有机磷农药构效关系的主成分分析-人工神经网络研究

采用主成分分析法对样本数据集进行预处理，将得到的新的样本数据集输入人工神经网络，对有机磷农药的毒性参数进行预报。研究结果表明，主成分分析－人工神经网络的预报精度优于单纯的人工神经网络。     

一种新的拓扑指数用于手性化合物的构效关系研究

为了研究羟酸类和氨基酸类手性化合物的构效关系,本实验引进了手性乘积算法:P＝(1-b/a)(1-c/b)(1-a/c)(1-a/d),其中a、b、c和d分别为与分子中手性中心碳原子4个不同连接片断手性eAm指数和分子连接性指数mx的计算值,并考察了所得参数与羟酸类和氨基酸类化合物手性薄层色谱保留指数的相关性.为了评定所得预测模型的稳定性,文中运用了交叉验证法.结果表明,由人工神经网络方法(BP法)所得数学模型比较稳定,且明显优于多元回归分析结果.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Classification of cultivated mussels from Galicia (Northwest Spain) with European Protected Designation of Origin using trace element fingerprint and chemometric analysis

Inductively coupled plasma-mass spectrometry (ICP-MS) in combination with different supervised chemometric approaches has been used to classify cultivated mussels in Galicia (Northwest of Spain) under the European Protected Designation of Origin (PDO). 158 mussel samples, collected in the five rías on the basis of the production, along with minor and trace elements, including high field strength elements (HFSEs) and rare earth elements (REEs), were used with this aim. The classification of samples was achieved according to their origin: Galician vs. other regions (from Tarragona, Spain, and Ethang de Thau, France) and between the Galician Rías. The ability of linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA) and artificial neural network (ANN) to classify the samples was investigated. Correct assignations for Galician and non-Galician samples were obtained when LDA and SIMCA were used. ANNs were more effective when a classification according to the ría of origin was to be applied.     

迭代目标转换因子分析与人工神经网络法用于邻、间、对硝基甲苯的分光光度同时测定的比较研究

本文将迭代目标转换因子分析与人工神经网络法用于分光光度法同时测定邻、间、对硝基甲苯,并与目标转换因子分析的结果进行了比较.结果表明,迭代目标转换因子分析法与线性网络法的效果都很好.其相对误差分别为1.3％和1.2％,而目标转换因子分析法的预测误差较大,其相对误差为10.4％.     

Multi‐Channel Piezoelectric Quartz Crystal Sensor with Principal Component Analysis and Back‐Propagation Neural Network for Organic Pollutants from Petrochemical Plants

A multi‐channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home‐made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4‐tert‐Butylcalix[6]arene, Cholesteryl chloroformate, C60‐Polyphenyl acetylene (C60‐PPA) and Ag(I)/cryptand‐2,2/Ethylene diamine/NH₃/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back‐propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi‐channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068‐1.127 mg/L for these organic pollutants. The multi‐channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1‐9.6%. Furthermore, this multi‐channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.     

Identification and Optimization of a Novel Taxanes Extraction Process from Taxus cuspidata Needles by High-Intensity Pulsed Electric Field

Taxanes are a series of natural compounds with great application potential in antitumor therapy, whereas the lack of efficient taxanes extraction methods significantly hinders the development of taxanes. The high-intensity pulsed electric field (PEF) is a novel technology used to extract bioactive ingredients from food and other natural products. However, the prospect of using PEF for taxanes extraction remains to be elucidated. Herein, we extracted taxanes from Taxus cuspidata via PEF and explored the effects of seven extraction conditions on the yields of target compounds. The Placket–Burman design (PBD) assay revealed that electric field strength, pulse number, and particle size are key factors for taxanes extraction. The response surface methodology (RSM) and back-propagation neural network conjugated with genetic algorithm (GA-BP) were further used to model and predict the optimal extraction conditions, and GA-BP exerted higher reliability, leading to a maximum extraction yield of 672.13 μg/g under electric field strength of 16 kV/cm, pulse number of 8, particle size of 160 meshes, solid–liquid ratio of 1:60, a single extraction, centrifugal speed of 8000 r/min, and flow rate of 7 mL/min, which was 1.07–1.84 folds that of control, solid–liquid extraction (SL), and ultrasonic extraction (US) groups. Additionally, the scanning electron microscopy (SEM) results indicated that the sample particles extracted by PEF method exhibited a coarser surface morphology. Thus, we present for the first time that PEF is feasible for the extraction of taxanes from Taxus cuspidata and highlight the application value of the PBD, RSM, and GA-BP models in parameters optimization during extraction process.     

A 21st century technique for food control: Electronic noses

This work examines the main features of modern electronic noses (e-noses) and their most important applications in food control in this new century. The three components of an electronic nose (sample handling system, detection system, and data processing system) are described. Special attention is devoted to the promising mass spectrometry based e-noses, due to their advantages over the more classical gas sensors. Applications described include process monitoring, shelf-life investigation, freshness evaluation, authenticity assessment, as well as other general aspects of the utilization of electronic noses in food control. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in food control are also mentioned.