Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.

Mobile genetic elements facilitate the transmission of antibiotic resistance genes in multidrug-resistant Enterobacteriaceae from duck farms

Multidrug-resistant(MDR)Enterobacteriaceae critically threaten duck farming and public health.The phenotypes,genotypes,and associated mobile genetic elements(MGEs)of MDR Enterobacteriaceae isolated from 6 duck farms in Zhejiang Province,China,were investigated.A total of 215 isolates were identified as Escherichia coli(64.65%),Klebsiella pneumoniae(12.09%),Proteus mirabilis(10.23%),Salmonella(8.84%),and Enterobacter cloacae(4.19%).Meanwhile,all isolates were resistant to at least two antibiotics.Most isolates carried tet(A)(85.12%),blaTEM(78.60%)and sul1(67.44%)resistance genes.Gene co-occurrence analysis showed that the resistance genes were associated with IS26 and integrons.A conjugative IncFII plasmid pSDM004 containing all the above MGEs was detected in Proteus mirabilis isolate SDM004.This isolate was resistant to 18 antibiotics and carried the blaNDM-5 gene.MGEs,especially plasmids,are the primary antibiotic resistance gene transmission route in duck farms.These findings provide a theoretical basis for the rational use of antibiotics in farms which are substantial for evaluating public health and food safety.

Research and exploration of direct current power supply and distribution systems for Antarctic research stations

The power supply and distribution systems for Antarctic research stations have special characteristics.In light of a worldwide trend toward a gradual increase in the application of renewable energy,an analysis was performed to assess the feasibility of achieving a direct current power supply and distribution at Antarctic research stations by comparing the characteristics of direct current and alternating current electricity.Research was also performed on the status quo and future trends in direct current power supply and distribution systems in Antarctica research stations in combination with case studies.

The Effects of Accumulated Short Bouts of Mobile-Based Physical Activity Programs on Depression,Perceived Stress,and Negative Affectivity among College Students in South Korea:Quasi-Experimental Study

Regular physical activity(PA)is known to enhance multifaceted health benefits,including both physical and mental health.However,traditional in-person physical activity programs have drawbacks,including time constraints for busy people.Although evidence suggests positive impacts on mental health through mobile-based physical activity,effects of accumulated short bouts of physical activity using mobile devices are unexplored.Thus,this study aims to investigate these effects,focusing on depression,perceived stress,and negative affectivity among South Korean college students.Forty-six healthy college students were divided into the accumulated group(n=23,female=47.8%)and control group(n=23,female=47.6%).The accumulated group engaged in mobile-based physical activity,following guidelines to accumulate a minimum of two times per day and three times a week.Sessions were divided into short bouts,ensuing each bout lasted at least 10 min.The control group did not engage in any specific physical activity.The data analysis involved comparing the scores of the intervention and control groups using several statistical techniques,such as independent sample t-test,paired sample t-tests,and 2(time)×2(group)repeated measures analysis of variance.The demographic characteristics at the pre-test showed no statistically significant differences between the groups.The accumulated group had significant decreases in depression(t_(40)=2.59,p=0.013,Cohen’s D=0.84)and perceived stress(t_(40)=2.06,p=0.046,Cohen’s D=0.56)from the pre-to post-test.The control group exhibited no statistically significant differences in any variables.Furthermore,there were significant effects of time on depression scores(F1,36=4.77,p=0.036,η_(p)^(2)=0.12)while significant interaction effects were also observed for depression(F_(1,36)=6.59,p=0.015,η_(p)^(2)=0.16).This study offers informative insights into the potential advantages of mobile-based physical activity programs with accumulated periods for enhancing mental health,specifically in relation to depression.This study illuminates the current ongoing discussions on efficient approaches to encourage mobile-based physical activity and improve mental well-being,addressing various lifestyles and busy schedules.

A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Normothermic regional perfusion mobile teams in controlled donation after circulatory death pathway: Evidence and peculiarities

To facilitate the implementation of controlled donation after circulatory death(cDCD)programs even in hospitals not equipped with a local Extracorporeal Membrane Oxygenation(ECMO)team(Spokes),some countries and Italian Regions have launched a local cDCD network with a ECMO mobile team who move from Hub hospitals to Spokes for normothermic regional perfusion(NRP)implantation in the setting of a cDCD pathway.While ECMO teams have been clearly defined by the Extracorporeal Life Support Organization,regarding composition,responsibilities and training programs,no clear,widely accepted indications are to date available for NRP teams.Although existing NRP mobile networks were developed due to the urgent need to increase the number of cDCDs,there is now the necessity for transplantation medicine to identify the peculiarities and responsibility of a NRP team for all those centers launching a cDCD pathway.Thus,in the present manuscript we summarized the character-istics of an ECMO mobile team,highlighting similarities and differences with the NRP mobile team.We also assessed existing evidence on NRP teams with the goal of identifying the characteristic and essential features of an NRP mobile team for a cDCD program,especially for those centers who are starting the program.Differences were identified between the mobile ECMO team and NRP mobile team.The common essential feature for both mobile teams is high skills and experience to reduce complications and,in the case of cDCD,to reduce the total warm ischemic time.Dedicated training programs should be developed for the launch of de novo NRP teams.

基于MobileNetV3Small-ECA的水稻病害轻量级识别研究

为实现水稻病害的轻量化识别与检测,使用ECA注意力机制改进MobileNetV3Small模型,并使用共享参数迁移学习对水稻病害进行智能化轻量级识别和检测。在PlantVillage数据集上进行预训练,将预训练得到的共享参数迁移到对水稻病害识别模型上微调优化。在开源水稻病害数据集上进行试验测试,试验结果表明,在非迁移学习下,识别准确率达到97.47%,在迁移学习下识别准确率达到99.92%,同时参数量减少26.69%。其次,通过Grad-CAM进行可视化,本文方法与其他注意力机制CBAM和SENET相比,ECA模块生成的结果与图像中病斑的位置和颜色更加一致,表明网络可以更好地聚焦水稻病害的特征,并且通过可视化和各水稻病害分析了误分类原因。本文方法实现了水稻病害识别模型的轻量化,使其能够在移动设备等资源受限的场景中部署,达到快速、高效、便携的目的。同时开发了基于Android的水稻病害识别系统,方便于在边缘端进行水稻病害识别分析。

基于GC-IMS和GC-MS技术结合化学计量法分析干燥方式对香椿挥发性成分的影响

为研究干燥方式对香椿挥发性风味物质的影响,利用气相色谱-离子迁移谱技术(gaschromatography-ion mobility spectrometry,GC-IMS)和气相色谱-质谱联用技术(gas chromatography-mass spectrometry,GC-MS)、相对香气活性值(relative odor activity value,ROAV)对真空冷冻干燥、热泵干燥、热风干燥、微波真空干燥的香椿样品进行挥发性成分分析,并进一步结合化学计量方法主成分分析(principal component analysis,PCA)和偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA),探究样品间挥发性成分的差异性。结果表明,通过GC-IMS检测到78个峰,鉴定出59种化合物。通过GC-MS鉴定出94种挥发性化合物,主要挥发性成分是硫化物、烯烃类和醛类。经过干燥后总挥发性化合物的含量显著降低。通过ROAV法确定了(E,Z)-二-1-丙烯基二硫化物、2-巯基-3,4-二甲基-2,3-二氢噻吩、正己醛、2-己烯醛、丁香酚等为香椿样品中关键香气化合物。PCA和PLS-DA结果表明,不同干燥方式的样品与新鲜样品有明显的分离,彼此之间也有明显的区别。鲜样和真空冷冻干燥样品为一组,其他3个干燥香椿样品为一组。根据变量投影重要性(variable importance in projection,VIP)得分共筛选出10种(VIP>1)标志挥发性化合物。干燥方式对香椿风味特性影响显著,真空冷冻干燥组与鲜样最为接近,考虑实际应用热泵干燥为香椿最适宜的脱水方式。本研究为热加工过程中风味品质控制提供一定的理论参考。

基于HS-GC-IMS和HS-SPME-GC-MS的蛋白酶对豆粕挥发性风味的影响分析

为研究不同蛋白酶酶解对豆粕挥发性风味成分的影响,选用4种蛋白酶(碱性蛋白酶、中性蛋白酶、木瓜蛋白酶、风味蛋白酶)对豆粕进行酶解,采用顶空-气相色谱-离子迁移谱(Headspace-gas chromatography-ion mobility spectroscopy,HS-GC-IMS)和顶空固相微萃取-气相色谱-质谱(Headspace solid phase microextraction-gas chromatography-mass spectrometry,HS-SPME-GC-MS)联用技术分析不同豆粕酶解物(Soybean meal hydrolysates,SMH)的挥发性风味成分,并结合主成分分析(Principal component analysis,PCA)、热图聚类和正交偏最小二乘判别法(Orthogonal partial least squares-discriminant analysis,OPLS-DA)对不同SMH进行分析。结果表明:碱性蛋白酶、中性蛋白酶、木瓜蛋白酶和风味蛋白酶酶解豆粕的挥发性风味成分存在较大差异。HS-GC-IMS鉴定出84种挥发性成分,筛选得到33种差异风味物质,发现酶解后酮类物质显著降低而醛类、醇类和酯类物质含量明显增加。PCA结果表明不同SMH之间的风味存在显著差异。最终通过OPLS-DA筛选出贡献较大的挥发性化合物,同时构建出可靠的用以鉴别SMH的模型。HS-SPME-GC-MS检测出103种差异风味物质,可用于区分不同SMH,被检出的挥发性组分中醛类、醇类和酮类等化合物为SMH风味的形成做出主要贡献,明晰了部分风味化合物形成的原因。PCA和聚类热图结果表明不同蛋白酶酶解对豆粕的挥发性风味物质的种类和含量有显著影响,其中,风味蛋白酶和木瓜蛋白酶对豆粕的风味改善最为显著。

不同厂家水基胶挥发性物质的GC-IMS差异分析

目的快速分析不同厂家水基胶中的挥发物特性。方法采用气相色谱-离子迁移谱(GC-IMS)联用技术对各种水基胶中的挥发性物质进行定性分析;结合生物指纹图谱、主成分分析(PCA)法以及欧式距离分析法对不同水基胶样品进行差异对比分析。结果水基胶中共识别出88种挥发性化学物质,涵盖酯类、酮类、醇类、醛类、杂环类、酸类、芳香类、醚类、萜类、酰胺类和腈类等11大类别,其中酯类物质占据显著比例,达26种,占总挥发性物质的38%~46%。结论采用GC-IMS技术结合生物指纹图谱、PCA以及欧式距离分析,能够有效区分不同厂家水基胶样品,同时同一厂家不同规格样品之间显示出较高的相似性;GC-IMS技术为不同厂家水基胶的快速溯源鉴别及挥发性物质差异分析提供了有力工具。

基于GC-IMS分析泡椒味万州烤鱼堂食过程的“三层次”风味变化

为分析泡椒味万州烤鱼堂食过程中风味的变化,采用气相色谱-离子迁移谱(GC-IMS)技术结合偏最小二乘判别分析(PLS-DA)探究其堂食过程中3个阶段的整体风味差异性及关键的风味标志物。结果显示:在3个阶段中共筛选出65种差异性风味成分,基于这些成分的PLS-DA模型能够有效区分各阶段的风味,呈现“三层次”风味分明的特点;变量投影重要性分析表明,乙酸异戊酯、乙酸芳樟酯、香茅醛(D)、2-乙基-5-甲基吡嗪、丁酸丁酯、己醇、2,6-二甲基吡啶、2-丁醇、1-辛烯-3-醇、2-庚醇、丁酸异丁酯、乙酸乙酯、2-甲基-2-戊烯醛、3-蒈烯、2,5-二甲基吡嗪共15种风味成分是区分泡椒味万州烤鱼堂食过程中“三层次”风味的潜在风味标志物。该研究深入挖掘万州烤鱼风味特征,为提升万州烤鱼品牌价值和制定万州烤鱼风味标准提供了有力的支持。

基于GC-IMS结合多元统计方法研究掺入不同比例玉米油对橄榄辣椒油风味的影响

为分析掺入不同比例玉米油对橄榄辣椒油风味的影响,文章采用感官评分、气相色谱-离子迁移谱(GC-IMS)和正交偏最小二乘-判别分析(OPLS-DA)等方法对辣椒油样品的风味成分进行检测和多元统计分析。结果表明,6种辣椒油的色泽和滋味无显著性差异,但香气差异显著(P<0.05),其中GLY1香气最佳,GLY2和GLY6香气和谐。通过GC-IMS共检测出81种挥发性有机化合物(VOCs),其中醇类、酯类、酸类、醛类、杂环类和酮类化合物在6种辣椒油中的相对含量较高。通过变量投影重要性(VIP)筛选出异丁醛、丙酮、乙酸-D、乙酸甲酯等29种VOCs为6种辣椒油的关键风味差异标志物(VIP>1.0)。OPLS-DA的因子载荷结果与热图聚类分析、感官评价结果一致。因此,GC-IMS结合OPLS-DA和热图聚类分析能将6种辣椒油进行有效区分。该研究结果可为橄榄辣椒油的鉴伪和风味多样化辣椒油产品的生产提供一定理论支撑。

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

As millimeter waves will be widely used in the Internet of Things(IoT)and Telematics to provide high bandwidth communication and mass connectivity,the coverage optimization of base stations can effectively improve the quality of communication services.How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers.This paper proposes the Muti-Fusion Sparrow Search Algorithm(MFSSA)optimize the situation to address the problem of discrete coverage maximization and rapid convergence.Firstly,the initial swarm diversity is enriched using a sine chaotic map,and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability.Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a“precocious”state.Such a swarm is very suitable for solving NP-hard problems.Secondly,an elite opposition-based learning strategy is added to expand the search range of the algorithm,and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum.This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm.Finally,the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem,and the convergence speed is better than other algorithms.MFSSA is improved by more than 10%compared to the original algorithm.

基于电子鼻结合GC-IMS分析不同烹饪方式猪肉的挥发性成分差异

烹饪方式可对食品风味产生较大影响,为深入探究烹饪方式对猪肉香气的影响,本研究采用电子鼻结合气相-离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)对不同烹饪方式猪肉挥发性化合物进行多元统计分析,并使用GC-IMS对不同烹饪方式下猪肉的100种挥发性化合物(包括单聚体和二聚体)进行表征。最终采用正交偏最小二乘判别分析建立出稳定的模型。结果显示,烹饪方式对猪肉风味化合物的形成至关重要。同蒸制和煮制两种烹饪方式相比,烤箱烤制和空气炸锅法烹饪的猪肉含有(E)-2-己烯酸乙酯、1-辛烯-3-醇、(Z)-4-癸烯醛、3-壬烯-2-酮、(E)-2-已烯-1-醇、丁酸丙酯、呋喃酮和双戊烯-M等特征挥发性化合物,且空气炸锅法猪肉中(E)-2-己烯酸乙酯、1-辛烯-3-醇、(Z)-4-癸烯醛、3-壬烯-2-酮的含量比烤箱烤制猪肉高。戊醛和乙酸乙酯标记为蒸制和煮制烹饪方式的特征挥发性物质化合物,电子鼻传感器的结果表明,RPCA模型可以有效地识别出各烹饪方式下猪肉的挥发性香气。研究结果揭示了不同的烹饪方式对猪肉挥发性成分的影响,空气炸锅制和烤箱烤制的香气更为相似,为猪肉的烹饪加工提供一定的参考。

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

基于GC-IMS分析不同煸炒温度对郫县豆瓣风味影响及游离氨基酸呈味分析

为探寻不同煸炒温度对郫县豆瓣风味变化和游离氨基酸呈味的影响,使用菜籽油在60,80,100,120,140℃煸炒豆瓣,采用气相色谱-离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)技术、气味活性值(odor activity value,OAV)、正交偏最小二乘法判别分析(orthogonal partial least squares-discrimination analysis,OPLS-DA)和主成分分析(principal component analysis,PCA)确定不同煸炒温度对豆瓣风味和关键化合物的影响,结合滋味活性值(taste activity value,TAV)分析对游离氨基酸呈味的影响。结果表明,煸炒温度在60,80℃时郫县豆瓣化合物开始分解转化,煸炒温度在100,120,140℃时郫县豆瓣化合物大量聚集、浓缩为关键风味化合物。GC-IMS共鉴定出40种挥发性化合物(单体或二聚体),醇类是郫县豆瓣的主要化合物(相对浓度为12.43%~36.89%);其中,2-乙基呋喃使煸炒后的豆瓣呈现焦香风味,1-戊烯-3-醇使豆瓣呈现果香,正戊醛、(E)-2-庚烯醛使豆瓣呈现刺激气味和油脂香气。TAV分析确定120℃为煸炒最佳温度,此时郫县豆瓣的滋味最佳,谷氨酸的鲜味强度最高(TAV为44.38);当煸炒温度达到140℃时,缬氨酸的鲜味强度最高(TAV为18.21),豆瓣开始呈现苦味。Pearson相关性分析确认郫县豆瓣的TAV与OAV存在明显的相关性。