基于GC-MS指纹图谱和化学计量学方法鉴别陈皮

制备了陈皮挥发油提取物并建立其气相色谱-质谱（GC-MS）指纹图谱,对不同批次的陈皮指纹图谱进行相似度评价;对其共有成分峰面积数据进行主成分分析（PCA）和层次聚类分析（HCA）。建立了具有16个共有峰的新会陈皮挥发油指纹图谱,通过化学计量学分析方法能将新会陈皮和其他陈皮样品准确区分开来;不同采摘期及贮存时间陈皮的成分呈现规律性特征。方法可用于不同产地、采摘期及贮存时间陈皮的鉴别,并为陈皮的质量控制提供依据。     

Label‐free monitoring of inflammatory tissue conditions using a carrageenan‐induced acute inflammation rat model

Although the confirmation of inflammatory changes within tissues at the onset of various diseases is critical for the early detection of disease and selection of appropriate treatment, most therapies are based on complex and time‐consuming diagnostic procedures. Raman spectroscopy has the ability to provide non‐invasive, real‐time, chemical bonding analysis through the inelastic scattering of photons. In this study, we evaluate the feasibility of Raman spectroscopy as a new, easy, fast, and accurate diagnostic method to support diagnostic decisions. The molecular changes in carrageenan‐induced acute inflammation rat tissues were assessed by Raman spectroscopy. Volumes of 0 (control), 100, 150, and 200 µL of 1% carrageenan were administered to rat hind paws to control the degree of inflammation. The prominent peaks at [1,062, 1,131] cm^?1 and [2,847, 2,881] cm^?1 were selected as characteristic measurements corresponding to the C–C stretching vibrational modes and the symmetric and asymmetric C–H (CH₂) stretching vibrational modes, respectively. Principal component analysis of the inflammatory Raman spectra enabled graphical representation of the degree of inflammation through principal component loading profiles of inflammatory tissues on a two‐dimensional plot. Therefore, Raman spectroscopy with multivariate statistical analysis represents a promising method for detecting biomolecular responses based on different types of inflammatory tissues.     

Regional forecasts of photovoltaic power generation according to different data availability scenarios: a study of four methods

The development of methods to forecast photovoltaic (PV) power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power 1 day ahead of time and to compare their performances. Four forecast methods were regarded, of which two are new ones proposed in this study. Together, they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were performed for 1 year of hourly forecasts using data of 273 PV systems installed in two adjacent regions in Japan, Kanto, and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems' forecasts and the one based on stratified sampling provided the best results. In this case, the best annual normalized root mean square error (RMSE) and mean absolute error (MAE) were 0.25 and 0.15 kWh/kWh_avg, respectively. For Kanto, with homogeneous weather conditions, the four methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWh_avg for the normalized RMSE and 0.202 kWh/kWh_avg for the normalized MAE. Copyright © 2014 John Wiley & Sons, Ltd.     

Lurgi MTP工艺循环烃组分分布研究

采用气相色谱仪对德国Lurgi MTP工艺的C4循环烃进行分析,并从MTP催化剂运行周期方向考察了循环烃的组分变化规律。分析结果表明,C4循环烃主要组分是烷烃,约占其总物质的量的57%,其余组分为烯烃和少量的甲醇、二甲醚;其中,各组分含量大小为:异丁烷异丁烯正丁烷1-丁烯反-2-丁烯。     

Mixture semisupervised principal component regression model and soft sensor application

Traditionally, data‐based soft sensors are constructed upon the labeled historical dataset which contains equal numbers of input and output data samples. While it is easy to obtain input variables such as temperature, pressure, and flow rate in the chemical process, the output variables, which correspond to quality/key property variables, are much more difficult to obtain. Therefore, we may only have a small number of output data samples, and have much more input data samples. In this article, a mixture form of the semisupervised probabilistic principal component regression model is proposed for soft sensor application, which can efficiently incorporate the unlabeled data information from different operation modes. Compared to the total supervised method, both modeling efficiency and soft sensing performance are improved with the inclusion of additional unlabeled data samples. Two case studies are provided to evaluate the feasibility and efficiency of the new method. © 2013 American Institute of Chemical Engineers AIChE J 60: 533–545, 2014     

一种基于可预测偏最小二乘法的故障检测方法

将可预测元分析(Fore CA)与偏最小二乘法(PLS)结合用于故障检测,在选取合适的可预测元的基础上,运用偏最小二乘回归,进一步提高模型对系统的预测能力,克服了偏最小二乘回归方法无法反映系统动态时序特性的缺陷,并构造CUSUM统计量和SPE统计量以检测故障是否发生。最后通过TE模型上的仿真实验结果表明:Fore PLS方法能有效检测慢漂移等故障。     

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。     

双组分聚氨酯人造草背胶的研制

介绍了一种双组分聚氨酯人造草背胶的制备方法,以聚醚多元醇、异氰酸酯为主体原料,3,3'-二氯-4,4'-二氨基二苯基甲烷(MOCA)为扩链剂,研究了不同聚醚多元醇质量比、扩链剂用量、异氰酸酯质量比、异氰酸酯指数对人造草背胶操作时间、脱粘时间及力学性能的影响。结果表明,当异氰酸根指数为0.99,A组分中聚醚多元醇MN-3050与DL-1000质量份数分别为80、20,A组分中扩链剂MOCA用量为4份,B组分中MDI-50与PM-200质量比为1∶1,当A、B组分按质量比2∶1混合时,操作时间及脱粘时间最佳,人造草背胶的综合力学性能达到最好。     

Statistical Validation of Reproducibility of HPLC Peptide Mapping for the Identity of an Investigational Drug Compound Based on Principal Component Analysis

Peptide mapping is a key analytical method for studying the primary structure of proteins. The sensitivity of the peptide map to even the smallest change in the covalent structure of the protein makes it a valuable “fingerprint” for identity testing and process monitoring. We recently conducted a full method validation study of an optimized reverse-phase high-performance liquid chromatography (RP-HPLC) tryptic map of a therapeutic anti-CD4 monoclonal antibody. We have used this method routinely for over a year to test production lots for clinical trials and to support bioprocess development. One of the difficulties in the validation of the peptide mapping method is the lack of proper quantitative measures of its reproducibility. A reproducibility study may include method and system precision study, ruggedness study, and robustness study. In this paper, we discuss the use of principal component analysis (PCA) to quantitate peptide maps properly using its projected scores on the reduced dimensions. This approach allowed us not only to summarize the reproducibility study properly, but also to use the method as a diagnostic tool to investigate any troubles in the reproducibility validation process.