Process development for isolation of dietary eugenol from leaves of basil (Ocimum sanctum) in combination of optimization of process variables and modeling by artificial neural network

Dietary eugenol helps prevent free radical-induced and lifestyle-related chronic illnesses such as cancer, autoimmune disorders, cardiovascular disease, and aging. A technique for extracting eugenol from green basil (Ocimum sanctum) leaves is created using a combination of extraction variable optimization and the organization of an artificial neural network (ANN) model. For thermally degradable bioactive eugenol, solvent extraction is the recommended separation method. With the following optimum variables: polarity of the solvent of 0.009, the solid-solvent ratio of 1.0 ?g/20 ?mL, stirring speed of 200 ?rpm, extraction temperature of 40 ?°C, and extraction duration of 40 ?min, a yield of 5.39 × ?10^?3 ?kg eugenol per kilogram dried leaves of basil was found. At 10 ?min of batch extraction, the highest throughput of eugenol was found to be 5.4 ?× ?10^?3 ?kg ?m^?3 ?s^?1. Additionally, experimental data are used to construct the yield prediction model. The statistical parameters that are obtained in model evaluation encourage the use of the predicted model for the commercialization of eugenol isolation.     

基于神经网络的机械磨损故障光谱定位诊断法

在分析常用光谱定位诊断方法的基础上提出了基于神经网络的光谱定位诊断法；将机械摩擦副材质的元素含量作为神经网络输入，将材质所对应的部件作为神经网络输出，建立了相应的神经网络训练样本；通过整理训练样本和训练神经网络，利用神经网络超强的非线性映射能力和容错性实现了磨损故障部位诊断；通过算例分析验证了所提出的诊断方法的可行性和准确性．结果表明，所建立的方法简洁有效，并具有很高的诊断精度．     

Optimization of solar photocatalytic biodegradability of seawater using statistical modelling

The performance of zinc oxide (ZnO) as a photocatalyst was evaluated for the treatment of pollutants present in seawater. Batch experimental studies were carried out by varying the dosage of photocatalyst (1–4 ​g/L). The effect of reaction time, pH and the dosage of photocatalyst was evaluated with the percentage removal efficiencies of chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC) and the biodegradability (BOD/COD) of the seawater. Response surface methodology-central composite design (RSM-CCD) and artificial neural network-Levenberg Marquardt (ANN-LM) statistical models were employed to optimize the photocatalytic biodegradability (BOD/COD). A quadratic polynomial statistical model was obtained to predict the percentage removal efficiencies of COD, TOC, BOD and biodegradability. For the experimental runs, the maximum percentage removal efficiencies for COD, TOC, BOD was found to be 62.3, 40.1, and 18.8%, respectively. Whereas, the maximum biodegradability was 0.036. As per RSM-CCD and ANN-LM statistical model method the maximum percentage removal efficiencies were found to be COD ​= ​58.14, 60.39%, TOC ​= ​33.74, 40.09%, BOD ​= ​18.47, 18.7% and Biodegradability ​= ​0.0315, 0.0360, respectively. The predicted values from statistical models were well correlated with experimental values. ANN modelling predicted better values for the responses with an average of R² ​= ​0.99697 than RSM modelling with average R² ​= ​0.8948.     

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for “good practice” shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.     

On the Robustness of Forward and Inverse Artificial Neural Networks for the Simulation of Ethylene/1‐Butene Copolymerization

Forward and inverse artificial neural network (ANN) models are used to describe ethylene/1‐butene copolymerization with a model catalyst having two site types. The forward ANN predicts number and weight average molecular weights, average comonomer content, and polymer yield as a function of a set of polymerization conditions, while the inverse model estimates polymerization conditions needed to produce copolymers with desired microstructures. The forward model is found to be robust and resilient to random noise introduced into the datasets. The inverse model, however, leads to multiple solutions (several polymerization conditions can produce polymers with similar microstructures) and is sensitive to random noise in the data. Although the polymerization conditions estimated from inverse ANN are different from the model data, the estimated polymerization conditions are found to provide similar microstructures even with the random noise.     

Ion chromatographic separations of phosphorus species: a review

The aim of this paper is to review recent literature regarding the determination of phosphorus species by ion chromatography (IC), and describe the implementation of new developments in sample treatment and ion chromatography methodology for the analysis of these compounds. Ion-exchange methods using both carbonate/hydrogencarbonate and hydroxide selective columns in combination with self-regenerating membrane and solid-phase-based suppressors enable determination of phosphate down to ppb levels. New technology, particularly on-line electrolytic hydroxide generators and electrolytic self-regenerating suppressor devices, has allowed the use of elution gradients in both carbonate/hydrogencarbonate and hydroxide selective systems, improving sensitivity and reducing total analysis time for samples containing phosphate together with other inorganic anions. In addition to a review of these developments, optimization and application of chromatographic methods using reversed stationary phases and cationic and/or zwitterionic surfactants is also discussed.The objective of most of the IC methods developed for phosphorus species is the determination of phosphate and total phosphorus. Therefore, sample treatment and separation conditions specifically developed for this purpose are also described. In addition, application of IC to the analysis of other inorganic (reduced and condensed) and organic (phytates, alkyl phosphate, and phosphonates) phosphorus species is discussed along with methodology and relevant applications in water analysis and other miscellaneous fields.     

Electronic Noses Using Quantitative Artificial Neural Networ

The present paper covers a new type of electronic nose(e-nose) with a four-sensor array,which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error(RE) in changing quantitative analysis into the artificial neural network (ANN).. Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, liquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.     

Electronic Noses Using Quantitative Artificial Neural Network

The present paper covers a new type of electronic nose (e-nose) with a four-sensor array, which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error (RE) in changing quantitative analysis into the artificial neural network (ANN). Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, iiquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.     

聚丙烯酸酯类Tg的量子化学-神经网络研究

用密度泛函方法在6-31G(d)基组上优化了38种聚丙烯酸酯类的结构单元, 得到了其单元的量子化学参数, 探讨了这些参数与聚丙烯酸酯类玻璃化温度(Tg)的关系. 计算表明, 影响聚丙烯酸酯类Tg的主要因素有结构单元的侧链长度、侧链的分支数、最高占据轨道能级、极化率、偶极矩、等体积热容和热力学能等参数. 用模式识别方法(偏最小二乘法)讨论了这些参数与Tg的定性关系, 两类Tg大小不同的聚合物基本分布在不同区域, 用逐步回归和人工神经网络方法建立了这些参数与Tg的定量关系, 2种方法的预测结果与实验值的相关系数分别为0.9753、0.9985, 标准偏差分别为18.42、4.25, 预报结果与实验值基本一致.     

ANN based modelling of hydrodynamic cavitation processes: Biomass pre-treatment and wastewater treatment

We have developed artificial neural network (ANN) based models for simulating two application examples of hydrodynamic cavitation (HC) namely, biomass pre-treatment to enhance biogas and degradation of organic pollutants in water. The first case reports data on influence of number of passes through HC reactor on bio-methane generation from bagasse. The second case reports data on influence of HC reactor scale on degradation of dichloroaniline (DCA). Similar to most of the HC based applications, the availability of experimental data for these two applications is rather limited. In this work a systematic methodology for developing ANN model is presented. The models were shown to describe the experimental data very well. The ANN models were then evaluated for their ability to interpolate and extrapolate. Despite the limited data, the ANN models were able to simulate and interpolate the data for two very different and complex HC applications very well. The extrapolated results of biomethane generation in terms of number of passes were consistent with the intuitive understanding. The extrapolated results in terms of elapsed time were however not consistent with the intuitive understanding. The ANN model was able to generate intuitively consistent extrapolated results for degradation of DCA in terms of number of passes as well as scale of HC reactor. The results will be useful for developing quantitative models of complex HC applications.