无需待测化合物标准样品便可测定校正因子

根据色谱峰面积、校正因子和待测组分含量的关系,应用Ｌｅｖｅｎｂｅｒｇ－Ｍａｒｑｕａｒｄｔ方法对气相色谱分析数据进行最优化计算,可在缺乏待测组分标准样品的条件下测定相应的校正因子。分别测定了甲苯－氯苯、二乙氧基甲烷－乙酸乙酯和二乙氧基甲烷－乙醇－水等３个体系中各组分的相对校正因子。结果表明：化合物校正因子的测定值与相应文献值的相对偏差小于３％。根据实验结果进一步讨论了方法的适用范围以及进行测定所需样品的数目。     

碱性在Pt／L分子筛催化剂中的作用

用ＣＯ２－ＴＰＤ，ＴＥＭ，ＰＡＳＣＡ等表征手段，考察了Ｐｔ／Ｌ分子筛催化剂上碱性对金属分散度、活性中心的影响及其内在联系，首次对Ｌ分子筛上碱性位的分布进行了讨论。结果发现，碱性的存在可以改善金属铂的分散度；高分散的金属铂是位于分子筛中电子给予体上，即碱性位上；载体与金属之间的相互作用使得金属铂上富集电子，烷烃氢解受到抑制，从而一步实现了双功能催化剂上进行二组分，三组分，乃至四组分金属调变以改善催化     

偏最小二乘（PLS）分光光度法同时测定镉,汞,铅

本文研究了溴化十六甲铵基（ＣＴＭＡＢ）存在时，ｍｅｓｏ－四（４－磺酸苯基）卟啉（ＴＰＰＳ４）与Ｃｄ（ＩＩ），Ｈｇ（ＩＩ），Ｐｂ（ＩＩ）在室温下发生高灵敏度的显色反应条件以及应用偏最小二乘（ＰＬＳ）法同时测定三组分的方法。由微型计算机处理实验数据可心到三组分的浓度，本文还定义了未知样品与标准样之间的相关系数Ｑｒ，通过Ｑｒ值的大小预报未知样测试结果的准确性，利用此法可以提高测定速度，可靠性和选择性。     

iPS—b—iPP共聚物对iPS／iPP共混物的性能和形态影响的研究

报道了苯乙烯－丙烯等规嵌段共聚物增溶作用及ｉＰＳ－ｂ－ｉＰＰ／ｉＰＳ／ｉＰＰ三组分共混体系微观形态和力学性能的研究结果。ｉＰＳ－ｂ－ｉＰＰ的加入明显地改善了ｉＰＳ／ｉＰＰ二组分共混物的力学性能；共聚物含量超过１５％时，三组分共混物的抗冲击强度超过ＨＩＰＳ的抗冲击强度，并具有较高的耐热性。ＳＥＭ结果表明，ｉＰＳ－ｂ－ｉＰＰ在ｉＰＳ／ｉＰＰ共混中起到了相分散和相间“偶联”作用，并降低了共混体系的微相尺     

制备方法对MoO3/ZrO2结构的影响及MoO3/ZrO2固体超强酸的结构特征Ⅰ.ZrO2的形态与样品?

ＬＲＳ，ＸＲＤ，ＸＰＳ结果表明，仲钼酸铵浸渍仅经干燥的Ｚｒ（ＯＨ）４，再焙烧制得的固体超强酸（ＭｏＯ３／ＺｒＯ２（Ｉ））与浸渍晶态ＺｒＯ２制得的部分氧化催化剂（ＭｏＯ３／ＺｒＯ２（Ⅱ）中，活性组分的存在形式明显不同，在ＭｏＯ３／ＺｒＯ２（Ⅱ）中，ＭｏＯ３以二维聚钼酸根和Ｍｏ－Ｏ－Ｚｒ表面物种两种表面态存在于介稳的四方ＺｒＯ２上，后者在ＬＲＳ谱中表现为～８１４ｃｍ＾－１的宽峰，ＭｏＯ３含量超过一定值     

二组分气体在固体上吸附的研究（Ⅲ）：丙酮—正己烷,甲苯—正己…

测定了丙酮－正己烷，甲苯－正己烷，苯－正己烷，正戊烷－正己烷４个二组分气体在硅烷化硅胶上的吸附等温线，并测定了单组分体吸附等温线，实验结果表明，二组分气体在硅烷化硅胶上的竞争吸附的强弱可以通过其且分气体在硅烷化硅胶上第一层吸附热Ｑ１数值的大小预测。     

甲醇制汽油反应及动力学研究

采用等温流动积分反应器，在下列条件范围内：反应温度３６０℃～３９０℃，压力０．１ＭＰａ～４．０ＭＰａ，重量空速５．０ｈ￣－１～１１．Ｏｈ￣－１、对甲酵在ＺＳＭ－５分子筛催化剂上转化制汽油反应进行了研究。根据实验结果，对Ｍｔｈａｉｌ￣［１］提出的甲醇转化成烃类机理做了某些修正。建立了甲醇在ＺＳＭ－５分子筛催化剂上转化成汽油的动力学模型，该模型是一个包括２６个方程的常微分方程组。用Ｇｅａｒ法求解此方程组，用Ｐｏｗｅｌｌ法和Ｍａｒｑｔｉａｒｄｔ法相结合的方法对实验数据进行了回归，求出了每个反应的活化能和指前因子。用此模型预测反应结果，计算值与实验值相符合较好。     

制备方法对MoO3/ZrO2结构的影响及MoO3/ZrO2固体超强酸的结构特征Ⅱ.活性组分存在形态?

ＬＲＳ，ＸＲＤ，ＸＰＳ结果表明，仲钼酸铵浸渍仅经干燥的Ｚｒ（ＯＨ）４，再焙烧制得的固体超强酸（ＭｏＯ３／ＺｒＯ２（Ｉ））与浸渍晶态ＺｒＯ２制得的部分氧化催化剂（ＭｏＯ３／ＺｒＯ２（Ⅱ）中，活性组分的存在形式明显不同，在ＭｏＯ３／ＺｒＯ２（Ⅱ）中，ＭｏＯ３以二维聚钼酸根和Ｍｏ－Ｏ－Ｚｒ表面物种两种表面态存在于介稳的四方ＺｒＯ２上，后者在ＬＲＳ谱中表现为～８１４ｃｍ＾－１的宽峰，ＭｏＯ３含量超过一定值     

植物油的质谱分析：1.蓖麻油

运用一种植物油的系统分析方法测定蓖麻油的精细组分，这包括皂化，ＢＦ３－ＭｅＯＨ甲基化，ＧＣ－ＣＩＭＳ法测定脂肪酸碳链长度和不饱和度，ＡＭＰ衍生化，ＧＣ－ＥＩＭＳ测定不饱和脂肪酸的双链位置，分析了蓖麻油的十一种脂肪酸组分，有五个为未知组分，并测定了其中三个组分的双链位置。     

Cr-AlN界面的二次离子质谱研究

采用电子束蒸发的方法在２００℃抛光的氮化铝（ＡｌＮ）陶瓷衬底上淀积２００ｎｍ的Ｃｒ膜，并在高真空中退火。利用ＭＣｓ＋－ＳＩＭＳ技术（在Ｃｓ＋一次离子轰击下检测ＭＣｓ＋型二次离子）对样品进行了深度剖析，给出了界面组分分布随退火温度与时间的变化关系。结果表明，ＭＣｓ＋－ＳＩＭＳ技术是研究金属－陶瓷界面扩散与反应的有效方法。     

Robust regression techniques A useful alternative for the detection of outlier data in chemical analysis

The validation of an analytical procedure means the evaluation of some performance criteria such as accuracy, sensitivity, linear range, capability of detection, selectivity, calibration curve, etc. This implies the use of different statistical methodologies, some of them related with statistical regression techniques, which may be robust or not. The presence of outlier data has a significant effect on the determination of sensitivity, linear range or capability of detection amongst others, when these figures of merit are evaluated with non-robust methodologies.In this paper some of the robust methods used for calibration in analytical chemistry are reviewed: the Huber M-estimator; the Andrews, Tukey and Welsh GM-estimators; the fuzzy estimators; the constrained M-estimators, CM; the least trimmed squares, LTS. The paper also shows that the mathematical properties of the least median squares (LMS) regression can be of great interest in the detection of outlier data in chemical analysis. A comparative analysis is made of the results obtained by applying these regression methods to synthetic and real data. There is also a review of some applications where this robust regression works in a suitable and simple way that proves very useful to secure an objective detection of outliers. The use of a robust regression is recommended in ISO 5725-5.     

稳健灰色模型在色谱保留值研究中的应用

本文提出了具有稳健性的ＧＭ（１，１）灰色模型，并用此模型对８种多环芳烃化合物容量因子与流动相组成间的关系作了研究，建立了其间的稳数学模型，结果表明，稳健ＧＭ（１，１）模型比常规ＧＭ（１，１）模型具有更好的抗干扰性能和受异常点影响小的优点，是一个值得推广的好方法。既丰富了灰色系统理论，又拓宽了灰色理论在分析化学中的应用范围。     

Partial least squares and random sample consensus in outlier detection

A novel outlier detection method in partial least squares based on random sample consensus is proposed. The proposed algorithm repeatedly generates partial least squares solutions estimated from random samples and then tests each solution for the support from the complete dataset for consistency. A comparative study of the proposed method and leave-one-out cross validation in outlier detection on simulated data and near-infrared data of pharmaceutical tablets is presented. In addition, a comparison between the proposed method and PLS, RSIMPLS, PRM is provided. The obtained results demonstrate that the proposed method is highly efficient.     

An enhanced Monte Carlo outlier detection method

Outlier detection is crucial in building a highly predictive model. In this study, we proposed an enhanced Monte Carlo outlier detection method by establishing cross‐prediction models based on determinate normal samples and analyzing the distribution of prediction errors individually for dubious samples. One simulated and three real datasets were used to illustrate and validate the performance of our method, and the results indicated that this method outperformed Monte Carlo outlier detection in outlier diagnosis. After these outliers were removed, the value of validation by Kovats retention indices and the root mean square error of prediction decreased from 3.195 to 1.655, and the average cross‐validation prediction error decreased from 2.0341 to 1.2780. This method helps establish a good model by eliminating outliers. © 2015 Wiley Periodicals, Inc.     

High-throughput screening of argan oil composition and authenticity using benchtop 1H NMR

We use 60-MHz benchtop nuclear magnetic resonance (NMR) to acquire ¹H spectra from argan oils of assured origin. We show that the low-field NMR spectrum of neat oil contains sufficient information to make estimates of compositional parameters and to inform on the presence of minor compounds. A screening method for quality and authenticity is presented based on nearest-neighbour outlier detection. A variety of oil types are used to challenge the method. In a survey of retail-purchased oils, several instances of fraud were found.     

Robust estimates of the theoretical standard deviation to be used in interlaboratory precision experiments

Interlaboratory experiments often contain results that strongly deviate from other results obtained in the same laboratory under repeatability conditions, or laboratory means that strongly deviate from other laboratory means. In ISO 5725-2 [1] and IUPAC [2], the basic standards for the organisation and analysis of interlaboratory experiments for the determination of precision of a measurement method, outlier tests are performed in order to detect such situations and to finally decide whether these values are retained in the analysis or discarded as outliers. This outlier treatment, which always has a subjective aspect, becomes unnecessary by using robust estimates of the repeatability and reproducibility standard deviation. This paper proposes to use Rousseeuw’s Q _n as an extremely robust and efficient estimate of the standard deviation. Two examples show the performance of the new method.     

Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration

A principal component regression (PCR) model is built for prediction of total antioxidant capacity in green tea using near-infrared (NIR) spectroscopy. The modelling procedures are systematically studied with the focus on outlier detection. Different outlier detection methods are used and compared. The root mean square error of prediction (RMSEP) of the final model is comparable to the precision of the reference method.     

Use of principal components analysis for mutation detection with two-dimensional electrophoresis protein separations.

The application of two-dimensional electrophoresis (2-DE) to mutation detection requires the capability to monitor each protein in a 2-DE pattern for significant changes in abundance indicative of a mutation event. Previously, mutation searches were done using a univariate outlier detection method in which each protein spot was considered independently in a classical outlier search. An alternative approach to analysis of 2-DE patterns for quantitative changes is a multivariate procedure which takes advantage of the observation that protein spots in a 2-DE pattern often represent correlated rather than independent measurements. We have compared the efficiency of univariate and multivariate procedures for mutation detection using data from the Argonne National Laboratory 2-DE database of mouse liver proteins. Analyses involving a total of over 1500 gels were performed to compare the performance of a multivariate method based on principal components analysis (PCA) with the univariate method. Up to 279 spots from each pattern were used for PCA. First, a simulation was performed to assess the detection efficiency of PCA for single protein spots decreased in abundance by 50%. Then, the ability to detect actual mutations was tested using eight confirmed mutations. Results show that, compared to a univariate approach to analysis of data from the mouse model system, the multivariate method increases the number of protein spots on each 2-DE pattern that can be monitored for quantitative changes indicative of mutations by compensating for variables that contribute to the background quantitative variability of protein spots.     

A conformational analysis method for understanding the energy landscapes of clusters.

A newly developed unbiased structural optimization method, named dynamic lattice searching (DLS), is proposed as an approach for conformational analysis of atomic/molecular clusters and used in understanding the energy landscape of large clusters. The structures of clusters are described in terms of the number of basic tetrahedron (BT) units they contain. We found that the hit numbers of different structural motifs in DLS runs is proportional to the number of BTs. A parameter T(max) is defined to limit the maximal number of atoms moved in a structural transition. Results show that T(max) is a key parameter for modulating the efficiency of the DLS method and has a great influence on the hit number of different motifs in DLS runs. Finally, the effect of potential range on the conformational distribution of the (Morse)(98) cluster is also discussed with different potential-range parameters.