等离子体刻蚀工艺控制模型分析

讨论了主因素分析法以及神经网络法在等离子体刻蚀工艺中的应用.结果表明主元素分析法可以实现对数据的压缩,而神经网络算法则显示出比传统的统计过程控制算法更好的准确性.     

浙江中部夏熟作物田主要杂草生态相似性的数量分析

应用七级目测法对浙江中部地区27个夏熟作物田样点的杂草进行了优势等级调查，将所得数据转换成重要值，以杂草在27个样点中的重要值为运算指标，应用主成分分析法和图论聚类中的最小生成树法，对24种杂草的生态学相似性进行了比较，指出了长期使用单一的除草剂导致农田杂草种群迅速更迭的原因：杂草对除草剂的敏感性差异及不同杂草间的生态学特性的差异。     

Mathematical topographical correction of XPS images using multivariate statistical methods

For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background image acquisition and subtraction are frequently utilized to minimize topographical effects so that images represent concentration variations in the sample. This procedure may significantly increase the data acquisition time. Multivariate statistical methods can assist in resolving topographical and chemical information from multispectral XPS images. Principal component analysis (PCA) is one method for identification of the highest correlation/variation between the images. Topography, which is common to all of the images, will be resolved in the first most significant component. The score of this component contains spatial information about the topography of the surface, whereas the loading is a quantitative representation of the topography contribution to each elemental/chemical image. The simple‐to‐use self‐modelling mixture analysis (Simplisma) method is a pure variable method that searches for the source of most differences in the data and therefore has the potential to distinguish between chemical and topographical phases in images. The mathematical background correction scheme is developed and validated by comparing results to the experimental background correction for samples with differing degrees of topography. Copyright © 2004 John Wiley & Sons, Ltd.     

Attenuated Total Reflection-Fourier transform infrared analysis of the fermentation process of pineapple

A direct and reagent free procedure has been developed to monitor the fermentation process of pine apple nectar using Attenuated Total Reflectance Fourier-transform mid-infrared spectrometry (FT-IR) and multivariate analysis. A classical 4² design for standards was employed for calibration using the information in the spectral range from 907 to 1531 cm⁻¹ of the first order derivative spectra after mean centering of infrared data. The root mean square error of calibration (RMSEC) of 0.040, 0.021, 0.063 and 0.074% w/w were obtained for glucose, fructose, saccharose and ethanol, respectively, and a mean relative validation error of 2.9, 2.1, 2.6 and 3.6% was achieved for glucose, fructose, saccharose and ethanol. Results obtained by the proposed procedure for the alcohol content at different fermentation levels were statistically comparable with those obtained by a reference spectrometric method. So, FT-IR spectrometry provides a fast alternative to long and tedious classical procedures to ethanol determination and sugar enzymatic analysis.     

一般工商业零售电价套餐适应性评估与优化方法

在电力体制改革的大背景下,合理评估零售电价套餐适应性,对控制电网经营风险和推进售电侧改革有重要意义。针对我国电力市场以及一般工商业的特点,首先从竞争、用户以及市场环境角度出发建立了一般工商业零售电价套餐评估指标体系;其次将层次分析法和改进的灰色白化权函数相结合,对电价套餐进行适应性评估;最后针对该评估方法建立了基于蚁群算法的优化模型,以最小成本得到提高电价套餐适应性等级的优化方案,并验证了该方法具有良好的鲁棒性,具有一定的参考意义。     

Chromatographic analysis of sugars applied to the characterisation of monofloral honey

The control of the floral quality of honey has become a priority issue as a result of the number of abuses observed and the relative ease of getting around existing control methods. We conducted chromatographic analyses of honey sugars to determine new criteria for authenticating an origin. The work involved creating databases by analysing a large number of authentic honeys from seven monofloral varieties, followed by statistical processing of the results by a principal components analysis. Differences in composition could thus be demonstrated, such as the presence of trisaccharides in fir honey, that provide an additional tool for authenticating unknow commercial honeys.     

The state of conservation of painted surfaces in the presence of accelerated ageing processes monitored by use of FT-Raman spectroscopy and multivariate control charts

A new method has been developed for monitoring the degradation of paintings. Two inorganic pigments (ultramarine blue and red ochre) were blended with linseed oil and spread on canvas. Each canvas was subjected to simulated accelerated ageing in the presence of typical degradation agents (UV radiation and acidic solution). Periodically the painted surfaces were analysed by FT-Raman, to investigate the status of the surface. The data obtained were analysed by principal component analysis (PCA). Finally the Shewhart and cumulative sum control charts based on the relevant principal components (PC) and the so called scores monitoring and residuals tracking (SMART) charts were built. The method based on the use of PC to describe the process was found to enable identification of the presence of relevant modification occurring on the surface of the samples studied.Electronic supplementary material Supplementary material is available for this article at     

Multivariate monitoring of soybean oil ethanolysis by FTIR

In this work, an analytical procedure was developed to monitor the ethanolysis of degummed soybean oil (DSO) using Fourier-transformed mid-infrared spectroscopy (FTIR) and methods of multivariate analysis such as principal component analysis (PCA) and partial least squares regression (PLS). The triglycerides (reagents) and ethyl esters (products) involved in ethanolysis were shown to have similar FTIR spectra. However, when the FTIR spectra derived from seven standard mixtures of triolein and ethyl oleate were treated by PCA at the region that represents the CO stretching vibration of ester groups (1700-1800 cm⁻¹), only two principal components (PC) were shown to capture 99.95% of the total spectral variance (92.37% for the former and 7.58% for the latter PC). This observation supported the development of a multivariate calibration model that was based on the PLS regression of the FTIR data. The prevision capability of this model was measured against 40 reaction aliquots whose ester content was previously determined by size exclusion chromatography. Only small discrepancies were observed when the two experimental data sets were treated by linear regression (R²=0.9837) and these deviations were attributed to the occurrence of non-modeled transient species in the reaction mixture (reaction intermediates), particularly at short reaction times. Therefore, the FTIR/PLS model was shown to be a fast and accurate method to predict reaction yields and to follow the in situ kinetics of soybean oil ethanolysis.     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。