纸浆卡伯值的可见分光光度法在线测量

卡伯 (Kappa)值是纸浆质量的重要指标 ,而纸浆中的木素含量决定了纸浆的卡伯值。该文研究了用可见分光光度法在线测量酸法制浆过程蒸煮液中溶出的木素含量去预测纸浆中木素含量和纸浆卡伯值的方法 ;确定了合适的可见光光谱测量波段 (460～580nm) ;建立了纸浆卡伯值与蒸煮液吸光度 (A)的相关方程。结果显示卡伯值与A之间有良好的线性关系 ,为实现蒸煮过程纸浆卡伯值的在线测量提供了依据。     

吸光度比值-导数光谱法同时测定苯酚和间苯二酚

吸光度比值-导数光谱是近年来提出的多组分光谱分析法.该法以二元混合物的混合光谱与其中某一组分(被视为干扰组分)的标准光谱的比值对波长求导,从而得到吸光度比值-导数光谱.由于干扰组分对吸光度比值-导数光谱的贡献为零,所以可利用吸光度比值-导数值测出待测组分的含量.本文在普通分光光度计上用计算法完成了吸光度比值导数运算,同时测定了复方雷琐辛涂剂中苯酚(P)和间苯二酚(R)的含量,取得了较为满意的结果.     

凝胶渗透色谱法对桉木过氧化氢化学法化学机械浆制浆过程木素分子量变化的机理研究

利用凝胶渗透色谱法和元素分析法测定了尾叶桉酶解木素在过氧化氢化学法化学机械浆(APMP)制浆及漂白过程分子量的变化.研究结果表明:由于HOO的特殊作用制浆及漂白之后,浆中剩余木素的平均分子量相对有所增加.     

化学镀镍液中次磷酸钠在线检测仪的设计

基于次磷酸钠的化学性质和碘溶液的光谱吸收特征，将现有的分光光度计进行改造，采用流动注射光度分析法测定镀液中次磷酸钠的含量，借助于计算机控制，实现次磷酸钠的在线检测，能满足生产工艺分析的要求。     

基于氧化淀粉的新型Pickering苯丙乳液表面施胶剂的合成

以苯乙烯、丙烯酸丁酯为单体,过硫酸铵为引发剂,氧化淀粉作固体粒子乳化剂,采用无皂乳液聚合的方法来制备Pickering苯丙聚合物乳液,考察了不同含量的氧化淀粉对乳液固含量、转化率、卡伯值以及稳定性的影响,并对制备的Pickering苯丙聚合物乳液进行了粒径、红外光谱表征,当氧化淀粉的含量为2%时,制备的Pickering苯丙聚合物乳液转化率为96%,粒径为264.4nm,卡伯值为71.4g/m~2,抗水性和稳定性较好.     

凝胶渗透色谱法对桉木过氧化氢化学法化学机械浆制浆过程木素分子量变化的

利用凝胶渗透色谱法和元素分析法测定了尾叶桉酶解木素在过氧地化学机械浆（ＡＰＭＰ制浆及漂白过程分子量的变化。研究结果表明：由于ＨＯＯ的特殊作用制浆及漂白之后,浆中剩余木素的平均分子量相对有所增加。     

主成分-BP算法在近红外光谱法纸浆卡伯值测量中的应用研究

提出采用主成分-BP算法建立纸浆卡伯值近红外光谱法在线测量模型。结果表明,这种算法由于既考虑到了近红外光谱响应的非线性因素,又可防止BP算法在建模时出现“过拟合”的现象,利用该算法建立的纸浆卡伯值测量模型与一元回归,多元回归和主成分回归等线性方法相比,具有更高的预测精度。     

迭代法在相思树近红外光谱分析中的应用

以相思树样本的克拉森木素含量为研究对象,利用多波长下的近红外光谱数据建立了若干个预测木素含量的子数学模型.使用加权平均值公式给出了木素含量的首次近似值.根据木素含量实验值与近似值之间所具有的较强线性关系,给出了建立近红外光谱数据预测模型的迭代法.模型的预测精度随迭代次数的增加而提高.本迭代法有望用于其它树木某些化学成分...     

溶液中荧光材料发光效率的快捷测定方法

本文在通用比较法测量材料发光效率的基础上,提出了改进的溶液中荧光材料发光效率的快捷测定方法。利用紫外-可见分光光度计和荧光分光光度计,本文探讨了常见的几种标准荧光物质溶液的吸光度与发光光谱之间的内在联系,归纳出标准溶液的吸光度与其所发射的光子数随着荧光波长变化的函数关系。通过拟合函数参数,标准荧光物质的发射光子数可通过公式由溶液的吸光度来求得。因此,只需测量未知材料溶液的吸光度与发光强度,通过比较同吸光度标准溶液的发光强度,即可测定该材料的发光效率,从而取代了传统比较法的繁琐测量过程。经过对几种半导体量子点的发光效率的测量验证,结果证明本文的公式计算结果与传统的直接比较法测量结果相一致。     

配位聚合物{[CuL2].2H2O}n的合成、晶体结构及光学性质

制备了配位聚合物2-吡啶甲酸铜{[CuL2]·2H2O}n(HL=2-吡啶甲酸).通过元素分析、红外光谱、X-射线单晶衍射等方法对配位聚合物结构进行了表征.采用紫外-可见光谱和荧光光谱研究了配位聚合物在DMF溶液中的发光性能.结果表明,目标配位聚合物具有良好的光学性质,温度对在272nm的吸光度值和346nm处的荧光发射强度有重要影响.同时循环伏安曲线显示标题配位聚合物的氧化还原过程较困难,电化学性质稳定性.     

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。     

Simultaneous determination of composition of human urinary calculi by use of artificial neural networks

A new chemometric method, which uses artificial neural networks (ANN), is presented for determination of the composition of urinary calculi. The selected constituents were whewellite, weddellite, and uric acid from which approximately 40% of the urinary calculi obtained from Macedonia patients are composed. The results for the synthetic mixtures were better then those obtained by partial least squares (PLS) regression or by the principal component regression (PCR), because neural networks have better prediction capacity. The generalization abilities of the optimized neural networks were checked using the standard addition method on carefully selected real natural samples.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

Quantification of butanol and ethanol in aqueous phases by reflectometric interference spectroscopy--different approaches to multivariate calibration

This paper presents several methods for analysis of data from reflectometric interference spectroscopic measurements (RIfS) of water samples. The set-up consists of three sensors with different polymer layers. Mixtures of butanol and ethanol in water were measured from 0 to 12,000 ppm each. The data space was characterized by principal component analysis (PCA). Calibration and prediction were achieved by multivariate methods, e.g. multiple linear regression (MLR), partial least squares (PLS) with additional predictors, and quadratic partial least squares (Q-PLS), and by use of artificial neural networks. Artificial neural networks gave the best results of all the calibration methods used. Calibration and prediction of the concentration of the two analytes by artificial neural nets were robust and the set-up could be reduced to only two sensors without deterioration of the prediction.     

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。     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

Differential Kinetic Spectrophotometric Determination of Methamidophos and Fenitrothion in Water and Food Samples by Use of Chemometrics

A spectrophotometric method for simultaneous analysis of methamidophos and fenitrothion was proposed by application of chemometrics to the spectral kinetic data, which was based upon the difference in the inhibitory effect of the two pesticides on acetylcholinesterase (AChE) and the use of 5,5′‐dithiobis(2‐nitrobenzoic acid) (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The absorbance of the chromogenic product was measured at 412 nm. The different experimental conditions affecting the development and stability of the chromogenic product were carefully studied and optimized. Linear calibration graphs were obtained in the concentration range of 0.5–7.5 ng·mL^?1 and 5–75 ng·mL^?1 for methamidophos and fenitrothion, respectively. Synthetic mixtures of the two pesticides were analysed, and the data obtained processed by chemometrics, such as partial least square (PLS), principal component regression (PCR), back propagation‐artificial neural network (BP‐ANN), radial basis function‐artificial neural network (RBF‐ANN) and principal component‐radial basis function‐artificial neural network (PC‐RBF‐ANN). The results show that the RBF‐ANN gives the lowest prediction errors of the five chemometric methods. Following the validation of the proposed method, it was applied to the determination of the pesticides in several commercial fruit and vegetable samples; and the standard addition method yielded satisfactory recoveries.     

Quantification of butanol and ethanol in aqueous phases by reflectometric interference spectroscopy – different approaches to multivariate calibration

This paper presents several methods for analysis of data from reflectometric interference spectroscopic measurements (RIfS) of water samples. The set-up consists of three sensors with different polymer layers. Mixtures of butanol and ethanol in water were measured from 0 to 12,000 ppm each. The data space was characterized by principal component analysis (PCA). Calibration and prediction were achieved by multivariate methods, e.g. multiple linear regression (MLR), partial least squares (PLS) with additional predictors, and quadratic partial least squares (Q-PLS), and by use of artificial neural networks. Artificial neural networks gave the best results of all the calibration methods used. Calibration and prediction of the concentration of the two analytes by artificial neural nets were robust and the set-up could be reduced to only two sensors without deterioration of the prediction. Received: 29 September 2000 / Revised: 30 April 2001 / Accepted: 3 May 2001