectively, as “too little,” whereas 38.04% rated hardness as “too much.” These deviations from ideal intensities significantly reduced overall liking for the gluten‐free cookies samples ( p  < 0.05). Additionally, gluten‐free cookies more frequently elicited negative valence‐related emotions, such as “blue/uninspired,” “unhappy/dissatisfied,” “tense/bothered,” and “jittery/nervous,” and were associated with lower purchase intent scores ( p  < 0.05). In conclusion, our findings indicate that most gluten‐free cookies evaluated in this study remain less favored than their gluten‐containing counterparts in terms of consumer acceptance, evoked emotions, and purchase intent. Practical Applications: These findings provide valuable guidance for product developers and sensory professionals aiming to enhance the quality of gluten‐free cookies. Improving key sensory attributes, especially flavor and texture (chocolate flavor, sweetness, hardness, and chewiness), has the potential to significantly increase consumer acceptance and emotional satisfaction. Enhancing the sensory and emotional appeal of gluten‐free products can elevate both the diversity and quality of options available on the market, ultimately benefiting consumers who require gluten‐free diets by offering a wider selection of enjoyable and emotionally satisfying products.

Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across spatio-temporal domains. Our proposed approach efficiently uses transformer-based encoders to alleviate the weakly supervised setting of group activity recognition. By leveraging the benefits of transformer models, our approach can model long-term relationships along spatio-temporal dimensions. Our proposed SoGAR method achieves state-of-the-art results on three group activity recognition benchmarks, namely JRDB-PAR, NBA, and Volleyball datasets, surpassing the current state-of-the-art in terms of F1-score, MCA, and MPCA metrics.

The pet food industry is a growing business launching a variety of new products in the market. The acceptability or preference of pet food samples has traditionally been measured using either one‐bowl or two‐bowl tests. Academic researchers and professionals in the pet food industry have explored other methods, including the cognitive palatability assessment protocols and the ranking test, to evaluate more than two samples. A variety of approaches and perspectives were also utilized to predict palatability and key sensory attributes of pet foods, including descriptive sensory analysis by human‐trained panelists and pet food caregivers’ perceptions of pet food. This review article examined a range of testing methods for evaluating the palatability of pet foods, specifically targeting products for dogs and/or cats. It outlined the advantages and disadvantages of each method. Additionally, the review provided in‐depth insights into the key sensory attributes of pet foods and the methodologies for assessing palatability. It also explored pets’ behavioral responses and facial expressions in relation to different pet foods. Furthermore, this review discussed current challenges and future opportunities in pet food development, including the use of instrumental analyses and artificial intelligence–based approaches.