粒子群最小二乘支持向量机结合偏最小二乘法用于芝麻油质量的鉴别

结合粒子群最小二乘支持向量机(PSO-LSSVM)与偏最小二乘法(PLS)提出一种基于气相色谱技术的新方法,对芝麻油进行真伪鉴别,并对掺伪品中掺假比例进行定量分析。采用主成分分析法(PCA)对857个样本的脂肪酸色谱数据进行分析,优选主成分作为最小二乘支持向量机(LSSVM)的输入向量。利用粒子群算法(PSO)优化LSSVM,构建芝麻油掺伪鉴别的两级分类模型,同时运用PLS建立掺伪芝麻油中掺伪油脂的定量校正模型,两级分类模型的准确率分别达到了100%和98.7%,定量分析模型的平均预测标准偏差(RMSEP)为3.91%。结果表明,本方法的鉴别准确性和模型泛化能力均优于经典的BP神经网络和支持向量机(SVM),可用于食用油脂加工和流通环节的质量控制,为食用油质量的准确鉴定提供了一条有效途径。     

粒子群算法结合支持向量机回归法用于近红外光谱建模

研究了最小二乘法支持向量机(LSSVM)应用于烟丝样品和小麦样品的近红外光谱建模,采用粒子群优化算法(PSO)优化LSSVM的参数。通过对烟草样品和小麦样品的近红外光谱建模和预测,并与常规的偏最小二乘法(PLS)比较发现,PSO-LSSVM法具有更好的预测效果和稳健性。     

应用高效液相色谱-二极管阵列检测器-蒸发光散射检测器联用技术同时测定清开灵注射液中的五类有效成分

建立了复方清开灵注射液中5类主要有效成分的定量测定方法。应用高效液相色谱-二极管阵列检测器-蒸发光散射检测器联用技术(HPLC-DAD-ELSD),根据各类成分紫外吸收光谱的差异,分别在240,254,280和330 nm波长下检测栀子苷、核苷(包括尿苷和腺苷)、黄芩苷和有机酸(包括绿原酸和咖啡酸)等4类成分,同时使用ELSD测定胆酸、熊去氧胆酸和猪去氧胆酸等3种甾体化合物,从而实现了清开灵注射液中5类有效成分(共9个化合物)的同时分离和定量测定。用该法测定了3个不同厂家的19批清开灵注射液成品。该法快速、准确,操作相对简单,为中药复方复杂体系的多组分定量测定和质量控制提供了一种可靠、合理且简便、易行的方法模式。     

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。     

燃料电池的机理模型及控制建模的研究

根据直接甲醇燃料电池(DNIFC)的组成结构、工作原理,并运用电化学,流体动力学、热力学等学科理论,建立了DNIFC电池性能数学模型,并结合DNIFC实验数据进行仿真,结果表明这种数学建模是合理和有效的。由于数学模型的复杂性难以满足工程上对PNIFC控制系统的设计特别是实时控制需要的情况,本文提出一种基于最小二乘支持向量机建模算法,用具有RBF核函数的LS-SVM离线建立DNIFC电堆的非线性模型;仿真和实验结果表明了该建模方法具有建模简单、模型精度高等优点,亦证明了该算法的有效性和优越性。研究结果对直接甲醇燃料电池控制系统的建模和控制具有一定的实用价值。     

支持向量机算法在冶金和材料科学中的应用

将支持向量机算法用于相图评估和预报,冶金物料物性建模,冶金生产优化及故障诊断,以及材料设计等方面,效果良好.     

清开灵注射液HPLC/ELSD指纹图谱建立及质量相关性研究

运用高效液相色谱／蒸发光散射检测器(HPLC／ELSD)建立了复方清开灵注射液的指纹图谱。对其中7种有效成分进行了定量测定,方法学考察结果良好;通过指纹图谱相似度评价软件,对10批清开灵注射液的HPLC／ELSD指纹图谱进行了相似度计算,结果表明：10批样品相似度很好。该方法为中药质量控制提供了一种可以同时实现整体定性、指标成分定量且简便易行的方法模式。     

基于自组织映射神经网络的中药注射剂质量快速鉴别方法

将近红外光谱分析技术与人工神经网络相结合,研究提出一种基于自组织映射神经网络的近红外光谱神经元分类模型,用于对中药注射剂产品的近红外光谱进行计算分析,可实现对注射剂质量的快速鉴别。以3个不同厂家生产的参麦注射剂为研究对象,考察本方法的分类能力,其分类正确率达到96.4%,优于参与比较的判别式偏最小二乘法(90.5%)、反向传播神经网络(88.1%)和支持向量机(90.5%)。     

基于局部最小二乘支持向量机的光谱定量分析

提出了一种基于局部最小二乘支持向量机(LSSVM)的回归方法,以克服待测参数和光谱数据间的非线性。本方法首先通过欧式距离选取局部训练样本子集,然后利用该子集建立LSSVM校正模型。由于每个测试样本建模时要选取不同的训练样本,因此提出相对距离的概念用来改进高斯核函数,使LSSVM的参数对于不同的训练样本具有自调整功能。针对一批汽油样本的实验结果表明,本方法的预测精度优于常见的局部线性建模方法和全局建模方法。     

基于支持向量机的汽油族组成近红外光谱分析方法研究

提出一种基于最小二乘支持向量机(LS-SVM)的汽油族组成近红外光谱分析方法。采用国家标准方法(GB 11132-1989液体石油产品烃类测定法——荧光指示剂吸附法)测定了重庆地区销售的汽油族组成,并采用主成分分析-最小二乘支持向量机建立汽油族组成的预测模型。预测模型对汽油中芳烃和烯烃含量的RMSEC分别为0.2090和0.2142。实验结果表明所建模型具有计算量小,预测准确、可靠,而且操作简单、维护费及测试费用低等特点。     

Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR

This paper presents the use of least-squares support vector machine (LS-SVM) for quantitative determination of hydroxyl value (OHV) of hydroxylated soybean oils by horizontal attenuated total reflection Fourier transform infrared (HATR/FT-IR) spectroscopy. A least-squares support vector machine (LS-SVM) calibration model for the prediction of hydroxyl value (OHV) was developed using the range 1805.1-649.9 cm(-1). Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oil predicted by the LS-SVM model to the values obtained by the AOCS standard method. A correlation coefficient equal to 0.989 and RMSEP = 4.96 mg of KOH/g was obtained. This study demonstrates a better prediction ability of the LS-SVM technique to determine OHV in hydroxylated soybean oil samples by HATR/FT-IR spectra.     

Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis–NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x–y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard–Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.     

共轴双脉冲激光诱导击穿光谱和最小二乘支持向量机法定量分析植物油中铬

利用共轴双脉冲激光诱导击穿光谱( DP-LIBS)技术对植物油(大豆油、花生油和玉米油)中的重金属铬( Cr)含量进行定量分析。采用Ava-Spec双通道高精度光谱仪采集样品的LIBS光谱,然后通过其LIBS谱线图确定了CN分子谱线(421.49 nm)、Ca原子谱线(422.64 nm)及Cr的3条原子谱线(425.39、427.43和428.87 nm),根据上述谱线建立了Cr元素的单变量定标模型和最小二乘支持向量机(LS-SVM)校正模型,并用验证样品对它们进行检验。研究结果表明,对于单变量定标法,大豆油、花生油及玉米油验证样品的平均预测相对误差(PRE)分别为12.57%,12.11%和13.72%;对于三变量LS-SVM法,其定标样品真实值与预测值之间的拟合度 R2分别为0.9785,0.9792和0.9654,验证样品的平均 PRE 分别为8.92%,8.33%和10.98%;对于五变量LS-SVM法(增加两基体元素谱线变量),其定标样品真实值与预测值之间的拟合度R2分别为0.9895,0.9901和0.9855,验证样品的平均PRE分别为7.46%,8.96%和8.95%。由此可知,LS-SVM校正模型性能优于单变量定标法,且五变量LS-SVM校正模型性能优于三变量LS-SVM校正模型;采用LS-SVM法及引入合适的基体元素谱线( CN、Ca)能有效减小定量分析误差,提高LIBS技术对植物油中Cr含量预测的精度。     

Classification of Rhizoma et Radix Notoperygii by HPLC Fingerprints with the Aid of Chemometrics

Chromatographic profiles of Rhizoma et Radix Notoperygii (RRN, “Qianghuo” in Chinese), a complex traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography with diode array detection (HPLC-DAD) at 330 nm. These data profiles were used as fingerprints to investigate quality control classification modeling of the RRN samples. In contrast to the classical methods for discrimination of TCMs, that is, just using common HPLC peaks, all chromatographic profile data were pre-processed by the correlation optimized warping method and polynomial functions; then, these data were submitted as fingerprints (variables) for classification on the basis of sample origin. Chemometrics methods used for calibration modeling and subsequent sample classification-least square support vector machine (LS-SVM), artificial neural network (ANN), and partial least square discriminant analysis (PLS-DA); all produced satisfactory calibrations as well as classification results.     

Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods

Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

支持向量机回归法在近红外光谱测定植物纤维原料中甲氧基含量中的应用

以26个植物纤维原料为实验材料,由20个样品作校正样品,采用径向基核函数方法对纤维原料中甲氧基含量与纤维原料样品近红外光谱进行支持向量机(SVM)回归建模.以所建SVM回归模型对6个纤维原料样品中甲氧基含量进行预测,回归模型的预测结果与采用改良的维伯克法确定的甲氧基含量的相关系数为0.977,预测样本集的标准偏差为0.43.将SVM回归模型的预测效果与PLS回归模型的预测结果进行比较,所建近红外光谱测定植物纤维原料中甲氧基含量的SVM回归模型可用于实际植物纤维原料样品的定量分析,且具有较好的分析效果.     

Quantification of <Emphasis Type="Italic">Lactobacillus</Emphasis> in fermented milk by multivariate image analysis with least-squares support-vector machines

This paper reports an approach for quantification of Lactobacillus in fermented milk, grown in a selective medium (MRS agar), by use of digital colour images of Petri plates easily obtained by use of a flatbed scanner. A one-dimensional data vector was formed to characterize each digital image on the basis of the frequency-distribution curves of the red (R), green (G), and blue (B) colour values, and quantities derived from them, for example lightness (L), relative red (RR), relative green (RG), and relative blue (RB). The frequency distributions of hue, saturation, and intensity (HSI) were also calculated and included in the data vector used to describe each image. Multivariate non-linear modelling using the least-squares support vector machine (LS-SVM) and a linear model based on PLS regression were developed to relate the microbiological count and the frequency vector. Feasibly models were developed using the LS-SVM and errors were below than 10% for Lactobacillus quantification, indicating the proposed approach can be used for automatic counting of colonies.