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The sensory characteristics of foods have become an important part of measuring the quality of foods, and the descriptive analysis of sensory characteristics is of great significance to the evaluation of the sensory characteristics of foods. Currently, the commonly used ‘static’ sensory descriptive methods include quantitative descriptive analysis (QDA) primarily based on expert panels, and the check-all-that-apply (CATA) and rate-all-that-apply (RATA) methods that are available for experts or consumers to profile the overall sensory characteristics of a product. However, the sensory characteristics of foods can change during consumption, so ‘static’ description methods are unable to fully and accurately represent the sensory characteristics of food products and their delicate changes. Therefore, dynamic sensory description methods such as temporal check-all-that-apply (TCATA), time intensity (TI), and temporal dominance of sensation (TDS), which more accurately and completely describe the sensory characteristics of food products, have attracted great attention. In this article, several common ‘static’ and dynamic sensory description methods and their applications are introduced in order to provide methodological guidance for research on the sensory characteristics of food products.

ABSTRACT This study investigated the effects of varying concentrations of peanut shell flavonoids (PSFs) on the properties of peanut meal extract‐tilapia skin protein composite films and their impact on cherry tomatoes preservation. Peanut meal alcohol extract (Pe) and tilapia skin protein (Co) were used as base materials, combined with PSFs to prepare composite films with excellent antioxidant properties. The results demonstrated that the optimized composite films exhibited superior mechanical properties, with a tensile strength of 9.83 MPa and an elongation at break of 204.04%. Increased flavonoid content enhanced the films' antioxidant capacity, achieving superoxide anion and DPPH radical scavenging rates of 15.08% and 21.37%, respectively. The microstructure, IR spectra, and circular dichroism spectra of the composite films are also significantly different with the change of flavonoid content. In the cherry tomato preservation experiment, the PeCo‐0.5 composite film treatment group maintained a low weight loss rate (11.18%) and malondialdehyde content (13.33 μmol/g) after 15 days, delayed the peak respiration rate, and significantly reduced the peak respiration intensity (3.43 mgCO2∙ mg−1∙gh−1). At the same time, the activity of polyphenol oxidase in the tissue of Cherry Tomatoes was significantly inhibited, and the decrease rate of Vc content was also significantly decreased, which effectively preserving the bright color, smooth appearance, and good aroma of the cherry tomatoes. This study not only provides new insights into the comprehensive utilization of tilapia skin and peanut by‐products but also opens new avenues for the development and application of fruit and vegetable preservation films. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence that diet and aging are associated; however, few studies have examined the relationship between macronutrient subtypes and phenotypic age acceleration, and the extent to which BMI (body mass index) mediates this association is unclear. Methods: This study included 6911 individuals who were 20 years or older and had participated in the National Health and Nutrition Examination Survey. Daily macronutrient intakes were calculated and classified by the quartile of their subtypes. PhenoAgeAccel was calculated as an aging index using nine chemistry biomarkers. Multivariable linear regression and isocaloric substitution effects were used to evaluate the association of macronutrients with PhenoAgeAccel. Mediation analyses were used to examine the mediation role of BMI in the association. Results: After adjusting for the potential covariates, the consumption of high-quality carbohydrates (β = −1.01, 95% CI: −1.91, −0.12), total protein (β = −2.00, 95% CI: −3.16, −0.84), and plant protein (β = −1.65, 95% CI: −2.52, −0.78) was negatively correlated with PhenoAgeAccel; the consumption of SFAs (β = 1.77, 95% CI: 0.72, 2.81) was positively correlated with PhenoAgeAccel. For every serving of low-quality carbohydrates/animal protein and other calories replaced by one serving of high-quality carbohydrates/plant protein, PhenoAgeAccel would be reduced by about 25 percent. The ratio between BMI-mediated high-quality carbohydrates and PhenoAgeAccel accounted for 19.76% of the total effect, while the ratio between BMI-mediated total fat and PhenoAgeAccel accounted for 30.78% of the total effect. Conclusions: Different macronutrient consumption subtypes are related to PhenoAgeAccel, which is partially mediated by BMI, depending on the quality of macronutrients. Replacing low-quality macronutrients with high-quality macronutrients might slow aging. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to investigate the relationship between the plant-based dietary index and vision impairment (VI), hearing impairment (HI), and dual sensory impairment (DSI) among Chinese aged 65 and older. Methods: Based on the 2018 data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a cross-sectional study was conducted on 14,859 samples. The assessment of dietary quality utilized the plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI). Logistic regression analysis was used to examine the associations between PDIs and sensory impairments. Additionally, restricted cubic spline analysis was utilized to investigate the nonlinear association between PDIs and sensory impairments. Results: Participants in the highest quintile of PDI exhibited reduced prevalence of VI (OR 0.78, 95% CI: 0.67–0.90, ptrend <0.001), HI (OR 0.83, 95% CI: 0.70–0.99, ptrend <0.001), and DSI (OR 0.62, 95% CI: 0.51–0.77, ptrend <0.001) relative to those in the lowest quintile. Moreover, individuals who ranked in the highest quintile for hPDI exhibited a 25% reduced risk of VI disease. Conversely, those in the highest quintile of uPDI were associated with increased prevalence of VI (OR 1.37, 95% CI: 1.17–1.61, ptrend <0.001), HI (OR 1.36, 95% CI: 1.12–1.65, ptrend <0.001), and DSI (OR 1.56, 95% CI: 1.25–1.95, ptrend <0.001). The relationship between PDIs increasing by every 10 units and sensory impairments showed similar patterns. Notably, hPDI demonstrated a nonlinear relationship with HI (pfor nonlinearity = 0.001), while the others exhibited linear associations. Conclusion: The increase in PDI and hPDI correlates with a reduced prevalence of one or more sensory impairments. Conversely, an increase in uPDI is associated with an elevated prevalence of multiple sensory impairments. Our study findings emphasize the significance of plant-based food quality, advocating for adherence to a plant-based dietary pattern while reducing the intake of less healthy plant foods and animal-based products. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. Purpose: To introduce a temporal‐regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. Study Type: Retrospective. Population: 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. Field Strength/Sequence: Sagittal 2D IW‐TSE‐FS (IW) and 3D‐DESS‐WE (DESS) at 3T. Assessment: Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12‐month, and 24‐month were automatically segmented and served as inputs to form compartment‐based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet‐169 were compared. Statistical Tests: DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. Results: The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%–45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%–67% vs. 12%–37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12‐month: 52% vs. 39%, 24‐month: 31% vs. 48%). Data Conclusion: The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. Level of Evidence: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]