循环空间回归为喹喏酮N1位构效关系建模

拟从线性泛函的角度分析循环子空间回归（CSR）。CSR方法将从自变量参数矩阵和因变量向量中提取成分,循环地构造并扩张Krylov子窨,且以此作为源空间,运用最小二乘准则解最映射到因变量实空间的线性泛函。整个求解过程包容了最小二乘回归（LSR）、主成分回归（PCR）、偏最小二乘回归（PLS）以及其它中间的回归方法。然后以预报能力的强弱,从中确定最佳回归模型。本文应用SCR方法为喹喏酮N1位抗菌构效关系建模,效果良好。     

CCD近红外光谱快速测定柴油中的芳烃含量

研究了采用电感耦合器件（ＣＣＤ）近工外光谱仪在短波近红外区域（７００－１１００ｎｍ）、利用偏最小二乘回归（ＰＬＳ）测定柴油中芳烃含量的方法。考虑样品颜色对短波近红外光谱的影响,对波长范围的选择和基线处理方式进行了研究。考察了样品进入光路的时间对测定结果的影响。将ＣＣＤＮＩＲ对未知样品的预测结果与傅里叶变换近红外光谱（ＦＴ－ＮＩＲ）在长波近红外范围（１０００－２０００ｎｍ）的预测结果及液相色谱的测定     

人工神经网络用于近红外光谱测定柴油闪点

采用主成分－人工神经网络对不同留程柴油的近红外光谱进行校正,预测其闪点。采用监控集控制网络训练过程,以避免过训练。探讨了人工神经网络（ＡＮＮ）、直接线性连接人工神经网络（ＬＡＮＮ）的校正效果,并与局部权重回归（ＬＷＲ）、主成分回归（ＰＣＲ）及偏最小二乘（ＰＬＳ）等校正方法进行了比较,认为人工神经及直接线性关联的较好手段。     

含硅二硫代磷酸酯的合成及生物活性

硅基甲硫醇Ｒ＾１Ｒ＾２ＣＨ３ＳｉＣＨ２ＳＨ与Ｏ，Ｏ－二烷基二硫代磷酸酯（ＲＯ）２Ｐ（Ｓ）ＳＨ及甲醛可顺利地发生类Ｍａｎｎｉｃｈ缩合反应，利用此反应和硅基甲硫醇与Ｏ，Ｏ－二乙基－Ｓ（２－溴乙基）二硫代磷酸酯的取代反应合成了３７种新的含硅二硫代到酯化合物（ＲＯ）２Ｐ（Ｓ）Ｓ（ＣＨ２）ｎＳＣＨ２ＳｉＣＨ３Ｒ＾１Ｒ＾２（ｎ＝１，２），在初筛浓度下，该类化合物具有一定的杀虫，杀螨活性。     

模糊聚类-偏最小二乘光度法同时测定锇和钌

将模糊聚类分析与偏最小二乘相结合，对锇、钌双组份重叠光谱进行解析；较好地解决了校正分析中校准模型的优化问题，提高了分析结果的准确度；研究了锇、钌－ＳｎＣｌ２－乳化剂ＯＰ－阿拉伯树胶（ＡＧ）－丁基罗丹明Ｂ（ＢＲＢ）高灵敏多元离子缔合物显色体系。锇、钌的检出限分别为 ０，６３２ μｇ／Ｌ和 ０．３６８ μｇ／Ｌ，模拟地质样品分析结果的相对误差小于±１０％。     

紫外-可见光谱-偏最小二乘法测定渣油四组分含量

用已建立的渣油聚类和识别模型将渣油分为坟渣油、减压渣油和加氢渣油,用紫外－可见光谱－偏最小二乘法（ＵＶ－ＰＬＳ）分别建立了这３种渣油的饱和烃、芳烃、胶质和沥青质四组分测定模型。该方法的测定结果与经典洗脱色谱法（ＥＣ）的结果相吻合。与ＥＣ方法相比,该方法具有快速、操作简单、不需分离,重复性好等特点。     

循环子空间回归为喹喏酮N1位构效关系建模

拟从线性泛函的角度分析循环子空间回归(CSR).CSR方法将从自变量参数矩阵和因变量向量中提取成分,循环地构造并扩张Krylov子空间,且以此作为源空间,运用最小二乘准则解得映射到因变量实空间的线性泛函.整个求解过程包容了最小二乘回归(LSR)、主成分回归(PCR)、偏最小二乘回归(PLS)以及其它中间的回归方法.然后由预报能力的强弱,从中确定最佳回归模型.本文应用CSR方法为喹喏酮N₁位抗菌构效关系建模,效果良好.     

红外光声光谱技术结合稳健回归用于油菜籽定量分析

将稳健偏最小二乘回归和偏稳健M回归应用于油菜籽红外光声光谱分析中含氮量、含油量与硫苷含量的测定。结果表明,利用红外光声光谱技术可以实现油菜籽品质的快速测定。同时,稳健回归模型较经典偏最小二乘回归模型可以更好地规避异常值影响,进而优化定量分析结果;其中,稳健偏M回归稍优于稳健偏最小二乘回归。     

溶剂浮选—偏最小二乘回归光度法测定地质样品痕量贵金属元素

研究了贵金属Ｒｕ、Ｒｈ、Ｐｄ、Ａｕ－ＳｎＣｌ２－ＲＢ体系及缔和物溶剂浮选的条件,采用偏最小二乘回归法对重叠光谱进行解数据处理。对地质样品Ｒｕ、Ｒｈ、Ｐｄ、Ａｕ同时测定,相对误差小于１１．１％,标准差为０．００６２－０．０１９。     

柄型金属有机化合物（Ⅳ）：四甲基二硅桥连取代环戊二烯？…

四甲基二硅桥连取代环戊二烯基配体相继与丁基锂及ＭＣ１４。２ＴＨＦ作用,生成四甲基二硅桥连取代环戊二烯基钛和锆化合物（Ｍｅ２ＳｉＳｉＭｅ２）（Ｃ５Ｈ４Ｒ）（Ｃ５Ｈ４Ｒ１）ＭＣ１２「Ｒ＝Ｈ,Ｒ１＝ｔ－Ｂｕ,Ｍ＝Ｔｉ（１）,Ｚｒ（２）,Ｈｆ（３）;Ｒ＝Ｈ,Ｒ１＝Ｍｅ,Ｍ＝Ｔｉ（４）;Ｒ＝Ｒ１＝Ｍｅ,Ｍ＝Ｔｉ（５）,Ｚｒ（６）」。通过元素分析、ＭＳ和Ｈ１ＮＭＲ谱表征了化合物的分了结构,并通过Ｘ射线衍射分析     

Random subspace regression ensemble for near-infrared spectroscopic calibration of tobacco samples.

An ensemble, a model-independent technique based on combining several models for classification/regression tasks, allows us to achieve a high accuracy that is often not achievable with single models. Such combinations have gained increasing attention in many fields. This paper proposes the use of random subspace (RS)-based regression ensemble as an alternative method for near-infrared (NIR) spectroscopic calibration of tobacco samples. Because of the considerable reduction of variables in a random subspace, multiple linear regression (MLR) is used as the base algorithm and the method is therefore also referred to as RS-MLR. The overall performance of the proposed RS-MLR method is compared to those of partial least square regression (PLSR), kernel principal component regression (KPCR) and kernel partial least square regression (KPLSR). The results reveal that the RS-MLR method not only has a simple concept but also can produce a more parsimonious and more accurate calibration model than PLSR, KPCR and KPLSR, at a lower computational cost. Besides, we also found that the RS-MLR method is very appropriate for the so-called small sample problems and that the calibration models built by RS-MLR are less sensitive to overfitting.     

化学计量学-分光光度法同时测定饲料中的诺氟沙星、氧氟沙星和洛美沙星

在pH1.81的Britton-Robinson(B-R)缓冲溶液中对诺氟沙星、氧氟沙星和洛美沙星三组分混合溶液进行光度测定,所得的重叠光谱数据用经典最小二乘(CLS),主成分回归(PCR),偏最小二乘(PLS)和径向基人工神经网络(RBF-ANN)方法处理和分析,结果表明RBF-ANN对合成样中三种药物浓度的预报结果...     

Quantitative analysis of slurry sample by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been employed for the analysis of slurry samples. Quantitative analysis of slurry samples is crucial and challenging. The problems associated with slurry samples include splashing, surface turbulence, and the difficulties of obtaining reproducible samples due to sedimentation. The LIBS analysis has achieved limited success due to inherent disadvantages when applied to slurry samples. In order to achieve improved measurement precision and accuracy, a spin-on-glass sampling method was evaluated. Five elements (Al, Ca, Fe, Ni, and Si) were examined in five slurry simulants containing varying amounts of each ion. Three calibration models were developed by using univariate calibration, multiple linear regression, and partial least square regression. LIBS analysis results obtained from the partial least square regression model were determined to be the best fit to results obtained from inductively coupled plasma optical emission spectroscopy analysis.     

近红外光谱相似性评估结合局部回归方法无损检测苹果糖度

基于Bayesian相似性评估方法结合偏最小二乘局部回归,对苹果近红外数据库进行数据挖掘。通过相似性计算方法搜索出与预测样品相近的近红外光谱,形成校正子集后采用局部回归方法获得待测样品的相关信息。该方法所建立局部模型的平均检验标准偏差(SEV)约为0.57,分析30个预测样品的预测标准偏差(SEP)约为0.61;基于马氏距离的传统方法建立的偏最小二乘局部模型的平均SEV为0.59,分析30个待测样品的预测SEP为0.64;而采用整个数据库建立的全局偏最小二乘模型的SEV约为0.65,分析30个预测样品SEP约为0.70。基于Bayesian相似性评估的局部回归方法在苹果糖度的近红外无损定量分析中获得较好的应用结果,在实际应用中该方法比全局回归方法具有更强的适用性,为近红外光谱分析提供了新的分析工具。     

互信息诱导子空间集成偏最小二乘在近红外光谱定量校正中的应用

在集成框架下,提出了一种联合自助采样和基于互信息变量选择的子空间回归集成偏最小二乘算法MISEPLS.此算法的核心是通过训练集自助采样和随后计算互信息的方式来引入成员模型的差异性.由于互信息量小于一个特定阈值的变量被淘汰,每个成员模型在原始变量的一个子空间得到训练.模型融合考虑了简单平均和加权平均两种方式.通过两个近红外光谱定量校正实验,与建立单模型的全谱偏最小二乘算法(PLS)和基于互信息变量选择的偏最小二乘算法(MIPLS)进行了比较.结果表明,在不增加模型复杂度的情况下,MISEPLS能建立起更精确、更稳健的校正模型.     

Determination of Parameter Uncertainty for Quantitative Analysis of Shaddock Peel Pectin using Linear and Nonlinear Near-infrared Spectroscopic Models

Fourier transform near-infrared spectrometry has been used in combination with multivariate chemometric methods for wide applications in agriculture and food analysis. In this paper, we used linear partial least square and nonlinear least square support vector machine regression methods to establish calibration models for Fourier transform near-infrared spectrometric determination of pectin in shaddock peel samples. In particular, the tunable kernel parameters of the linear and nonlinear models were set changing in a moderate range and were optimally selected in conjunction with a Savitzky–Golay smoother. The smoothing parameters and the linear/nonlinear modeling parameters were combined for simultaneous optimization. To investigate the robustness of calibration models, parameter uncertainty were estimated in a direct way for the optimal linear and nonlinear models. Our results show that the nonlinear least square support vector machine method gives more accurate predictive results and is substantially more robust compared to the spectral noise when compared with the linear partial least square regression. Furthermore, the optimized least square support vector machine model was evaluated by the randomly selected test samples and the model test effect was much satisfactory. We anticipate that these linear and nonlinear methods and the methodology of determination of model parameter uncertainty will be applied to other analytes in the fields of near-infrared or Fourier transform near-infrared spectroscopy.     

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。     

稳健线性回归法探讨分析精度与浓度之间的关系

研究分析精度与浓度之间的关系对于分析温度的质量控制具有重要意义。本文以土壤及水系沉积中微量元素为例,研究了不同实验室之间分析精度与被测组分浓度之间的关系。应用重新加权迭代最小二乘法（ＩＲＬＳ）成功地对分析结果的标准偏差与含量之间的关系进行了线性拟合,表明线性关系良好,与经典最小二乘法比较发现,ＩＲＬＳ法具有很好的稳健性,受离群值的影响小,回归结果也更符合实际。