Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

影响荔枝酒发酵过程中挥发酸的因素

挥发酸的控制是荔枝酒的生产过程中的重要技术瓶颈,发酵过程易造成荔枝果酒挥     

Effect of cooking temperature on texture and flavour binding of braised sauce porcine skin

To investigate the effect of cooking temperature (55, 65, 75, 85 and 95 °C) on texture and flavour binding of braised sauce porcine skin (BSPS), sensory acceptance, microstructure and flavour-binding capacity were investigated during the processing of BSPS. Samples cooked at 85 and 95 °C showed better texture and aroma scores. Hardness and chewiness of BSPS were obviously improved at 85 and 95 °C than control group. Collagen structure was significantly destroyed over 85 °C. The porcine skin collagen heated at 85 and 95 °C showed relatively higher flavour-binding capacity than other samples. The improvement of texture of BSPS was mainly attributed to the degradation of collagen. Higher aroma scores of BSPS were related to intense binding abilities with aroma compounds at 85 and 95 °C. Cooking at 85 or 95 °C could be an optimal cooking temperature for BSPS.     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Real-time molecular characterization of air pollutants in a Hong Kong residence: Implication of indoor source emissions and heterogeneous chemistry

Due to the high health risks associated with indoor air pollutants and long-term exposure, indoor air quality has received increasing attention. In this study, we put emphasis on the molecular composition, source emissions, and chemical aging of air pollutants in a residence with designed activities mimicking ordinary Hong Kong homes. More than 150 air pollutants were detected at molecular level, 87 of which were quantified at a time resolution of not less than 1 hour. The indoor-to-outdoor ratios were higher than 1 for most of the primary air pollutants, due to emissions of indoor activities and indoor backgrounds (especially for aldehydes). In contrast, many secondary air pollutants exhibited higher concentrations in outdoor air. Painting ranked first in aldehyde emissions, which also caused great enhancement of aromatics. Incense burning had the highest emissions of particle-phase organics, with vanillic acid and syringic acid as markers. The other noteworthy fingerprints enabled by online measurements included linoleic acid, cholesterol, and oleic acid for cooking, 2,5-dimethylfuran, stigmasterol, iso-/anteiso-alkanes, and fructose isomers for smoking, C₂₈-C₃₄ even n-alkanes for candle burning, and monoterpenes for the use of air freshener, cleaning agents, and camphor oil. We showed clear evidence of chemical aging of cooking emissions, giving a hint of indoor heterogeneous chemistry. This study highlights the value of organic molecules measured at high time resolutions in enhancing our knowledge on indoor air quality.     

Lactobacillus Gasseri LGZ 1029 in yogurt: rheological behaviour and volatile compound composition

Yogurt preserves and enhances nutritional value of milk. In this study, we have compared several strains to determine the physicochemical, sensory, rheological and aroma characteristics of different yogurts. We used Lactobacillus gasseri LGZ 1029 (LG), commercial probiotic L. rhamnosus (LGG) and traditional fermentation strains Streptococcus thermophilus and L. bulgaricus (SL). Results showed that the flavour and texture characteristics of mixed-strain yogurts were obviously better than in single-strain yogurts. Addition of LG increased pseudoplastic behaviour, as shown by Herschel–Bulkley model analysis of rheological behaviour. The LG + SL group also had both the highest viscosity consistency index and thickening ability. In addition, a total of 57 volatile compounds were detected in yogurts and the fermentation with the addition of LG was mainly affected by ketones. Our study suggested that a yogurt with new attributes can be produced by using LGZ 1029.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.