Production of Novel Bigels from Cold Pressed Chia Seed Oil By-Product: Application in Low-Fat Mayonnaise.

The objective of this study was to produce an innovative bigel formulation by combining glycerol monostearate (GMS) oleogel with hydrogels stabilized by various agents, including cold pressed chia seed oil by-product gum (CSG), gelatin (G), and whey protein concentrate (WPC). The findings indicated that the choice of hydrogel influenced the rheological, textural, and microstructural properties of the bigels. The G' value of the bigel samples was higher than G″, indicating that all the bigels exhibited solid-like characteristics. In order to numerically compare the dynamic rheological properties of the samples, K' and K″ values were calculated using the power law model. K' values of the samples were found to be higher than K″ values. The K' value of bigel samples was significantly affected by the hydrogel (HG)/oleogel ratio (OG) and the type of stabilizing agent used in the hydrogel formulation. As the OG ratio of bigel samples increased, the K' value increased significantly (p < 0.05). The texture values of the samples were significantly affected by the HG/OG ratio (p < 0.05). The study's findings demonstrated that utilizing CSG, G, and WPC at an OG ratio more than 50% can result in bigels with the appropriate hardness and solid character. The low-fat mayonnaise was produced by using these bigels. The low-fat mayonnaise showed shear-thinning and solid-like behavior with G' values greater than the G″ values. Low-fat mayonnaise produced with CSG bigels (CSGBs) showed similar rheological properties to the full-fat mayonnaise. The results showed that CSG could be used in a bigel formulation as a plant-based gum and CSGB could be used as a fat replacer in low-fat mayonnaise formulation.

Optimization of asymmetric bioreduction conditions of 1-indanone by Leuconostoc mesenteroides N6 using a face-centered design-based multi-objective optimization model.

There has been an increasing interest in biocatalysts over the past few decades in order to obtain high efficiency, high yield, and environmentally benign procedures aiming at the manufacture of pharmacologically relevant chemicals. Lactic Acid Bacteria (LAB), a microbial group, can be employed as biocatalysts while performing asymmetric reduction of prochiral ketones. In this study, Leuconostoc mesenteroides N6 was used for the asymmetric bioreduction 1-indanone. And then, a novel and innovative face-centered design-based multi-objective optimization model was used to optimize experimental conditions. Also, the experimental design factors were defined as agitation speed, incubation period, pH, and temperature for optimization to acquire the maximum enantiomeric excess (ee) and conversion rate (cr) values. When using the face-centered design-based multi-objective optimization model, the optimum culture conditions corresponded to 96.34 and 99.42%, ee and cr responses, respectively, were pH = 5.87, incubation temperature = 35 °C, incubation period = 50.88 h, and agitation speed = 152.60 rpm. Notably, the validation experiment under the optimum model conditions confirmed the model results. This study demonstrated the importance of the optimization and the efficiency of the face-centered design-based multi-objective model.

Optimization of asymmetric bioreduction conditions of 1-(thiophen-2-yl)ethanone by Weissella cibaria N9 using a desirability function-embedded face-centered optimization model.

Prochiral ketones can be effectively bio-reduced to chiral secondary alcohols by whole-cell biocatalysts, which are possible useful precursors to synthesize physiologically active chemicals and natural products. When whole-cell biocatalysts strains are used, bioreduction process can be influenced by various cultural factors, and it is vital to optimize these factors that affect selectivity, conversion rate, and yield. In this study, Weissella cibaria N9 was used as whole-cell biocatalyst for bioreduction of 1-(thiophen-2-yl)ethanone, and cultural design factors were optimized using a desirability function-embedded face-centered optimization model. For this, effects of pH (4.5-5.5-6.5, x1), (2) temperature (25-30-35 °C, x2), (3) incubation period (24-48-72 h, x3), and (4) agitation speed (100-150-200 rpm, x4) on two response variables; (1) ee (%) and (2) cr (%) were tested. Next, desirability function-embedded face-centered optimization model revealed that a pH of 6.43, a temperature of 26.04 °C, an incubation period of 52.41 h, and an agitation speed of 150 rpm were the optimum levels and the estimated ee and cr responses were 99.31% and 98.16%, respectively. Importantly, the actual experimental ee and cr responses were similar to the estimated values indicating the capability of the offered desirability function-embedded face-centered optimization model when using the optimum cultural conditions.

Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods.

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient's age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches.

The G20 countries are the locomotives of economic growth, representing 64% of the global population and including 4.7 billion inhabitants. As a monetary and market value index, real gross domestic product (GDP) is affected by several factors and reflects the economic development of countries. This study aimed to reveal the hidden economic patterns of G20 countries, study the complexity of related economic factors, and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019 (COVID-19) pandemic recession (2019-2020). In this respect, this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita, industrial production, government spending, COVID-19 cases/population, patient recovery, COVID-19 death cases, number of hospital beds/1000 people, and percentage of the vaccinated population to identify clusters for G20 countries. The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development. The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries, splitting them into three clusters by sharing different measurements and patterns (harmonies and variances across G20 countries). A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors. Similarly, the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession. Variables such as GDP per capita and patient recovery of COVID-19 cases with values of $12,012 and 82.8%, respectively, were the most significant factors for clustering the G20 countries, with a correlation coefficient (R2) of 91.8%. The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced.

An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms.

Design and implementation of biological neural networks is a vital research field in the neuromorphic engineering. This paper presents LUT-based modeling of the Adaptive Exponential integrate-and-fire (ADEX) model using Nyquist frequency method. In this approach, a continuous term is converted to a discrete term by sampling factor. This new modeling is called N-LUT-ADEX (Nyquist-Look Up Table-ADEX) and is based on accurate sampling of the original ADEX model. Since in this modeling, the high-accuracy matching is achieved, it can exactly reproduce the spiking patterns, which have the same behaviors of the original neuron model. To confirm the N-LUT-ADEX neuron, the proposed model is realized on Virtex-II Field-Programmable Gate Array (FPGA) board for validating the final hardware. Hardware implementation results show the high degree of similarity between the proposed and original models. Furthermore, low-cost and high-speed attributes of our proposed neuron model will be validated. Indeed, the proposed model is capable of reproducing the spiking patterns in terms of low overhead costs and higher frequencies in comparison with the original one. The properties of the proposed model cause can make it a suitable choice for neuromorphic network implementations with reduced-cost attributes.

Bioactive and technological properties of an α-D-glucan synthesized by Weissella cibaria PDER21.

A slimy-mucinous-type colony of EPS-producing Weissella cibaria PDER21 was isolated and identified. The monomer composition was glucose, showing that the EPS is a glucan type homopolysaccharide, The core structure of (1 â†’ 6)-linked α-d-glucose units including (1 â†’ 3)-linked α-d-glucose branches at a ratio of 93.4/6.6 was revealed by 1H and 13C NMR spectra and confirmed by FTIR analysis. The glucan showed a superior thermal stability with almost no degradation in structure up to 300 °C. XRD analysis revealed the amorphous structure while SEM analysis confirmed the layer-like morphology. The glucan had an antioxidant activity (89.5%), water-holding capacity (103.7%) and water solubility index (80.7%) values, suggesting that the glucan had a strong level of antioxidant properties; good water binding capacity and excellent solubility. The glucan PDER21 is a polysaccharide possessing a good combination of technical and functional attributes, suggesting a great deal of potential for use in the food industry.

Structural and bioactive characteristics of a dextran produced by Lactobacillus kunkeei AK1.

In this study, structural and techno-functional characteristics of an exopolysaccharide (EPS) produced by Lactobacillus kunkeei AK1 were determined. High-performance liquid chromatography (HPLC) analysis demonstrated that EPS AK1 was composed of only glucose units. 1H and 13C Nuclear magnetic resonance (NMR) analysis revealed that EPS AK1 was a dextran type EPS containing 4.78% (1 â†’ 4)-linked α-d-glucose branches. The molecular weight of EPS AK1 was determined to be 45 kDa by Gel Permeation Chromatography (GPC) analysis. A high level of thermal stability up to 280 °C was determined for dextran AK1 detected by Differential scanning calorimetry (DSC) and Thermogravimetric analysis (TGA). Dextran AK1 appeared as regular spheres with compact morphology and as irregular particles in the solution with no clear cross-linking between the chains of the polysaccharide observed by Scanning electron microscopy (SEM) and Atomic force microscopy (AFM) analysis, respectively. X-ray diffraction analysis (XRD) analysis demonstrated that dextran AK1 had a crystalline structure. A relatively strong antioxidant activity was observed for dextran AK1 determined by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical scavenging and cupric reducing antioxidant capacity (CUPRAC) tests. Finally, only a digestion ratio of 3.1% was observed for dextran AK1 following the in vitro simulated gastric digestion test.

Correction: Wang et al. An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms. Biology 2022, 11, 1125.

There was an error in the original publication [...].

Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas-Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries.

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

Rapid Screening of Mentha spicata Essential Oil and L-Menthol in Mentha piperita Essential Oil by ATR-FTIR Spectroscopy Coupled with Multivariate Analyses.

Mentha piperita essential oil (EO) has high economic importance because of its wide usage area and health-beneficial properties. Besides health-beneficial properties, Mentha piperita EO has great importance in the flavor and food industries because of its unique sensory and quality properties. High-valued essential oils are prone to being adulterated with economic motivations. This kind of adulteration deteriorates the quality of authentic essential oil, injures the consumers, and causes negative effects on the whole supply chain from producer to the consumer. The current research used fast, economic, robust, reliable, and effective ATR-FTIR spectroscopy coupled chemometrics of hierarchical cluster analysis(HCA), principal component analysis (PCA), partial least squares regression (PLSR) and principal component regression (PCR) for monitoring of Mentha spicata EO and L-menthol adulteration in Mentha piperita EOs. Adulterant contents (Mentha spicata and L-menthol) were successfully calculated using PLSR and PCR models. Standard error of the cross-validation SECV values changed between 0.06 and 2.14. Additionally, bias and press values showed alteration between 0.06 and1.43 and 0.03 and 41.15, respectively. Authentic Mentha piperita was successfully distinguished from adulterated samples, Mentha spicata and L-menthol, by HCA and PCA analysis. The results showed that attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometrics could be effectively used for monitoring various adulterants in essential oils.

Rapid detection of green-pea adulteration in pistachio nuts using Raman spectroscopy and chemometrics.

BACKGROUND: Ground pistachio nut is prone to adulteration because of its high economic value and wide usage. Green pea is known as the main adulterant in frauds involving pistachio nuts. The present study developed a new, rapid, reliable and low-cost methodology by using a portable Raman spectrometer in combination with chemometrics for the detection of green pea in pistachio nuts. RESULTS: Three different methods of Raman spectroscopy-based chemometrics analysis were developed for the determination of green-pea adulteration in pistachio nuts. The first method involved the development of hierarchical cluster analysis (HCA) and principal component analysis (PCA), which differentiated authentic pistachio nuts from green pea and green pea-adulterated samples. The best classification pattern was observed in the adulteration range of 20-80% (w/w). In addition to classification methods, partial least squares regression (PLSR) and genetic algorithm-based inverse least squares (GILS) were also used to develop multivariate calibration models to determine quantitatively the degree of green-pea adulteration in grounded pistachio nuts. The spectral range of 1790-283 cm-1 was used in the case of multivariate data analysis. A green-pea adulteration level of 5-80% (w/w) was successfully identified by PLSR and GILS. The correlation coefficient of determination (R2 ) was determined as 0.91 and 0.94 for the PLSR and GILS analyses, respectively. CONCLUSION: A Raman spectrometer combined with chemometrics has a high capability with regard to the detection of adulteration in pistachio nuts, combined with low cost, strong reliability, a high level of accuracy, rapidity of analysis, and minimum sample preparation. © 2020 Society of Chemical Industry.

Detection of Orange Essential Oil, Isopropyl Myristate, and Benzyl Alcohol in Lemon Essential Oil by FTIR Spectroscopy Combined with Chemometrics.

Essential oils are high-valued natural extracts that are involved in industries such as food, cosmetics, and pharmaceutics. The lemon essential oil (LEO) has high economic importance in the food and beverage industry because of its health-beneficial characteristics and desired flavor properties. LEO, similar to other natural extracts, is prone to being adulterated through economic motivations. Adulteration causes unfair competition between vendors, disruptions in national economies, and crucial risks for consumers worldwide. There is a need for cost-effective, rapid, reliable, robust, and eco-friendly analytical techniques to detect adulterants in essential oils. The current research developed chemometric models for the quantification of three adulterants (orange essential oil, benzyl alcohol, and isopropyl myristate) in cold-pressed LEOs by using hierarchical cluster analysis (HCA), principal component regression (PCR), and partial least squares regression (PLSR) based on FTIR spectra. The cold-pressed LEO was successfully distinguished from adulterants by robust HCA. PLSR and PCR showed high accuracy with high R2 values (0.99-1) and low standard error of cross-validation (SECV) values (0.58 and 5.21) for cross-validation results of the raw, first derivative, and second derivative FTIR spectra. The findings showed that FTIR spectroscopy combined with multivariate analyses has a considerable capability to detect and quantify adulterants in lemon essential oil.

Detection of lard in butter using Raman spectroscopy combined with chemometrics.

There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods and food additives. The current paper presented a new Raman spectroscopy-based methodology for detection and quantification of lard in butter. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully performed for the classification and discrimination of butter and lard-adulterated samples. Strong discrimination pattern was observed in the HCA analysis. Also, partial least squares regression and principal component regression (R2 = 0.99) were applied for quantification of lard in butter samples. Quite favorable prediction capabilities were observed in the cross-validation of PLS and PCR analysis for the adulteration levels between 0% and 100% lard fat (w/w). Raman spectroscopy coupled chemometrics was employed effectively for quantification of lard fat in butter fat samples with easy, robust, effective, low-cost and reliable application in the quality control of butter.

Structural and physicochemical characterisation and antioxidant activity of an α-D-glucan produced by sourdough isolate Weissella cibaria MED17.

An exopolysaccharide (EPS) producer slimy-mucoid type colony was isolated from sourdough and identified as Weissella cibaria MED17. The 1H and 13C NMR spectra of EPS MED17 demonstrated that this EPS was a dextran type glucan ((1 â†’ 6)-linked α-D-glucose core structure) containing (1 â†’ 3)-linked α-D-glucose branches and proportion of (1 â†’ 6)-linked α-D-glucose units to (1 â†’ 3)-linked α-D-glucose units was 94.3:5.7%. The FTIR analysis also confirmed the (1 â†’ 6)-linked α-D-glucose linkage. A high level of thermal stability was observed for glucan MED17 as no degradation up to 300 °C was observed by TGA and DSC analysis. The XRD analysis of glucan MED17 showed its semi- crystalline nature and its compact sheet-like morphology was observed by SEM analysis. Finally, antioxidant characteristics of glucan MED17 were determined by ABTS and DPPH radical scavenging activity tests that revealed a moderate antioxidant activity of glucan MED17. These findings show potential techno-functional characteristics of glucan MED17.

Response surface methodology as optimization strategy for asymmetric bioreduction of acetophenone using whole cell of Lactobacillus senmaizukei.

Whole cell applications are one of the main methodologies for the bioreduction of prochiral ketones to obtain enantiomerically rich chiral secondary alcohols which are mainly affected by the culture parameters of the whole cell. In this study, whole cell of Lactobacillus senmaizukei as a safe Lactic Acid Bacteria (LAB) was used for the reduction of acetophenone and Response Surface Methodology (RSM) application was used to optimize the culture parameters in terms of temperature, pH, incubation time, and agitation level to obtain the highest enantiomeric excess (ee) and conversion rate. The predicted optimum conditions for the bioreduction with whole cell Lactobacillus senmaizukei were found to be pH of 5.25, temperature of 25 °C, incubation time of 72 hr, and agitation level of 100 rpm. Importantly, the efficiency of the reduction of the acetophenone was significantly affected by the linear and quadratic effects of culture parameters. These findings are important to show the role of culture parameters for the bioreduction reactions and also the efficiency of the RSM technique to optimize these parameters.

Partial characterization of a levan type exopolysaccharide (EPS) produced by Leuconostoc mesenteroides showing immunostimulatory and antioxidant activities.

Leuconostoc mesenteroides S81 was isolated from traditional sourdough as an exopolysaccharide (EPS) producer strain. The monosaccharide composition of the EPS from strain S81 was characterized by HPLC analysis and only fructose was found in the repeating unit structure. The NMR spectroscopy analysis revealed that EPS was a levan type EPS as a ß-(2 → 6)-linked fructan. The FTIR analysis further confirmed the presence of the furanoid rings in the EPS structure. The levan S81 showed high level of thermal stability determined by DSC and TGA analysis. The lyophilised levan S81 showed a sheet-like compact morphology and its aqueous solution formed spheroidal lumps with a compact structure detected by SEM and AFM analysis, respectively. Importantly the levan S81 showed a high level of immunomodulatory role, induced the anti-inflammatory cytokine IL-4, and exhibited a strong antioxidant capacity with EC50 value 1.7 mg mL-1 obtained by hydroxyl radical scavenging activity test under in vitro conditions. These findings reveal potential of levan S81 for technological purposes and as a potential natural immunomodulatory and antioxidant.

Characterization of chemical, molecular, thermal and rheological properties of medlar pectin extracted at optimum conditions as determined by Box-Behnken and ANFIS models.

In this work, response surface methodology and adaptive neuro-fuzzy inference system approaches were used to predict and model effect of extraction conditions of pectin from medlar fruit (Mespilus germanica L.). The pectin extracted at optimized conditions (89 °C, 4.83 h and 4.2 pH) could be classified as high methoxyl pectin. Sugar composition analysis showed that pectin was mainly composed of D-galacturonic acid, L-arabinose, L-rhamnose, D-galactose and D-glucose. Fourier Transform Infrared Spectroscopy, RAMAN and nuclear magnetic resonance spectra confirmed molecular structure, revealing presence of D-galacturonic acid backbone. X-ray diffraction patterns revealed an amorphous structure. Differential scanning calorimetry showed endothermic (123 °C) and exothermic peaks (192 °C). Thermogravimetric analysis revealed three decomposition regions, 50-225 °C, 225-400 °C and 400-600 °C. Steady and dynamic shear analyses revealed that pectin had a pseudo-plastic behavior with storage (G') and loss (G″) modulus increasing with increment in frequency, indicating viscoelastic structure more predominantly elastic than viscous.