Search Results (41)

Grape marc spirits represent a significant category within the alcoholic beverage sector, particularly across Mediterranean Europe. This study aimed to construct a sensory flavor wheel—covering aroma, taste, and mouthfeel modalities—specifically for non-flavored and non-wood-aged grape marc distillates. To achieve this, we explored the feasibility of a novel methodological approach combining three rapid sensory techniques: Ultra Flash Profiling (UFP), Check-All-That-Apply (CATA), and Rate-All-That-Apply (RATA). Forty-five (45) samples from Greece, Cyprus, and Italy were evaluated by a trained panel of 12 assessors. UFP generated 205 initial descriptors, which were refined to 59 for CATA. Despite the long attribute list, CATA data helped identify the most relevant terms for the final RATA experiment. The sequential application of these methods, along with intermediate data filtering, led to the selection of 45 key descriptors with occurrence frequencies ranging from 33.3% to 97.7%. These were organized into a comprehensive flavor wheel grouped into 12 general categories. This approach offers a flexible framework for future flavor wheel construction in other under-characterized product categories. Full article

Cannabis sativa L. exhibits a complex sensory profile governed by a diverse range of volatile and non-volatile compounds. Volatile constituents—such as terpenes, aldehydes, ketones, esters, and sulfur-containing compounds—together with non-volatile taste-active molecules including flavonoids and phenolic compounds, underlie its distinctive aroma and flavor. This review examines how genetic diversity, cultivation practices, and post-harvest processing modulate the synthesis, accumulation, and chemical transformation of these metabolites in the cannabis flower. It discusses recent advancements in the extraction, identification, and quantification of these compounds, highlighting the crucial integration of chemical characterization with sensory evaluation. By synthesizing findings from advanced analytical methodologies, this review addresses the challenges and opportunities involved in defining the sensory profiles of C. sativa L. varieties. Drawing insights from research on other consumer plants, strategies for future innovations are outlined, including the discovery of novel aroma and flavor compounds and the development of a universal cannabis aroma and flavor wheel. This work aims to support advancements in breeding programs, enhance product quality control, and guide future research in cannabis sensory science. Full article

Joint actuators are the key components in the innovation and iterative optimization of the robots, with a significant impact on both the performances of robots and manufacturing costs. Conventional industrial collaborative robots often use high-precision position and torque sensors, which are not cost-effective or energy-efficient in specific applications like assistive exoskeletons, legged robots, or wheeled robots. Alternatively, we propose a triboelectric-assisted sensory actuator that balances lightweight design, performance, and affordability for large-scale applications. The actuator is composed of a high-power density motor, a low reduction gearbox, and integrated with a rotational triboelectric sensor, which leads to high dynamic performances and low power consumption. The feasibility of the prototype is initially verified by characterizing the angular positioning accuracy and the back drivability. Experiments indicate that the rotational triboelectric sensor is able to accurately detect the angular displacement of the actuator with the self-generated signals. Overall, a highly integrated actuator module with the actuation and sensing circuit is fabricated as a compact design ready for assembling a complete intelligent robot. This actuator holds great potential as a cost-effective, energy-efficient, and versatile solution for modern robotics, crucial for advancing this field and improving human convenience. Full article

Legged robots, designed for high adaptability, are poised for deployment in hazardous tasks traditionally undertaken by humans, particularly in unstructured terrains where their wheeled counterparts cannot operate. Nevertheless, using them in unstructured settings necessitates advanced control techniques to maneuver without depending entirely on visual signals or pre-programmed instructions. To address these challenges, this research proposes a novel walking algorithm for quadruped robots that blends a stable blind gait without needing any visual cues with deep reinforcement learning to enhance mobility across diverse terrains. The algorithm’s effectiveness was evaluated virtually, emphasizing the ability to regulate the robot’s leg movements and posture when reaching obstacles. Our results demonstrated a success rate of over 90% in the stair-climbing task, suggesting that the algorithm improved the robot’s mobility and stability. Although emphasizing a steady blind gait reduces reliance on visual cues, incorporating the algorithm with further sensory inputs and environmental awareness may improve the robot’s functionality and versatility in practical situations. More dynamic gaits and a wider variety of static and dynamic obstacles will be the focus of future algorithm development. Furthermore, validation in the real world will aid in detecting any shortcomings or potential areas for enhancement in the algorithm, thereby improving its adaptability and resilience in diverse settings and assignments. Full article

Remote driving, i.e., the capacity of controlling road vehicles at a distance, is an innovative transportation technology often associated with potential ethical benefits, especially when deployed to tackle urban traffic issues. However, prospected benefits could only be reaped if remote driving can be executed in a safe and responsible way. This paper builds on notions elaborated in the philosophical literature on technological mediation to offer a systematic examination of the extent to which current and emerging Human–Machine Interfaces contribute to hindering or supporting the exercise of responsibility behind the remote wheel. More specifically, the analysis discusses how video, audio, and haptic interfaces co-shape the remote driving experience and, at the same time, the operators’ capacity to drive responsibly. The multidisciplinary approach explored in this research offers a novel methodological framework to structure future empirical inquiries while identifying finely tuned multi-sensory HMIs and dedicated training as critical presuppositions to the remote drivers’ exercise of responsibility. Full article

To investigate the flavour evolution mechanism of raw Pu-erh tea (RPT) during storage, the volatile and non-volatile compounds of RPT with different storage years (1–10 years) from the same raw material origin, manufacturer, and storage location in Wenshan Prefecture, Yunnan Province, were systematically analysed by HPLC, HS-SPME-GC-MS, and OAV. The results showed that both cluster analyses based on non-volatile and volatile compounds could classify RPT of different storage years into three ageing cycles, with key turning points in the third and eighth years of storage, which is also accompanied by the colour changing from green to orange or brown, the aroma changing from a faint scent to woody and ageing, the astringency diminishing, and the sweet and mellow increasing. Theophylline was identified as the potential marker of RPT stored 1–3 years, while (−)-catechin gallate, (−)-gallocatechin gallate, quercetin, and rutin as those for a storage of 9–10 years. The volatile compounds indicate a general trend of an initial increase followed by a decrease. Forty-four key aroma compounds (OAV ≥ 1) were identified. Eucalyptol, β-Caryophyllene, 2-Amylfuran, Copaene, Estragole, and α-Terpinene originated as potential markers for RPT stored 1–3 years, while (Z)-Linalool oxide (furanoid), α-Terpineol, Terpinen-4-ol, and cis-Anethol were for RPT stored 8–10 years. This study revealed the flavour characteristics and quality changes of RPT over the course of storage, and constructed a sensory flavour wheel, providing theoretical underpinnings for the quality control and assessment of RPT. Full article

Mezcal is a traditional beverage with relevant cultural and economic importance in Mexico, with different Protected Designation of Origin locations. This study focuses on creating a sensory lexicon for Mezcal with local producers by means of Free Choice Profiling. A selection of the most relevant descriptors was made to construct a sensory wheel. Subsequently, a sensory panel evaluated a total of 10 Mezcal samples using the sensory categories defined in the sensory wheel. Additionally, gas chromatography with mass spectrometry was performed to analyze volatile components’ contribution to the aroma and flavor descriptors. A total of 87 terms were selected for the sensory wheel, using 41 descriptors within 10 categories for odor modality and 46 more within 13 categories for flavor modality. The main volatile compounds that were identified were 37 esters, 17 alcohols, 12 ketals and 9 terpenes, which were the foremost contributors to the presence of several sensory descriptors and were also found in most of the Mezcal samples. The quantitative analysis results exhibited a higher floral odor for Mezcal of the Angustifolia variety and the highest smoked odor for an earthenware distilled Mezcal, thus proving that the selection of the descriptors from the wheel was appropriate for differentiating Mezcal samples from different origins, agave species and distillation processes. Therefore, the sensory wheel developed in this study can be used both as a quality control tool and as a marketing tool that allows producers to differentiate their products in the market. Full article

The intricate relationships between chemical compounds and sensory descriptors in distilled spirits have long intrigued distillers, sensory experts, and consumers alike. The importance and complexity of this relation affect the production, quality, and appreciation of spirits, and the success of a product. Because of that, profoundly investigating the different flavor and aroma combinations that the chemical compounds can give to a desired beverage takes an essential place in the industry. This study aims to study these relationships by employing machine learning techniques to analyze a comprehensive dataset with 3051 chemical compounds and their associated aroma descriptors for seven distilled spirit categories: Baijiu, cachaça, gin, mezcal, rum, tequila, and whisk(e)y. The study uses principal component analysis (PCA) to reduce the dimensionality of the dataset and a clustering machine learning model to identify distinct clusters of aroma descriptors associated with each beverage category. Based on these results, an aroma wheel that encapsulates the diverse olfactory landscapes of various distilled spirits was developed. This flavor wheel is a valuable tool for distillers, sensory experts, and consumers, providing a comprehensive reference for understanding and appreciating the complexities of distilled spirits. Full article

Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration. Full article

E-liquids contain combinations of chemicals, with many enhancing the sensory attractiveness of the product. Studies are needed to understand and characterize e-liquid ingredients, particularly flavorings, to inform future research and regulations of these products. We identified common flavor ingredients in a convenience sample of commercial e-liquids using gas chromatography–mass spectrometry. E-liquid flavors were categorized by flavor descriptors provided on the product packaging. A Flavor Ingredient Wheel was developed to link e-liquid flavor ingredients with flavor categories. An analysis of 109 samples identified 48 flavor ingredients. Consistency between the labeled flavor and ingredients used to produce such flavor was found. Our novel Flavor Ingredient Wheel organizes e-liquids by flavor and ingredients, enabling efficient analysis of the link between ingredients and their flavor profiles and allowing for quick assessment of an e-liquid ingredient’s flavor profile. Investigating ingredient profiles and identifying and classifying commonly used chemicals in e-liquids may assist with future studies and improve the ability to regulate these products. Full article

Innovative Tangyang Congou black tea, also known as Minkehong black tea (MKH), is of high quality and hence has gained a reputation on the market. In this study, standard samples of MKH were used as the research material, and the results of sensory evaluation showed that the overall quality of MKH decreased along with the downgrading. The radar chart showed a strong association between higher grades and specific aroma and taste character. Based on the detection of ultra-performance liquid chromatography–mass spectrometry (UPLC–MS) and related analysis, the content of both the ester-type catechins and non-ester catechins decreased along with the downgrading, and the details of their change trends were revealed. The content and dynamic changes in caffeine were also investigated. A total of 19 kinds of amino acids (AAs) were clustered; among them, 4 kinds of AAs, namely, Thr, Leu, Asp, and Ile, significantly contributed to the taste of the tea. Notably, the content of Thr had the highest correlation with the grade of MKH, and the correlation coefficient was 0.991 (p < 0.01). According to gas chromatography–mass spectrometry (GC–MS) detection and analysis, a total of 861 kinds of volatile compounds were detected, the aroma-active compounds in different grades were selected, and an aroma wheel of MKH was constructed. Our results found that non-volatile and volatile compounds not only contribute to defining the level of MKH standard samples but also provide a chemical basis for the measurement of flavour and quality of MKH. Full article

With a millennium-long history, traditional Chinese cereal vinegar (CCV) is a significant part of China’s cultural heritage. The unique flavor of CCV is derived from the use of cereal and its bran as raw materials and solid-state fermentation as a brewing technique. This paper systemically summarized recent research progress on the aroma compounds in CCV, the biochemical generation of aroma compounds during the brewing process, and the association between sensory perception and the primary aroma compounds. Furthermore, a complete CCV lexicon and sensory wheel prototype were constructed. This study aims to lay a foundation for future CCV aroma research, quality improvement, and industrialization. Full article

Neurofibromatosis type 1 (NF1) is caused by mutations in the NF1 gene. The clinical presentation of NF1 includes diverse neurological issues in pediatric and adult patients, ranging from learning disabilities, motor skill issues, and attention deficit disorder, to increased risk of depression and dementia. Preclinical research suggests that abnormal neuronal signaling mediates spatial learning and attention issues in NF1; however, drugs that improve phenotypes in models show inconclusive results in clinical trials, highlighting the need for a better understanding of NF1 pathophysiology and broader therapeutic options. Most NF1 patients show abnormalities in their brain white matter (WM) and myelin, and links with NF1 neuropathophysiology have been suggested; however, no current data can clearly support or refute this idea. We reported that myelin-targeted Nf1 mutation impacts oligodendrocyte signaling, myelin ultrastructure, WM connectivity, and sensory–motor behaviors in mice; however, any impact on learning and memory remains unknown. Here, we adapted a voluntary running test—the complex wheel (CW; a wheel with unevenly spaced rungs)—to delineate fine motor skill learning curves following induction of an Nf1 mutation in pre-existing myelinating cells (pNf1 mice). We found that pNf1 mutant females experience delayed or impaired learning in the CW, while proper learning in pNf1 males is predominantly disrupted; these phenotypes add complexity to the gender-dependent learning differences in the mouse strain used. No broad differences in memory of acquired CW skills were detected in any gender, but gene-dose effects were observed at the studied time points. Finally, nitric oxide signaling regulation differentially impacted learning in wild type (WT)/pNf1, male/female mice. Our results provide evidence for fine motor skill learning issues upon induction of an Nf1 mutation in mature myelinating cells. Together with previous connectivity, cellular, and molecular analyses, these results diversify the potential treatments for neurological issues in NF1. Full article

Derived from the residues of coffee processing, cascara has been consumed traditionally in Yemen, Ethiopia, and Bolivia as a herbal or tea-like infusion due, in part, to its pleasant flavor and nutritional properties. The aim of this work was to investigate the volatile fraction of eight cascaras from different origins and different processes, the pulp and husk of the Coffea arabica L. fruit from Congo, Brazil, Guatemala, Bolivia, Honduras, Colombia and two from Peru. In this study, the volatile compositions of these eight cascaras were characterized to understand their sensorial properties. Using SPME (Solid Phase Micro Extraction), more than one hundred volatile compounds were identified via gas chromatography–mass spectrometry. The assessment of these analyses in parallel with classical sensory analysis provides an idea of the potential use of cascara as an interesting ingredient for the food and beverage sector. Finally, a cascara flavor wheel has been proposed based on a sensory evaluation of all the investigated cascaras. Full article

Solid-state LiDAR offers multiple advantages over mechanism mechanical LiDAR, including higher durability, improved coverage ratio, and lower prices. However, solid-state LiDARs typically possess a narrow field of view, making them less suitable for odometry and mapping systems, especially for mobile autonomous systems. To address this issue, we propose a novel rotating solid-state LiDAR system that incorporates a servo motor to continuously rotate the solid-state LiDAR, expanding the horizontal field of view to 360°. Additionally, we propose a multi-sensor fusion odometry and mapping algorithm for our developed sensory system that integrates an IMU, wheel encoder, motor encoder and the LiDAR into an iterated Kalman filter to obtain a robust odometry estimation. Through comprehensive experiments, we demonstrate the effectiveness of our proposed approach in both outdoor open environments and narrow indoor environments. Full article