Common Modeling Strategies Research Articles

Time-lapse 4D full-waveform inversion for ocean-bottom cable seismic data with seawater velocity changes

Time-lapse (4D) full-waveform inversion (FWI) is a valuable technology for high-resolution imaging of reservoir changes caused by hydrocarbon production and CO$_2$ storage. However, it still faces challenges in dealing with non-repeatability issues due to changes in seawater or near-surface velocity between baseline and monitor surveys. Despite recent advances, the ability of 4D FWI to address this problem has rarely been demonstrated. {\color{red}We} investigate the effectiveness of current 4D FWI strategies, including parallel, double-difference, sequential, and common-model strategies, in resolving non-repeatability issues for 4D OBC (ocean-bottom cable) seismic data. Additionally, a three-stage 4D FWI strategy is developed for 4D OBC seismic data, involving the estimation of seawater velocities in the baseline and monitor models, obtaining a good common starting model, and final convergence to obtain subsurface 4D changes. The synthetic data tests conducted with varying levels of seawater velocity changes indicate that among the current strategies tested, the common-model strategy performs the best, and that our new strategy can further enhance the 4D inversion results.

Effect of Environmental and Operating Conditions on Partial Discharge Activity in Electrical Machine Insulation: A Comprehensive Review

Electrical machines for transportation applications are subjected to harsh environmental conditions during their operations. Partial discharge (PD), which is one of the main reasons for insulation failure, is greatly affected by ambient conditions (i.e., temperature, pressure, and humidity). Countless efforts are made for a comprehensive understanding of the physics of PD under variable environmental factors. This paper aims to review recent works addressing temperature, pressure, and humidity impact on PD activity. The main content of the paper is organized into three sections dealing with each environmental factor. In every section, relevant publications are reviewed considering the type of samples tested, voltage waveform applied, mutual effects, and the most common PD modeling strategies used. The applicability of the PD measurements for PD risk assessment is also discussed. Based on the review, the current progress in understanding the environmental effects on the PD inception mechanism and PD characteristics is presented and discussed in detail, and future research trends in this field are outlined.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

The application and algorithm of fabric simulation in game industry

fabric simulation has emerged as a crucial aspect of computer graphics, particularly in applications such as film production, video games, and design. This paper provides a comprehensive review of fabric simulation methodologies, implementation techniques, and its diverse applications within the game industry. The fabric simulation algorithms are broken down into Fabric Modeling, Dynamic Simulation, and Collision Handling. The three common modeling strategies of Elasticity-Based Models, Particle-Based Models, and Mass-Spring Damper Models, are discussed in detail. Additionally, dynamic simulation strategies such as Position-Based Dynamics (PBD) and Extended Position-Based Dynamics (XPBD) are explored for their role in real-time simulations, particularly in the game industry. The paper also delves into collision handling algorithms, exploring the strategies for accurate collision detection and response. The application of fabric simulation in the game industry is highlighted, showcasing how it enhances realism and immersion by simulating fabric movement and interactions with the environment. The paper introduced various fabric simulation tools and systems within popular game engines like Unity 3D and Unreal Engine 5. Furthermore, optimization strategies, such as view-dependent adaptive simulation, are discussed to improve performance providing insight for future work.

Micro-Macro Mediation Analysis in Social Networks

Mediation analysis is increasingly used in the social sciences. Extension to social network data, however, has proved difficult because statistical network models are formulated at a lower level of analysis (the dyad) than many outcomes of interest. This study introduces a general approach for micro-macro mediation analysis in social networks. The author defines the average mediated micro effect (AMME) as the indirect effect of a network selection process on an individual, group, or organizational outcome through its effect on an intervening network variable. The author shows that the AMME can be nonparametrically identified using a wide range of common statistical network and regression modeling strategies under the assumption of conditional independence among multiple mediators. Nonparametric and parametric algorithms are introduced to generically estimate the AMME in a multitude of research designs. The author illustrates the utility of the method with an applied example using cross-sectional National Longitudinal Study of Adolescent to Adult Health data to examine the friendship selection mechanisms that indirectly shape adolescent school performance through their effect on network structure.

Stepsize sharing in time-lapse full-waveform inversion

Full-waveform inversion (FWI) methods can produce high-resolution images of the physical properties of the subsurface. FWI has become a powerful tool for time-lapse or 4D seismic inversion, with applications in the monitoring of reservoir changes with injection and production, and potentially long-term storage of carbon. Current time-lapse FWI strategies include the parallel strategy (PRS), the sequential strategy, the double-difference strategy (DDS), the common-model strategy (CMS), and the central-difference strategy (CDS). PRS time-lapse inversion is affected by convergence differences between the baseline and monitoring inversions, as well as nonrepeatable noise and nonrepeatable acquisition geometries between surveys. The other strategies are largely efforts to fix the sensitivities of PRS, but robust solutions are still sought. We hypothesize that several problems in time-lapse FWI arise from the independence of step lengths during updating. This is supported by synthetic data tests, which indicate that stepsize sharing reduces artifacts caused by the variability in PRS convergence. Two strategies, which we refer to as stepsize-sharing PRS (SSPRS) and stepsize-sharing CMS, are then designed to address these remaining issues. In this paper, we have tested our methods in five scenarios, including noise-free data, nonrepeated noises, nonrepeatable source positions, biased starting models, and a combination of the latter three. The comparisons between the SSPRS and other strategies indicate that the SSPRS can adapt to all tested scenarios well. Especially, except for the DDS which is extremely sensitive to the nonrepeatable source positions, only the SSPRS can provide meaningful results in the latter two scenarios when compared with others. Furthermore, given that SSPRS through its sharing incurs half of the time cost of seeking stepsizes compared with the PRS and DDS, the total computational cost of SSPRS is less than half of that of the CMS and CDS.

Mathematical modeling of the lower urinary tract: A review.

Understand what progress has been made toward a functionally predictive lower urinary tract (LUT) model, identify knowledge gaps, and develop from them a path forward. We surveyed prominent mathematical models of the basic LUT components (bladder, urethra, and their neural control) and categorized the common modeling strategies and theoretical assumptions associated with each component. Given that LUT function emerges from the interaction of these components, we emphasized attempts to model their connections, and highlighted unmodeled aspects of LUT function. There is currently no satisfactory model of the LUT in its entirety that can predict its function in response to disease, treatment, or other perturbations. In particular, there is a lack of physiologically based mathematical descriptions of the neural control of the LUT. Based on our survey of the work to date, a potential path to a predictive LUT model is a modular effort in which models are initially built of individual tissue-level components using methods that are extensible and interoperable, allowing them to be connected and tested in a common framework. A modular approach will allow the larger goal of a comprehensive LUT model to be in sight while keeping individual efforts manageable, ensure new models can straightforwardly build on prior research, respect potential interactions between components, and incentivize efforts to model absent components. Using a modular framework and developing models based on physiological principles, to create a functionally predictive model is a challenge that the field is ready to undertake.

The partial derivative framework for substantive regression effects.

egression models are ubiquitous in the psychological sciences. The standard practice in reporting and interpreting regression models are to present and interpret coefficient estimates and the associated standard errors, confidence intervals and p-values. However, coefficient estimates have limited inferential utility if the outcome is modeled nonlinearly with respect to the substantively interpreted predictors. This is problematic in common modeling strategies, such as nonlinear predictor designs and/or generalized linear models. In the former, coefficients may correspond to product, power, log, and/or exponentially transformed units. In the latter, the relationship between the predictors and outcome are modeled via a function of the outcome, rather than the outcome in its original units. In both cases, the interpretation of the coefficients alone do not provide straightforward summaries of the data, and in fact may be misleading. We address these issues by developing a framework of regression effects by integrating two critical features. First, we explicitly model substantive variables in the units that provide the desired interpretation. Second, we use partial derivatives to summarize the relations between the substantive predictors and outcome variables to account for nonlinearities arising from modeling strategies. We show how to derive estimates and standard errors for quantities of interest in the interpretive units, as well as techniques to present the relationships between variables in meaningful ways. Finally, we provide demonstrations in both simulated and real data over a wide variety of models and estimation procedures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Techno-economic impacts of battery performance models and control strategies on optimal design of a grid-connected PV system

A battery storage has emerged as the most widely-used storage option, due to its flexible and complementary functionality for renewable energy systems such as solar PV and wind power. In order to ensure the efficient operation of batteries in energy systems, a proper battery model is essential in predicting realistic battery performance under various operating conditions. Accurate knowledge of the state of charge, state of power, and battery efficiency is a necessity for the development of advanced grid management applications. This paper investigates the techno-economic impacts of two battery modelling scenarios on the sizing and optimization of a grid-connected PV-battery system. Scenario 1 is based on a common simple battery model and control strategy which represents the battery status without reflecting dynamic behavior. By contrast, Scenario 2 is based on a complex battery model involving estimation of battery current-voltage characteristics under various operating conditions. A rule-based operational strategy linked to a non-dominated sorting genetic algorithm is further employed for the simulation and multi-objective optimization of a grid-connected hybrid PV-battery system. The battery life cycle cost and the self-sufficiency ratio are analyzed and optimized as objective functions, and battery capacity constitutes as a decision variable. The results show that in order to reach the same self-sufficiency ratio, the optimization of a hybrid energy system based on Scenario 1 leads to solutions with a higher life cycle cost and requiring bigger battery capacity, compared to that of Scenario 2. Moreover, under the same design parameters, the system optimization based on Scenario 2 delivers more power to the end-user, which leads to a higher self-sufficiency ratio compared to when the system is simulated based on Scenario 1. This study proves that an efficient battery model with sufficient accuracy is techno-economically more beneficial, and leads to more accurate battery sizing.

From Discrete to Continuous Modeling of Lymphocyte Development and Plasticity in Chronic Diseases.

The molecular events leading to differentiation, development, and plasticity of lymphoid cells have been subject of intense research due to their key roles in multiple pathologies, such as lymphoproliferative disorders, tumor growth maintenance and chronic diseases. The emergent roles of lymphoid cells and the use of high-throughput technologies have led to an extensive accumulation of experimental data allowing the reconstruction of gene regulatory networks (GRN) by integrating biochemical signals provided by the microenvironment with transcriptional modules of lineage-specific genes. Computational modeling of GRN has been useful for the identification of molecular switches involved in lymphoid specification, prediction of microenvironment-dependent cell plasticity, and analyses of signaling events occurring downstream the activation of antigen recognition receptors. Among most common modeling strategies to analyze the dynamical behavior of GRN, discrete dynamic models are widely used for their capacity to capture molecular interactions when a limited knowledge of kinetic parameters is present. However, they are less powerful when modeling complex systems sensitive to biochemical gradients. To compensate it, discrete models may be transformed into regulatory networks that includes state variables and parameters varying within a continuous range. This approach is based on a system of differential equations dynamics with regulatory interactions described by fuzzy logic propositions. Here, we discuss the applicability of this method on modeling of development and plasticity processes of adaptive lymphocytes, and its potential implications in the study of pathological landscapes associated to chronic diseases.

Modeling Training Loads and Injuries: The Dangers of Discretization.

To evaluate common modeling strategies in training load and injury risk research when modeling continuous variables and interpreting continuous risk estimates; and present improved modeling strategies. Workload data were pooled from Australian football (n = 2550) and soccer (n = 23,742) populations to create a representative sample of acute:chronic workload ratio observations for team sports. Injuries were simulated in the data using three predefined risk profiles (U-shaped, flat and S-shaped). One-hundred data sets were simulated with sample sizes of 1000 and 5000 observations. Discrete modeling methods were compared with continuous methods (spline regression and fractional polynomials) for their ability to fit the defined risk profiles. Models were evaluated using measures of discrimination (area under receiver operator characteristic [ROC] curve) and calibration (Brier score, logarithmic scoring). Discrete models were inferior to continuous methods for fitting the true injury risk profiles in the data. Discrete methods had higher false discovery rates (16%-21%) than continuous methods (3%-7%). Evaluating models using the area under the ROC curve incorrectly identified discrete models as superior in over 30% of simulations. Brier and logarithmic scoring was more suited to assessing model performance with less than 6% discrete model selection rate. Many studies on the relationship between training loads and injury that have used regression modeling have significant limitations due to improper discretization of continuous variables and risk estimates. Continuous methods are more suited to modeling the relationship between training load and injury. Comparing injury risk models using ROC curves can lead to inferior model selection. Measures of calibration are more informative judging the utility of injury risk models.

Modeling pyramidal sensors in ray-tracing software by a suitable user-defined surface

Following the unprecedented results in terms of performances delivered by the first light adaptive optics system at the Large Binocular Telescope, there has been a wide-spread and increasing interest on the pyramid wavefront sensor (PWFS), which is the key component, together with the adaptive secondary mirror, of the adaptive optics (AO) module. Currently, there is no straightforward way to model a PWFS in standard sequential ray-tracing software. Common modeling strategies tend to be user-specific and, in general, are unsatisfactory for general applications. To address this problem, we have developed an approach to PWFS modeling based on user-defined surface (UDS), whose properties reside in a specific code written in C language, for the ray-tracing software ZEMAX™. With our approach, the pyramid optical component is implemented as a standard surface in ZEMAX™, exploiting its dynamic link library (DLL) conversion then greatly simplifying ray tracing and analysis. We have utilized the pyramid UDS DLL surface—referred to as pyramidal acronyms may be too risky (PAM2R)—in order to design the current PWFS-based AO system for the Giant Magellan Telescope, evaluating tolerances, with particular attention to the angular sensitivities, by means of sequential ray-tracing tools only, thus verifying PAM2R reliability and robustness. This work indicates that PAM2R makes the design of PWFS as simple as that of other optical standard components. This is particularly suitable with the advent of the extremely large telescopes era for which complexity is definitely one of the main challenges.

Abstract P288: Multiple Versus Single Lesions in Lower-Extremity Peripheral Arterial Disease: 2.5-Year Risk of Recurrent Cardiovascular Events

Background: Lower-extremity peripheral arterial disease (PAD) is associated with an increased risk of mortality and high resource utilization. Therefore, there is a real need to assess PAD patients' risk for an initial cardiovascular event as well as their risk for multiple, serial cardiovascular events over time. Although it is expected, but unexplored, that those with a greater burden of disease (e.g. multiple vs. single lesions) would have an increased risk for events, common modeling strategies only emphasize time to first event. We tested a novel recurrent event model to examine whether more advanced disease is associated with a persistent, increased risk for subsequent cardiovascular events. Methods: A prospective cohort of 717 patients with newly diagnosed PAD - enrolled from 2 vascular clinics in The Netherlands - underwent duplex ultrasound testing at enrollment to determine the presence of multiple vs. single lesions. A traditional time-to-first event Cox regression model examined the association between multiple vs. single (reference) lesions and 2.5-year cardiovascular events, adjusting for site, demographics and risk factors (see Table). We then replicated these analyses using an advanced Cox-based model for recurrent events. Results: A total of 324 out of 717 (44.5%) patients had multiple lesions. After 2.5 year follow-up, patients with multiple lesions not only had an increased time-to-first cardiovascular event risk (35.2% vs. 32.8%; adjusted HR=1.32; 95%CI 1.07-1.62), but also had an increased risk of recurrent cardiovascular events (60.2% vs.47.7%; adjusted HR=1.24; 95%CI 1.00-1.53) (Table). Conclusions: Using a recurrent event model, we were able to demonstrate that patients with PAD who present with multiple lesions are not only at increased risk of having a first cardiovascular event, but also remain at increased risk for subsequent events over 2.5 years of observation; even after their first event and subsequent management of their PAD. Table-The Association Between Having Multiple vs. Single Lesions and 2.5-Year Cardiovascular Events * Model Unadjusted Adjusted HR (95% CI) P-value HR (95% CI) P-value (1) Time-to-First Event Model Cox Proportional Hazards Model 1.36 (1.12-1.67) 0.003 1.32 (1.07-1.62) 0.008 (2) Cox-Based Model for Recurrent Events Conditional-Gap Time † 1.36 (1.17-1.66) 0.002 1.24 (1.00-1.53) 0.047 The unadjusted and adjusted effects (Hazard Ratio [HR], 95% Confidence Interval [CI]) for (1) a traditional time-to-first event Cox Proportional Hazards model and (2) an advanced Cox-based model for recurrent events are depicted. Covariates in the adjusted model included: site, age, sex, history of cerebrovascular disease, history of cardiac disease, chronic obstructive pulmonary disease, hypertension, diabetes mellitus, kidney disease, current smoking, and hyperlipidemia. * Cardiovascular events constituted of admissions for peripheral, coronary, and carotid revascularizations, bleeding events, abnormal cardiac rhythm, heart failure, non-fatal stroke/transient ischemic attack, non-fatal myocardial infarction, angina, other ischemic events, and fatal myocardial infarction/stroke, and other cardiovascular death; † Risk interval is the time elapsed since each prior event.

A Comparison of Generalized Additive Models to Other Common Modeling Strategies for Continuous Covariates: Implications for Risk Adjustment

Common modeling strategies for quantitative covariates include single linear terms, dummy variables on categories, Fractional Polynomials (FP) and cubic smoothing splines in Generalized Additive Models (GAM). The goal of this study was to evaluate the impact of using GAM over other common covariate modeling strategies on risk adjustment. Analyses were based on inter-hospital mortality comparisons in a Canadian provincial trauma system (n=123,732; 59 hospitals). Parameter estimates describing the increase in log odds of mortality for one hospital compared to the reference were adjusted with five quantitative covariates modeled using 1) single linear terms, 2) dummy variables on 2, 3, 4, 5 categories, 3) FP, and 4) GAM. The parameter estimates generated by the first three modeling strategies were compared to that generated by the GAM using mean standardized difference. Mean standardized difference (95% CI) was 71.69 (51.7-91.7) for single linear terms, 21.1 (14.3-28.9); 23.4 (15.6-31.2); 49.6 (28.1-71.1); and 48.5 (28.8-68.2) for dummy variables on 2, 3, 4, and 5 categories, respectively and 12.7 (10.0-15.4) for FP. Results suggest that GAM, FP and at least 4 risk-homogeneous categories provide equivalent risk adjustment to smoothing splines in GAM while single linear terms and less than 4 categories may induce residual confounding.

Study on seafood volatile profile characteristics during storage and its potential use for freshness evaluation by headspace solid phase microextraction coupled with gas chromatography–mass spectrometry

Seafood volatile profile characteristics at different storage phases are various and can be used for freshness evaluation during storage. It is imperative to obtain the full volatile information prior to the further study of seafood volatile profile characteristics during storage. Also, the efficient data-processing method is another important factor for the interpretation of seafood volatile profile characteristics during storage and related potential volatile markers. In this work, a new analytical strategy, including the efficient sampling technique, sensitive detection and suitable data-processing method, for seafood freshness evaluation was developed based on the volatile profile characteristics during storage. First, the study of volatiles of seafood samples including razor clam, redspot swimming crab and prawn at different storage phases were conducted by headspace solid phase microextraction (HSSPME) followed by gas chromatography–mass spectrometry (GC–MS) detection. Then, seafood volatile profile characteristics at different storage phases were statistically interpreted by a combination data-processing method including normalization, principle component analysis (PCA) and common model strategy. The different seafood volatile profile characteristics and potential volatile markers were attempted to be distilled. The results tentatively suggested that the different seafood volatile profile characteristics during storage could reflect the transitional changing seafood freshness and provide more precise warning information for seafood spoilage during storage than any single chemical markers. This work developed an analytical method for study of seafood volatile profile characteristics and tentatively proposed a new idea of using seafood volatile profile characteristics during storage for the freshness evaluation from the point of view of analytical chemistry.

Study of the Alarming Volatile Characteristics of Tessaratoma papillosa Using SPME--GC--MS

The stinkbug's volatile compositions would alter very much before and after stinkbugs were disturbed or irritated, which caused the alarming effect. An efficient headspace solid-phase microextraction sampling method was established to study the alarming volatile characteristics and potential alarming volatiles of stinkbugs (Tessaratoma papillosa) followed by gas chromatography-mass spectrometry detection. The number of volatiles identified was 16 and 22 before and after stinkbug irritation, respectively. Long-chain alkanes, alkenes, and alcohols consisted of the main volatile compositions of Tessaratoma papillosa. When stinkbugs were disturbed, the typical unsaturated volatiles were released, especially including a series of tridecane derivatives. In comparison with the volatile compounds of lichi leaf and flower (plants the stinkbug eats), it could be seen that most stinkbug alarming volatiles were synthesized by the insects themselves, and that they do not originate from their food. The different statistical alarming volatile characteristics of Tessaratoma papillosa before and after irritation were interpreted by principal component analysis in the original Chromatography Data Processing System. However, temperature and light did not affect the alarming volatile characteristics. The variety of the stinkbug alarming volatile characteristics before and after irritation was specified by common model strategy. Tridecane, [E]-2-hexenal, dodecane, [E]-2-hexen-1-ol acetate, and 2,3-dimethyl-1-pentene contributed most to the various alarming volatile characteristics before and after irritation, which might be the potential alarming volatiles. It is hoped that this work will provide useful information for insect control.

Study of the volatile profile characteristics of longan during storage by a combination sampling method coupled with GC/MS

AbstractBACKGROUND: Longan (Dimocarpus longan Lour.) is a kind of subtropical fruit, but it has a very short shelf life under normal ambient conditions. In this work, a combination sampling method, including headspace solid phase micro‐extraction (HSSPME), simultaneous distillation extraction (SDE) and steam distillation (SD), was used to study the volatile profile characteristics of longan at the fresh and deteriorated storage phases followed by gas chromatography–mass spectrometry (GC‐MS) detection. The study would provide a method for investigating the volatile profile characteristics of longan during storage and some helpful clues for quality discrimination.RESULTS: In total, 28 and 22 volatile compounds of fresh and deteriorated longan were identified. Alkenes and esters were the main volatile components of longan. β‐ocimene accumulated, whereas allo‐ocimene and 3,4‐dimethyl‐2,4,6‐octatriene decreased during storage. Different volatile profile characteristics at the fresh and deteriorated storage phases obtained by HSSPME were specified by principal component analysis (PCA). Typical volatile compounds were distilled by a common model strategy.CONCLUSION: The volatile profiles of longan during storage were studied using a combination of sampling methods followed by GC‐MS detection. Five typical volatiles contributing significantly to the difference in volatile profile characteristics of longan at the fresh and deteriorated storage phases were distilled using a common model strategy. These compounds are potential bio‐markers for longan degradation. Tentative results suggest that combining HSSPME with conventional volatile isolation methods would provided more representative data on changes in the volatile compounds of longan during storage and some helpful clues for quality discrimination. Copyright © 2008 Society of Chemical Industry

Study of the volatile composition of tomato during storage by a combination sampling method coupled with gas chromatography/mass spectrometry

AbstractBACKGROUND: The volatile compositions of common tomato (Lycopersicon esculentum var. commune) and cherry tomato (Lycopersicon esculentum var. cerasiforme) during storage were studied by a combination sampling method, including headspace solid phase microextraction (HSSPME), simultaneous distillation extraction (SDE) and steam distillation (SD), coupled with gas chromatography/mass spectrometry (GC/MS) detection.RESULTS: Twenty‐one and 14 volatile compounds of fresh and stale common tomatoes and 27 and 17 volatile compounds of fresh and stale cherry tomatoes were identified with various degrees of certainty. The main identified volatile compounds of common and cherry tomatoes were C6 alcohols and aldehydes. During storage, saturated hexanal increased gradually whereas unsaturated (E)‐2‐hexenal decreased. Different volatile compositional characteristics at the fresh and stale storage phases obtained by HSSPME were specified by principal component analysis (PCA). Different metabolic pathways and enzyme catalyses resulted in the various volatile compositional characteristics during tomato storage. Five typical volatile compounds contributing greatly to the difference in volatile compositional characteristics of common and cherry tomatoes at the fresh and stale storage phases were distilled by a common model strategy. These compounds are potential biomarkers for tomato degradation, but further study is needed.CONCLUSION: The preliminary results suggest that the combination of HSSPME and conventional sampling methods would yield more representative information on changes in tomato volatile composition during storage. Copyright © 2007 Society of Chemical Industry

A preliminary study of plant aroma profile characteristics by a combination sampling method coupled with GC–MS

Plant aroma profile characteristic is an important bio-information. In this study, a sampling method in combination with headspace solid phase microextraction (HSSPME), simultaneous distillation extraction (SDE) and steam distillation (SD), followed by gas chromatography–mass spectrometry (GC–MS) detection, was used to study the aroma profile characteristics of the fresh and stale samples of common tomato, cherry tomato, durian and longan, and of the different samples of Chinese mango and Allium varieties. The typical aroma volatiles of these samples were isolated and identified according to the different degrees of certainty. The different samples showed different aroma profile characteristics when principal component analysis (PCA) was conducted to analyze the data of the aroma profile chromatograms. Then the crucial aroma volatiles contributing greatly to the clustering differences of the aroma profile characteristics of the fresh and stale common tomato, cherry tomato, durian and longan were identified by common model strategy. These compounds are potential bio-markers of plant metabolism, but further study is needed. Continuous investigations for the aroma profile characteristics of common tomato during storage and chive during growth were conducted to distill the potential bio-information from the plant metabolism processes. The saturated hexanal of common tomato increased during storage, whereas the unsaturated hexenal reduced. The accumulating trends of volatile sulfides were observed during chive growth. The preliminary results related with the corresponding bio-information could provide helpful clues to the study of plant's secondary metabolism process and benefit quality control.

The study of the aroma profile characteristics of durian pulp during storage by the combination sampling method coupled with GC–MS

AbstractIn this study, a combination sampling method, including headspace solid‐phase micro‐extraction (HSSPME), simultaneous distillation extraction (SDE) and steam distillation (SD), were used to study the aroma profile characteristics of durian (Durio zibethinus Murr.) pulp during storage, followed by gas chromatography–mass spectrometric (GC–MS) detection; 26 and 22 aroma volatiles of fresh and deteriorated durian pulps were identified according to different degrees of certainty. Volatile esters were identified as the main aromatic components of durian pulp. Most ethyl esters reduced in concentration during storage, whereas the methyl, propyl and butyl esters increased. Different aroma profile characteristics at the fresh and deteriorated storage phases obtained by HSSPME were specified by principal component analysis (PCA). Five typical aroma volatiles contributing greatly to the difference of aroma profile characteristics of durian pulp at the fresh and deteriorated storage phases were distilled by common model strategy. These compounds are potential bio‐markers for durian degradation, but further study is needed. Tentative results suggest that combining HSSPME with conventional volatile isolation methods would yield more representative data on changes in the aroma of durian pulp during storage. Copyright © 2006 John Wiley & Sons, Ltd.