Qualitative and quantitative analysis of pyrolysis oil by gas chromatography with flame ionization detection and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry

Pyrolysis oils have attracted a lot of interest, as they are liquid energy carriers and general sources of chemicals. In this work, gas chromatography with flame ionization detector (GC-FID) and two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS) techniques were used to provide both qualitative and quantitative results of the analysis of three different pyrolysis oils. The chromatographic methods and parameters were optimized and solvent choice and separation restrictions are discussed. Pyrolysis oil samples were diluted in suitable organic solvent and were analyzed by GC×GC-TOFMS. An average of 300 compounds were detected and identified in all three samples using the ChromaToF (Leco) software. The deconvoluted spectra were compared with the NIST software library for correct matching. Group type classification was performed by use of the ChromaToF software. The quantification of 11 selected compounds was performed by means of a multiple-point external calibration curve. Afterwards, the pyrolysis oils were extracted with water, and the aqueous phase was analyzed both by GC-FID and, after proper change of solvent, by GC×GC-TOFMS. As previously, the selected compounds were quantified by both techniques, by means of multiple point external calibration curves. The parameters of the calibration curves were calculated by weighted linear regression analysis. The limit of detection, limit of quantitation and linearity range for each standard compound with each method are presented. The potency of GC×GC-TOFMS for an efficient mapping of the pyrolysis oil is undisputable, and the possibility of using it for quantification as well has been demonstrated. On the other hand, the GC-FID analysis provides reliable results that allow for a rapid screening of the pyrolysis oil. To the best of our knowledge, very few papers have been reported with quantification attempts on pyrolysis oil samples using GC×GC-TOFMS most of which make use of the internal standard method. This work provides the ground for further analysis of pyrolysis oils of diverse sources for a rational design of both their production and utilization process.     

同时测定合成样中铜钴镍钒含量的分光光度法--主成分回归法和偏最小二乘法比较

研究了主成分回归和偏最小二乘方法在多组分分光光度法分析中的应用,以５－Ｂｒ－ＰＡＤＡＰ（２〔５－溴－２－吡啶）－偶氮〕５－二乙氨基苯酚）为显色剂,ＯＰ（聚乙二醇辛基苯基醚）作为增溶增稳剂,在ｐＨ＝３．６的条件下,用主成分回归及偏最小二乘分光光度法同时测定了合成样中的铜、钴、镍、钒４组分含量,测定相对误差在－６．００％～４．００％之间。实验证明,对于加和性不好的体系偏最小二乘分光光度法要优于主成分回     

正相薄层色谱中常见人参皂甙结构与保留值之间关系的初步研究(英文)

 在硅胶薄层板上用氯仿-乙酸乙酯-甲醇-水(体积比为15∶40∶22∶10)作展开剂,测定了常见人参皂甙的Rf值。为了研究它们的结构与保留值之间的关系,对它们的17种结构参数进行了计算。除了拓扑指数和理化参数外,引入了代表构成人参皂甙的母体化合物种类并反映它们分子极性特征的新参数“E”。通过相关分析优化选出范德华分子表面积AW、拓扑指数0B和参数E,建立了多参数线性回归方程,较好地描述了在正相薄层色谱中常见人参皂甙结构与保留值之间的关系,并与人工神经网络方法进行了比较。     

投影边缘在硝基苯类化合物构效关系研究中的应用

在三维投影的基础上,对投影的边缘进行了描述,并以其进行了定量结构活性相关研究.实验结果表明,与Am指数、指示变量和量化参数相结合,可使其数学模型得到明显提高.通过人工神经网络对硝基苯类化合物的毒性进行预测,结果令人满意.     

Partial Least Squares Processing of Near‐Infrared Spectra for Discrimination and Quantification of Adulterated Olive Oils

Abstract

A new processing based on partial least squares (PLS) algorithm for the discrimination and determination of adulterants in pure olive oil using near‐infrared (NIR) spectroscopy has been introduced. The 280 adulterations of olive oil with corn oil (n=70), hazelnut oil (n=70), soya oil (n=70), and sunflower oil (n=70) were prepared, and their NIR spectra in the region 12,000–4550 cm^?1 were collected. The 70 spectra of each adulteration of olive oil were divided into two sets, 50 spectra for a calibration set and 20 spectra for a prediction set. The spectra of a total calibration set (n=200) were separated into individual adulterant calibration sets (n_i=50, i=corn, hazelnut, soya, sunflower) by using discriminant PLS (DPLS) analysis, and PLS calibration models for the quantification of adulterants with corn oil, hazelnut oil, soya oil, or sunflower oil were developed separately. A variety of wavelength ranges and data pretreatments were examined for obtaining optimal results for the discrimination and quantification objects. Four PLS models for differentiating the adulterant types were evaluated by classifying the NIR spectra of a total prediction set (n=80) into known adulterant types. Then, these known adulterant spectra were analyzed by the PLS calibration models developed for each type to determine the content of an adulterant in pure olive oil. The results of evaluation revealed that the processing reported in this article works excellently for the discrimination and quantification of the adulterations of olive oil.     

Robust regression and outlier detection in the evaluation of robustness tests with different experimental designs

Robustness tests are usually based on an experimental design approach. As designed experiments generally lead to a large variability among the results, erroneous results are often not readily detected. As a consequence, the ordinary least squares (OLS) estimates of the effects of the robustness test can be biased. Here, two robustness tests are studied, which both contain a suspicious result. Moreover, simulated datasets are considered to examine the influence of the extent of the outlier as well as the influence of multiple outliers. On the one hand, different methods are applied to inspect the results of the experiments for outliers: the half-normal plot of the OLS residuals, the normal probability plot of the effects and a method, which is based on experimental design reconstruction. On the other hand, two robust regression methods are applied to calculate the effects with a minimum influence of possible outliers. The different methods are compared and it is evaluated under which circumstances they can be applied.     

Assessment of bitter taste of pharmaceuticals with multisensor system employing 3 way PLS regression

The application of the potentiometric multisensor system (electronic tongue, ET) for quantification of the bitter taste of structurally diverse active pharmaceutical ingredients (API) is reported. The measurements were performed using a set of bitter substances that had been assessed by a professional human sensory panel and the in vivo rat brief access taste aversion (BATA) model to produce bitterness intensity scores for each substance at different concentrations. The set consisted of eight substances, both inorganic and organic – azelastine, caffeine, chlorhexidine, potassium nitrate, naratriptan, paracetamol, quinine, and sumatriptan. With the aim of enhancing the response of the sensors to the studied APIs, measurements were carried out at different pH levels ranging from 2 to 10, thus promoting ionization of the compounds. This experiment yielded a 3 way data array (samples × sensors × pH levels) from which 3wayPLS regression models were constructed with both human panel and rat model reference data. These models revealed that artificial assessment of bitter taste with ET in the chosen set of API's is possible with average relative errors of 16% in terms of human panel bitterness score and 25% in terms of inhibition values from in vivo rat model data. Furthermore, these 3wayPLS models were applied for prediction of the bitterness in blind test samples of a further set of API's. The results of the prediction were compared with the inhibition values obtained from the in vivo rat model.     

红外光声光谱技术结合稳健回归用于油菜籽定量分析

将稳健偏最小二乘回归和偏稳健M回归应用于油菜籽红外光声光谱分析中含氮量、含油量与硫苷含量的测定。结果表明,利用红外光声光谱技术可以实现油菜籽品质的快速测定。同时,稳健回归模型较经典偏最小二乘回归模型可以更好地规避异常值影响,进而优化定量分析结果;其中,稳健偏M回归稍优于稳健偏最小二乘回归。     

基于ISC-SVR方法预测Th细胞表位

外源性抗原蛋白被抗原提呈细胞(APC)摄取送入溶酶体中被降解为长度不一的肽段. 在HLA-DM分子辅助下, MHC II类分子相关恒定链多肽(class II-associated invariant chain peptide, CLIP)从MHC II类分子的肽结合槽解离, 使得外源性抗原降解的肽段进入MHC II类分子空的肽结合槽中, 形成稳定的抗原肽-MHC II类分子复合物. 之后再被提呈到APC细胞表面供CD4⁺Th细胞的TCR识别, 激活Th细胞分泌细胞因子, 促进CTL细胞特异性杀伤靶细胞或辅助B细胞产生抗体. Th细胞的活化对机体的细胞免疫和体液免疫功能都有重要的辅助作用. 本工作基于迭代自洽策略与支持向量回归机(ISC-SVR)方法建立了MHC II类分子与外源性抗原肽结合亲合力预测模型, 采用13mer扩展核心结合序列可以提高预测模型的性能, 分别按照均值法、最大值法、结合法、加权平均值法4种方法计算MHC II类分子与抗原肽的结合亲合力值. 对17种MHC II类分子配型进行了回归分析, 与其他预测模型相比较, 本工作模型都得到了最佳AUC值. 最后, 以HLA DRB1*0101分子为例, 分析讨论了MHC II类分子与抗原肽的结合特异性.     

Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression

We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature.