生物漂白过程木素结构的红外光谱分析

利用富里埃变换红外光谱技术分析了尾叶桉硫酸盐浆原浆的残余木素、经生物处理后浆的残余木素及生物处理后纸浆经氢氧化钠抽出的木素。结果证实 ,在漆酶 助剂生物处理纸浆的过程碳水化合物不发生氧化 ,而木素的Cα羟基发生氧化产生α -羰基 ;酶作用后纸浆进一步碱处理导致木素溶出。从溶出木素的结构分析 ,推断纸浆中具有紫丁香基结构的残余木素优先发生降解     

基于近红外分析的基酒质量等级的研究

为实现基酒质量等级的自动化评定,采集63个不同香型的基酒样品,采用傅里叶变换红外光谱仪测试基酒样品的近红外光谱的指纹图谱,应用主成分分析法和支持向量机方法对基酒样品进行质量等级分类。结果表明该方法行之有效,可以有效的区分不同等级的基酒样品,其中特级和优级达到100%分类,一级和二级分别达到94%和89%。该方法为基酒等级的区分提供一种新途径。     

蒜头果木质素提取及成分分析

采用一般碱法、超声波辅助和微波辅助从蒜头果壳和枝中提取木质素,分别测定了蒜头果壳和枝中木质素含量及木质素的提取率,并对所提取木质素进行红外和紫外光谱分析。克拉森木素定量法测得蒜头果壳和枝中木质素质量分数分别为44.41%±0.65%和34.57%±0.52%,在碱浓度0.5 mol/L、碱液量30 mL/g、超声波处理时间1 h、水浴温度40℃的提取工艺条件下,蒜头果壳和枝的木质素提取率分别为45.21%±0.59%和63.78%±0.73%。紫外和红外光谱显示超声波辅助方法提取的木质素保持了木质素原有结构,存在明显的愈疮木基和紫丁香基苯环结构;并通过黏度法比较了3种不同提取方法所得木质素的相对分子质量。     

基于部分DPCA的动态系统故障隔离

Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.     

基于多尺度分析与主元分析的聚丙烯熔融指数神经元软测量预报模型

Prediction of melt index （MI）, the most important parameter in determining the product＇s grade and quality control of polypropylene produced in practical industrial processes, is studied. A novel soft-sensor model with principal component analysis （PCA）, radial basis function （RBF） networks, and multi-scale analysis （MSA） is proposed to infer the MI of manufactured products from real process variables, where PCA is carried out to select the most relevant process features and to eliminate the correlations of the input variables, MSA is introduced to a~quire much more information and to reduce the uncertainty of the system, and RBF networks are used to characterize the nonlinearity of the process. The research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in propylene polymerization processes.     

主成分分析法(PCA)在藻类生长影响分析中的应用

水华的发生、发展是一个相当复杂的过程,众多影响因素共同导致藻类生长、水华爆发。常用的分析工具难以分析、预测实际情况下藻类的生长。该文利用当今世界上最大型的统计与图表分析软件之一STATISTICA对2008年和2009年奥运森林公园主湖的监测数据进行主成分分析。结果表明STATISTICA软件中的主成分分析适用于藻类生长分析,并发现除了已知的藻类生长影响因素(营养盐、温度等)之外,景观湖中底泥的累积效应也应纳入分析之内。     

In-TFT-Array-Process Micro Defect Inspection Using Nonlinear Principal Component Analysis

Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image.     

主成分分析法在卷烟质量评价中的应用

文章以SPSS软件为工具,运用主成分分析法对15个牌号的卷烟质量进行了评价。结果表明：（1）按累积贡献率≥85%的标准,筛选出化学成分因子、感官质量因子、烟气成分因子共3个主成分,其对变异的累积贡献率达94.37%。（2）不同牌号卷烟的综合评价得分差异明显,其中A,B,C三个牌号的卷烟质量高于其他牌号的卷烟,而M,N,O三个牌号的卷烟质量得分则低于其他牌号的卷烟质量得分。     

Application of Principal Component Analysis to the Adsorption of Natural Organic Matter by Modified Activated Carbons

Adsorption of natural organic matter by physically and chemiclally modified activated carbons was studied in this work. Principal component analysis (PCA) was applied to datasets gathering morphological, physical, chemical properties, and adsorption capacity information of the modified activated carbon samples. The PCA was used to identify the main effects by clustering the samples according to the modifications undergone. Results indicate that total and/or fractional pore volume, and the iron and oxygen content were sensitive enough to detect good adsorption potential even for “a priori” nonoptimal overall properties. Unexpectedly, low values of pHpzc not always correlated with poor adsorption performance. The sum of N and O content may also be a good indicator to select a carbon with good adsorption performance. Based on PCA results, a prediction equation was developed to estimate values for the equilibrium uptake capacity and was tested against five additional carbon samples.     

Thermogravimetry‐FTIR Analysis of Pyrolysis of Pyrolytic Lignin Extracted from Bio‐Oil

Pyrolytic lignin is attributed to the instability of bio‐oil but is a potential chemical material. To improve the stability and increase the economic viability of bio‐oil, high‐ and low‐molecular‐mass pyrolytic lignin (HMM and LMM) were obtained using solvent extraction. The microstructure of pyrolytic lignin was examined by Fourier transform infrared spectrometry (FTIR). The dissimilar absorption intensities indicated the different content of corresponding functional groups in HMM and LMM. The pyrolysis behavior of HMM and LMM was studied by thermogravimetry coupled with FTIR. Obviously pyrolytic lignin undergoes three weight loss stages.     

基于主成分分析法的燃料乙醇行业清洁生产水平综合评价

燃料乙醇行业是可再生能源的重要组成部分,但是燃料乙醇行业具有高能耗、高污染等特点,环保压力巨大。文章基于主成分分析法对调研的8家燃料乙醇企业9条生产线进行清洁生产综合评价,找到了不同指标之间的相关性和信息重叠之处,提炼出了不同燃料乙醇企业清洁生产评价中的主要控制指标,得到了主成分表达式及主成分综合评价模型,评价结果与百分制法评价结果基本一致,表明主成分分析法在燃料乙醇清洁生产中的综合评价中是一种客观科学的方法。     

红外光谱结合主成分分析识别不同产地单枞茶

采用傅立叶变换红外(FTIR)光谱法结合主成分分析对梅州三个茶叶产区的单枞茶进行了聚类分析。实验结果表明,通过茶叶的红外光谱,利用主成分分析(PCA)结合马氏距离判据的方法可鉴别上述3个茶叶产区单枞茶。该法快速、简便,为鉴别单枞茶的产区提供了一种新的方法。     

Polyphenols of Pistachio (Pistacia vera L.) Oil Samples and Geographical Differentiation by Principal Component Analysis

The phenolic compounds of Italian and Turkish pistachio oil samples were investigated by gas chromatography–mass spectrometry (GC–MS) with the aim of identifying substances also present in very low concentration; 21 compounds were identified by means of the mass spectra of the trimethylsilyl (TMS) derivatives. Benzoic acid derivatives (protocatechuic acid, gallic acid, 4‐hydroxybenzoic acid) were the main components; 14 polyphenols were detected in pistachios for the first time. The concentrations of the compounds found were in the range 0.02–5.64 μg/g of fresh pistachio. No appreciable qualitative differences were found between samples of different origin; a multivariate approach was successfully tried to differentiate the samples according to their geographical origin, using Principal Component Analysis (PCA). Finally, Canonical Discriminant Analysis (CDA) was further conducted to classify different pistachio samples.     

自适应MPCA方法在链霉素过程监控中的应用

Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.     

常压塔产品质量在线估计的非线性主元分析方法

提出一种新的软测量方法,通过建立过程变量非线性主元得分与产品质量参数之间的三层前向神经网络模型,得到产品质量参数的预测值。实际应用表明,该方法比常规的线性主元分析方法和神经网络方法具有更好的预测性能。     

核主成分分析-广义回归神经网络法研究——70种烷烃类有机化合物的气相色谱保留指数

利用核主成分分析法和广义回归神经网络研究了70种烷烃在固定相为角鲨烷,柱温50℃时的气相色谱保留指数。核主成分分析将70×8维的样品集降为51×3维,以减少计算量;降维后的样品集作为广义回归神经网络的训练集对色谱保留时间进行计算和预测。该方法的计算结果满足误差要求。     

Determination of Sugarcane Bagasse Lignin S/G/H Ratio by Pyrolysis GC/MS

Abstract

In this work, analytical pyrolysis (Py-GC-MS) was employed to identify lignin markers derived from H, S, and G phenylpropanoid units in sugarcane bagasse. Temperatures of 450 and 500°C allowed the detection of key products that were informative on the bagasse lignin composition. The method was validated by comparing the S/G ratio as determined by the nitrobenzene oxidation (NBO) standard method for five sugarcane varieties. The S/G ratio as determined by Py-GC-MS, taking into consideration all known lignin markers in the analysis, resulted in a correlation coefficient of 0.85 with a linear regression coefficient of 0.74. When a group of selected markers (M2) was used, the correlation coefficient between methods was improved to 0.95 and the linear regression coefficient was adjusted to 0.92. M2 markers consisted of five syringyl markers (syringol, 4-methylsyringol, 4-ethylsyringol, 4-vinylsyringol, and trans-4-propenylsyringol) and four guaiacyl markers (guaiacol, 4-vinylguaiacol, 4-methylguaiacol, and vanillin). Importantly, Py-GC-MS allowed for the study of lignin composition in sugarcane bagasse without the need to remove the extractives, minimizing the work with sample preparations.     

基于多尺度动态核主元分析的化工过程故障检测

针对化工过程数据的多尺度性和非线性特性,提出一种多尺度动态核主元分析方法.使用小波变换分析数据的多尺度特性,借助核函数来解决非线性映射问题,同时解决了噪声和干扰造成的各变量数据具有时间序列动态性问题.在此基础上,提出一种基于矩阵相似度量的核函数参数选优方法.将上述方法应用于TE模型的故障检测过程中,仿真结果表明,该方法提高了过程性能监视和故障检测的准确性,优于线性主元分析法的检测效果.     

基于主成分分析法的注水油田开发效果评价

注水开发是各油田采取的主要开发方式之一,注水开发效果影响因素众多。本文主要从青海油田注水开发油田实际出发,建立了包含油藏因素、采油因素等14个方面指标的评价体系,采用专家确定权重的方法与主成分分析法相结合,建立了注水油田开发效果综合评价方法。通过实例验证,该评价方法的评价结果与实际生产状态吻合。本文所建立的注水油田开发效果综合评价方法对指导注水开发油田的生产具有实际意义。     

Hydrothermal Carbonization of Lignin with Vanillin as a Model Component

Hydrothermal carbonization (HTC) is one possibility to concentrate energy of wet biomass in so‐called hydrochar. To design technical HTC reactors in a rational way, the reaction kinetics of the four main components of biomass, i.e., carbohydrates, lignin, fats/oils, and proteins, should be known. To determine the kinetics of hydrochar functionalization, initially an appropriate analytical method must be developed. In the presented experiments, the hydrochar obtained by HTC of vanillin is investigated by diffuse reflectance infrared (IR) Fourier transform spectroscopy (DRIFTS). In order to assign the IR bands in the DRIFT spectra to different functional groups, temperature‐programmed desorption (TPD) experiments are conducted, followed by a combination of DRIFTS and TPD. This TPD/DRIFT combination is applied as an analytical method to investigate the kinetics of hydrochar functionalization during HTC.