Search Results (143)

The grading of beef appearance quality is crucial for standardizing market circulation and promoting the upgrading of the beef cattle industry. China’s current beef quality grading system, which relies primarily on human sensory-based visual assessment with marbling and meat color as core parameters, suffers from strong subjectivity, low efficiency, and large errors. This study proposes a rapid grading method for beef rib eye muscle using smartphone imaging combined with ImageJ software. Standardized images were acquired, and ImageJ was employed for grayscale conversion, threshold segmentation, and morphological processing to extract length, width, area, and marbling proportion. The R, G, B color channels were separated to calculate the R/(R + G + B) color ratio. Pearson correlation analysis showed that the ImageJ results were highly consistent with manual measurements (correlation coefficients > 0.97), indicating good reliability. A five-level grading standard (A1–A5) was established, characterized by low cost, simple operation, and objective results. It provides an economical technical solution for beef quality grading and facilitates the intelligent development of the industry. It should be noted that this experimental grading model has only been validated under the specific experimental conditions of this study, and further verification is required for broader application. Full article

Colonial cheese production represents a valuable cultural and economic activity in southern Brazil. This study evaluated the effect of oral supplementation of dairy cows with an essential oil blend (EOB)—a combination of eucalyptus oil, peppermint oil, and menthol crystals—on the chemical composition and quality parameters of Colonial cheese during 21 days of ripening. Nine dairy cows were randomly assigned to three groups: control, EOB3.6 (3.6 g/cow/day), and EOB7.2 (7.2 g/cow/day). Milk from each treatment was used to produce Colonial cheeses, which were analyzed for physicochemical composition, texture, color, lipid profile, thiobarbituric acid reactive substances (TBARS), and microbiological quality at different ripening stages. Data were analyzed by analysis of variance (ANOVA) using SAS^® software, following verification of normality and homogeneity of variances. When assumptions were met, repeated-measures ANOVA was applied, and means were compared using Tukey’s test (p < 0.05). Sensory data were evaluated by ANOVA using XLSTAT^® (Addinsoft, Paris, France). EOB supplementation maintained the physicochemical integrity of the cheeses, with a gradual increase in fat content during maturation (40 g/100 g at 21 days, p < 0.05). At seven days, the EOB7.2 treatment showed lower lipid oxidation (TBARS = 0.063, p < 0.05), indicating antioxidant potential. Significant interactions between treatment and maturation were observed for color parameters and polyunsaturated fatty acids (PUFA) (p < 0.05). Cheeses from EOB7.2 presented higher saturated fatty acids (SFA) and lower unsaturated fatty acids (UFA) compared with the control (p < 0.05). No Salmonella spp. or Staphylococcal enterotoxins were detected. Counts of coagulase-positive Staphylococcus, molds, and yeasts remained stable, while Escherichia coli counts were lower in EOB-supplemented cheeses throughout ripening. Overall, EOB supplementation improved oxidative stability and microbiological safety without compromising the technological or compositional quality of Colonial cheese. Full article

The ambiance-based approach to old urban fabrics has emerged as a response to the evolution of heritage, focusing on the spirit of place and the relationship between people and their environment. It aims to preserve the identity of architectural and urban spaces, incorporating intangible elements beyond their physical character. In Algeria, colonial-era urban fabrics continue to structure cities. Skikda, a city in eastern Algeria was created ex-nihilo during this era. In this context, Didouche Mourad Street—the main thoroughfare and structuring element of the city—constitutes the core of the analysis. This study focuses on the French colonial period (1838–1962), considered a foundational phase in the spatial and sensory formation of the street. It aims to restitute the sensory urban ambiences of this period and to analyse their evolution in order to identify sensory permanences contributing to the heritage identity of the place. A thematic content analysis was used to identify sensory ambiences, supported by NVivo software to quantify their recurrences and analyse their spatio-temporal dynamics. The findings show that some ambiences have persisted, others have disappeared, and new ones have emerged through successive transformations. By documenting the sensory history of the street, this research proposes a conceptual and methodological framework for the interpretation of heritage urban ambiences and for informing contemporary rehabilitation approaches, considering permanent ambiences as interpretative tools and reference points for understanding heritage dynamics. Full article

Background: Rehabilitation technologies, including assistive devices, adaptive software, and robotic systems, are increasingly integral to contemporary rehabilitation practice. Yet, ensuring that training in their use is inclusive and accessible remains a critical challenge. Methods: This study reports findings from patient and public involvement (PPI) activities conducted by the National Institute for Health and Care Research (NIHR) HealthTech Research Centre in Rehabilitation. Fifteen contributors participated, comprising rehabilitation professionals and educators, individuals with lived experience of serious illness, injury, or disability requiring rehabilitation, and technology innovators. The purpose of these activities was to identify the factors necessary to ensure that training in rehabilitation technologies is equitable for people with sensory, cognitive, and physical impairments. Findings: Contributors highlighted a series of priority domains that together capture the breadth of challenges and opportunities in this area. These included the need to address physical, sensory, and cognitive accessibility; to foster participation, motivation, and engagement; to strengthen instructional design and delivery; to ensure technological accessibility and integration; to enhance staff training and competence; and to embed participant-centred and policy approaches. Contributions in these domains were synthesised into thematic categories that provide a structured understanding of the training requirements of rehabilitation technology recipients. Evaluation: The PPI process was evaluated using the Guidance for Reporting Involvement of Patients and the Public (GRIPP2) Short Form, supplemented by an evaluation survey. This dual approach ensured that the contributions were systematically documented and critically appraised. Implications: Guided by implementation science, the principal output of this work was a co-created stakeholder engagement strategy, structured using the Implementation STakeholder Engagement Model (I-STEM). This plan will serve as a foundation for future research exploring the education and training needs of diverse stakeholder groups, thereby contributing to the development of more inclusive and effective rehabilitation technology training practices. Full article

This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing hardware module, Senseair K96, that integrates both a non-dispersive infrared (NDIR)-based gas sensing unit and a BME280 environmental sensing unit. To address the outdoor operation challenges caused by environmental fluctuation due to the varying temperature, humidity, and pressure, from the software aspect, multiple machine learning-based regression models were trained in this work on 13,125 calibration data points collected under controlled laboratory conditions. Among ten tested algorithms, the Multilayer Perceptron (MLP) and Elastic Net models achieved the highest accuracy, with R-squared coefficient $R^2>0.8$ on both indoor and outdoor scenarios, and with inter-sensor root mean square error (RMSE) within 1.5 ppm across four identical instruments. Moreover, field mobile validation was performed near a wastewater management facility using this solution, confirming a strong correlation with LI-COR reference measurements and a reliable detection of ${\mathrm{CH}}_4$ leaks with concentrations up to 18 ppm at the test site. Overall, this machine learning-integrated NDIR sensing solution (i.e., AIMNet) offers a practical and scalable solution towards a more robust distributed ${\mathrm{CH}}_4$ monitoring network for real-world field-deployable applications. Full article

With the introduction of emerging digital technologies, sensory and consumer science has evolved beyond traditional laboratory-based and self-response-centered sensory evaluations toward more objective assessments that reflect real-world consumption contexts. This review examines recent trends and potential applications in sensory evaluation research focusing on key enabling technologies—artificial intelligence (AI) and machine learning (ML), extended reality (XR), biometrics, and digital sensors. Furthermore, it explores strategies for establishing personalized, multimodal, and intelligent–adaptive sensory evaluation systems through the integration of these technologies, as well as the applicability of sensory evaluation software. Recent studies report that AI/ML models used for sensory or preference prediction commonly achieve RMSE values of approximately 0.04–24.698, with prediction accuracy ranging from 79 to 100% (R² = 0.643–0.999). In XR environment, presence measured by the IPQ (7-point scale) is generally considered adequate when scores exceed 3. Finally, the review discusses ethical considerations arising throughout data collection, interpretation, and utilization processes and proposes future directions for the advancement of sensory and consumer science research. This systematic literature review aims to identify emerging technologies rather than provide a quantitative meta-analysis and therefore does not cover domain-specific analytical areas such as chemometrics beyond ML approaches or detailed flavor and aroma chemistry. Full article

Visual impairment (VI) affects over two billion people globally, with prevalence increasing due to preventable conditions. To address mobility and navigation challenges, this study presents a multi-platform, multi-sensor Electronic Travel Aid (ETA) integrating a combination of ultrasonic, LiDAR, and vision-based sensing across head-, torso-, and cane-mounted nodes. Grounded in orientation and mobility (OM) principles, the system delivers context-aware haptic and auditory feedback to enhance perception and independence for users with VI. The ETA employs a hardware–software co-design approach guided by proxemic theory, comprising three autonomous components—Glasses, Belt, and Cane nodes—each optimized for a distinct spatial zone while maintaining overlap for redundancy. Embedded ESP32 microcontrollers enable low-latency sensor fusion providing real-time multi-modal user feedback. Static and dynamic experiments using a custom-built motion rig evaluated detection accuracy and feedback latency under repeatable laboratory conditions. Results demonstrate millimetre-level accuracy and sub-30 ms proximity-to-feedback latency across all nodes. The Cane node’s dual LiDAR achieved a coefficient of variation at most 0.04%, while the Belt and Glasses nodes maintained mean detection errors below 1%. The validated tri-modal ETA architecture establishes a scalable, resilient framework for safe, real-time navigation—advancing sensory augmentation for individuals with VI. Full article

Freezing of gait (FoG) is a common motor symptom in advanced Parkinson’s disease, leading to falls, disability, and reduced quality of life. Although cueing systems using visual or auditory stimuli can help patients resume walking, existing solutions are often expensive, uncomfortable, and conspicuous. ParCuR (Parkinson Cueing and Rehabilitation) is a compact, ankle-worn wearable integrating an inertial sensor, haptic stimulator, and AI-based software. It was developed to detect FoG episodes in real time and provides automatic sensory cues to assist patients with Parkinson’s Disease (PwP). A classifier was trained for FoG detection using the DAPHNet dataset, comparing patient-specific and patient-independent models. While a small-scale trial with PwP assessed usability and reliability. ParCuR is watch-sized (35 × 41 mm), discreet, and comfortable for daily use. The online detection algorithm triggers stimulation within 0.7 s of episode onset and achieves 94.9% sensitivity and 91.3% specificity using only 14 frequency-based features. Preliminary trials confirmed device feasibility and guided design refinements. This low-cost, wearable solution supports personalized, real-time FoG detection and responsive cueing, improving patient mobility while minimizing discomfort and continuous stimulation habituation. Full article

Tomato is one of the most important vegetable crops worldwide, with about one quarter of the yearly production of fresh fruits dispatched to the processing industry. Paste, canned tomatoes, and sauces represent the three leading categories. Background/Objectives: The metabolic profile of processed tomatoes can be modified by several production steps, affecting the nutritional and sensory profile of the finished product. Despite this, a detailed metabolomic profiling of transformed tomatoes is currently missing. The goal of this investigation is to provide qualitative metabolomic profiling of tomato purees with two main advances: first, the use of a more sustainable analytical approach based on a single extraction protocol and one-shot analysis for multiple information retrieval on different compound classes; second, the achievement of a curated database consolidated over a wide collection of commercial samples representative of the Italian market. Methods: A non-selective ethanol extraction was applied to collect the main polar metabolites followed by untargeted high-resolution MS/MS analysis and software-based compound identification. Results: A list of more than five hundred features was collected and assigned to specific compounds or compound groups with different confidence levels. The results confirmed the persistence in processed tomatoes of the main primary and secondary metabolites already reported in fresh fruits, such as essential amino acids, sugar, organic acids, vitamins, fatty acyls, and phytohormones. Moreover, new insight on specific components never traced before in similar finished samples is provided. Bioactive compounds were detected in all samples, such as oligopeptides with ACE-inhibitor activity, ɣ-aminobutyric acid, alkaloids, and polyphenols (flavonoids, coumarins, and cinnamic acids). Many of these compounds have antioxidant activities, proving the relevance of transformed tomatoes as a source of health-promoting compounds for the human diet. Conclusions: A detailed metabolic profile of commercial tomato puree samples was obtained, and a curated database of metabolites was compiled, which can be useful for multiple purposes, for example, authentication, quality, or nutritional assessments. Full article

In this study, six club raspberry varieties were examined for their aromatic profiles and sensory qualities, and statistical approaches were used to determine how aroma components affect consumer impressions. Analysis of the aroma’s chemical composition was performed utilizing headspace SPME and GC-MS. MS-DIAL -v5.5.250627 software was used to identify components from commercial libraries, after 10 repetitions for each variety, followed by manual verification. A sensory evaluation of fresh fruits, with 55 volunteers, was statistically analyzed and linked to chemical composition using multivariate analysis and the OPLS-HDA classification method, which was employed for the first time. Tula Magic was scored the highest in the sensory evaluation compared to Adelita, Himbo Top, Glen Dee, San Rafael, and Cascade Harvest. 2-Heptanol (fresh, lemongrass-like, herbal, floral, fruity, green), heptanal (fresh, aldehydic, fatty, green, herbal), and 2-methyl-6-hepten-1-ol (oily-green, herbaceous-citrusy) separated Tula Magic from the other varieties assessed. The same components were recognized in OPLS as positive contributors to the flavor score, while terpenoids like trans-β-ionone, α-ionone, and α,β-dihydro-β-ionone, as well as 2-heptanone, scored slightly lower. This suggests that a fine balance between the individual components is key to the overall aroma sensation. Full article

International standards for wines with Protected Designation of Origin (PDO) require characterisation through both analytical and sensory criteria, although sensory evaluation remains inherently subjective, especially regarding organoleptic properties. This study examined paired Blanc de noir and red wines made from identical grape varieties to determine whether varietal traits remain perceptible regardless of the vinification method while also assessing the role of visual stimuli in influencing olfactory and gustatory perception. Controlled tastings were conducted using both transparent and opaque glassware, with experienced panellists recording sensory descriptors. Physicochemical parameters were measured using a Lyza 5000 analyser to confirm compliance with quality standards, while statistical analyses of sensory data were conducted using the XLSTAT–Basic, student-type user software. Results showed that the absence of visual cues did not mislead tasters in recognising core attributes; however, the winemaking method significantly affected descriptors linked to maceration, including flavour intensity, astringency, and red/dark fruit notes. Panellists distinguished between white and red wines at statistically significant levels, even without visual input, suggesting that vinification-related chemical composition primarily guided their perception. Direct correlations were observed between red winemaking descriptors and parameters such as pH, lactic acid, glycerol, and volatile acidity, while indirect correlations were found with malic acid and titratable acidity. The results highlight how winemaking methods, chemical composition, and sensory perception interact in defining varietal characteristics. Full article

Background: Children with diplegic Cerebral Palsy often exhibit upper-limb (UL) motor impairments compounded by deficits in visuospatial, sensory, and executive functions. Despite this, research has primarily focused on lower-limb rehabilitation, leaving the treatment of UL function in diplegic Cerebral Palsy underexplored. Action Observation Therapy (AOT), based on Mirror Neuron System activation, has shown promise in promoting motor recovery, but evidence specific to this population is limited. This exploratory randomized controlled trial (RCT) aims to assess the feasibility and effectiveness of a home-based AOT program—ACT ON DIP—for improving upper-limb function in children and adolescents with diplegic Cerebral Palsy. Methods: Fifty-four participants with spastic diplegic Cerebral Palsy (MACS and GMFCS levels I–III, aged 5–16 years) will be randomly assigned to an experimental group (receiving an 8-week home-based AOT program) or a control group (receiving standard care). The ACT ON DIP system includes an ad hoc software, kits of objects for daily tasks, and wearable sensors. The system allows for delivering structured uni- and bimanual AOT activities tailored to the child’s profile. Primary outcome is the Both Hands Assessment (BoHA); secondary outcomes include motor (MA-2, BBT, ABILHAND), neuropsychological (NEPSY-II, Corsi Test, BRIEF), and participation measures (COPM, PEM-CY, CP-QOL). A subgroup will undergo fMRI to explore neural correlates of training-related changes. Results: Feasibility, compliance, and user experience with the home-based system will be assessed. This study will evaluate short-, medium-, and long-term changes in UL performance and related neuropsychological functions. Conclusions: ACT ON DIP represents a novel, personalized, and accessible tele-rehabilitation intervention for children with diplegic Cerebral Palsy. If effective, it could expand treatment opportunities for UL rehabilitation in this population and support broader implementation of home-based AOT. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Background/Objectives: The infraorbital canal (IOC) is a critical anatomical structure that passes through the anterior surface of the maxilla and opens at the infraorbital foramen, containing the infraorbital nerve, artery, and vein. Accurate localization of this canal in maxillofacial, dental implant, and orbital surgeries is of great importance to preventing nerve damage, reducing complications, and enabling successful surgical planning. The aim of this study is to perform automatic segmentation of the infraorbital canal in cone-beam computed tomography (CBCT) images using an artificial intelligence (AI)-based model. Methods: A total of 220 CBCT images of the IOC from 110 patients were labeled using the 3D Slicer software (version 4.10.2; MIT, Cambridge, MA, USA). The dataset was split into training, validation, and test sets at a ratio of 8:1:1. The nnU-Net v2 architecture was applied to the training and test datasets to predict and generate appropriate algorithm weight factors. The confusion matrix was used to check the accuracy and performance of the model. As a result of the test, the Dice Coefficient (DC), Intersection over the Union (IoU), F1-score, and 95% Hausdorff distance (95% HD) metrics were calculated. Results: By testing the model, the DC, IoU, F1-score, and 95% HD metric values were found to be 0.7792, 0.6402, 0.787, and 0.7661, respectively. According to the data obtained, the receiver operating characteristic (ROC) curve was drawn, and the AUC value under the curve was determined to be 0.91. Conclusions: Accurate identification and preservation of the IOC during surgical procedures are of critical importance to maintaining a patient’s functional and sensory integrity. The findings of this study demonstrated that the IOC can be detected with high precision and accuracy using an AI-based automatic segmentation method in CBCT images. This approach has significant potential to reduce surgical risks and to enhance the safety of critical anatomical structures. Full article

Three-dimensional food printing (3DFP), a specialized application of additive manufacturing (AM), employs a layer-by-layer deposition process guided by digital image files to fabricate edible structures. Utilizing heavily modified 3D printers and Computer-Aided Design (CAD) software technology allows for the precise creation of customized food items tailored to individual aesthetic preferences and nutritional requirements. Three-dimensional food printing holds significant potential in revolutionizing the food industry by enabling the production of personalized meals, enhancing the sensory dining experience, and addressing specific dietary constraints. Despite these promising applications, 3DFP remains one of the most intricate and technically demanding areas within AM, particularly in the context of modern gastronomy. Challenges such as the rheological behaviour of food materials, print stability, and the integration of cooking functions must be addressed to fully realize its capabilities. This article explores the possibilities of applying classical modified 3D printers in the food industry. The behaviour of certain recipes is also tested. Two test case scenarios are covered. The first scenario is the work and formation of a homogenized meat mass. The second scenario involves finding a chocolate recipe that is suitable for printing relatively detailed chocolate decorative elements. The current advancements, technical challenges, and future opportunities of 3DFP in the field of engineering, culinary innovation and nutritional science are also explored. Full article