基于支撑向量机方法的有机化合物的生成Gibbs自由能的预测

基于支撑向量机方法的有机化合物的生成Gibbs自由能的预测;支撑向量机;多元线形回归;吉布斯自由能     

支持向量机分类和回归用于肽的QSAR研究

使用支持向量机技术对两类肽化合物体系进行了分类和回归研究,并将其系统地与K最邻近法、多元线性回归、偏最小二乘、人工神经网络进行了比较。结果表明,对于小样本、非线性问题,支持向量机具有较强的稳定性能及泛化能力,在大多数情况下能够得到优于传统方法的建模效果。对于分类问题,支持向量机对训练集和测试集都达到了100%的分类正确率;对于回归问题,支持向量机虽对训练集样本拟合效果略低于人工神经网络,但对外部测试集却表现出较强的预测能力。     

Enhanced synchronous spectrofluorimetric determination of aflatoxin B1 in pistachio samples using multivariate analysis

The determination of aflatoxin B₁ (AFB1) in pistachio has been accomplished by normal and synchronous fluorimetry in combination with some multivariate calibration methods and derivative techniques. Extending the two-dimensional synchronous fluorescence scan to a three-dimensional total synchronous fluorescence scan was used to obtain the optimized Δλ for AFB1 in pistachio sample. The methods are based on the enhanced fluorescence of AFB1 by β-cyclodextrine in 10% (v/v) methanol-water solution. Twenty-six pistachio samples containing AFB1 in the range 0-15 ppb were used as calibration set. Eighteen combinational methods were tested to make best model for prediction of AFB1 and finally best results obtained using a method based on synchronous fluorimetry in combination with multiple linear regressions (MLR). For concentrations ranging from 0 to 15 ppb of AFB1 in 22 pistachio samples as prediction set, the values of root mean square difference (RMSD) and relative error of prediction (REP) using MLR were 0.328 and 4.453%, respectively were observed. Two naturally contaminated pistachio samples were analyzed by synchronous fluorimetry using MLR and compared with HPLC results.     

Comparative Study of Variable Selection Using Genetic Algorithm with Various Types of Chromosomes

In this study,different methods of variable selection using the multilinear step-wise regression(MLR) and support vector regression(SVR) have been compared when the performance of genetic algorithms(GAs) using various types of chromosomes is used.The first method is a GA with binary chromosome(GA-BC) and the other is a GA with a fixed-length character chromosome(GA-FCC).The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression(FSR) model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability(R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675) using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.