Effect of radiation absorption modes on ignition time of translucent polymers subjected to time-dependent heat flux

Majority of previous solid ignition models, including numerical and analytical ones, considered only surface absorption of incident heat flux for simplification. However, the influence of in-depth absorption on pyrolysis and subsequent ignition cannot be ignored for infrared translucent polymers. This work addresses this problem and focuses on time-dependent heat flux to establish an analytical model for ignition behaviors prediction by means of theoretical analysis. Ignition temperature was utilized as the ignition criterion, and both surface and in-depth absorption scenarios were considered. Thermally thick polymethyl methacrylate and polyamide 6 were selected as reference materials to verify the reliability and applicability of the proposed model by comparing the analysis results with experimental data as well as numerical simulations. A method for determining the approximation parameters of the theoretical analysis was presented to derive the relationship between ignition time and the coefficients in heat flux expressions. The results show that the higher surface temperature owing to surface absorption accelerates the pyrolysis rate and results in a shorter ignition time, while in-depth absorption affects the ignition time inversely. The effect of surface heat loss was also evaluated quantitatively through both analytical and numerical models. The uncertainty of the proposed model is mainly caused by the selection of the approximation parameters. Nevertheless, it provides an alternative approach to estimate the ignition time of translucent polymers besides numerical simulation.

     

自适应小波算法用于近红外光谱的多元校正

实现了一种构建自适应小波滤波器的方法,并将其用于近红外光谱数据的多元校正。该方法根据一定的目标函数,针对信号的特性自适应地构造小波滤波器。用该法构建的滤波器对烟草样品的近红外光谱进行压缩,并将压缩后的数据采用偏最小二乘法建模,实现了烟草样品常规组分的定量分析。     

Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel

This paper evaluates analytical methods based on near infrared (NIR) and middle infrared (MIR) spectroscopy and multivariate calibration to monitor the stability of biodiesel. There was a focus on three parameters: oxidative stability index, acid number and water content. Ethylic and methylic biodiesel from different feedstocks were used in experiments of accelerated aging, in order to take into account the wide variety of oilseeds and feedstocks available in Brazil. Partial least squares (PLS) and multiple linear regression (MLR) models were developed. Different pre-processing techniques and spectral variable/regions selection algorithms were evaluated. For MLR models, the successive projection algorithm (SPA) was employed. Interval PLS (iPLS) and selection of variables taking into account the significant regression coefficients were used for PLS models. Results showed that both near and middle infrared regions, and all variable selection methods tested were efficient for predicting these three important quality parameters of B100, the root mean squares error of prediction (RMSEP) values being comparable to the reproducibility of the corresponding standard method for each property investigated.     

Estimation of the standard uncertainty of a calibration curve: application to sulfur mass concentration determination in fuels

The construction of a calibration curve using least square linear regression is common in many analytical measurements, and it comprises an important uncertainty component of the whole analytical procedure uncertainty. In the present work, various methodologies are applied concerning the estimation of the standard uncertainty of a calibration curve used for the determination of sulfur mass concentration in fuels. The methodologies applied include the GUM uncertainty framework, the Kragten numerical method, the Monte Carlo method (MCM) as well as the approximate equation calculating the standard error of prediction. The standard uncertainty results obtained by all methodologies agree well (0.172?C0.175?ng???L^?1). Aspects of inappropriate use of the approximate equation of the standard error of prediction, which leads to overestimation or underestimation of calculated uncertainty, are discussed. Moreover, the importance of the correlation between calibration curve parameters (slope and intercept) within GUM, MCM and Kragten approaches is examined.     

近五年我国近红外光谱分析技术研究与应用进展

评述了近五年来(2014～2018)我国近红外光谱分析技术的研究与应用进展,内容涉及方法研究、软硬件研发、应用特点和趋势等方面,并对今后我国近红外光谱技术的发展方向进行了展望。引用文献97篇。     

Multivariate near infrared spectroscopy models for predicting methanol and water content in biodiesel

The transesterification of vegetable oils, animal fats or waste oils with an alcohol (such as methanol) in the presence of a homogeneous catalyst (sodium hydroxide or methoxyde) is commonly used to produce biodiesel. The quality control of the final product is an important issue and near infrared (NIR) spectroscopy recently appears as an appealing alternative to the conventional analytical methods. The use of NIR spectroscopy for this purpose first involves the development of calibration models to relate the near infrared spectrum of biodiesel with the analytical data. The type of pre-processing technique applied to the data prior to the development of calibration may greatly influence the performance of the model. This work analyses the effect of some commonly used pre-processing techniques applied prior to partial least squares (PLS) and principal components regressions (PCR) in the quality of the calibration models developed to relate the near infrared spectrum of biodiesel and its content of methanol and water. The results confirm the importance of testing various pre-processing techniques. For the water content, the smaller validation and prediction errors were obtained by a combination of a second order Savitsky-Golay derivative followed by mean centring prior to PLS and PCR, whereas for methanol calibration the best results were obtained with a first order Savitsky-Golay derivative plus mean centring followed by the orthogonal signal correction.     

近红外光谱法同时测定通天口服液中天麻素和芍药苷的含量

本文应用近红外光谱结合偏最小二乘法建立了同时测定通天口服液中天麻素与芍药苷含量的方法。以高效液相色谱(HPLC)法测定通天口服液样品中天麻素和芍药苷的化学参考值,随机抽取60个样本作校正集,20个样本作预测集。用偏最小二乘法(PLS)将校正集样本的近红外光谱与相应样本的天麻素和芍药苷含量分别相关联建立模型。结果表明,天麻素和芍药苷校正模型的决定系数分别为96.28%、94.55%,模型的交叉验证均方差分别为0.0336、0.00908,预测集的决定系数分别为94.23%、92.86%,预测集均方差分别为0.0453、0.00839。同时还做了模型的精密度实验,该方法能用于大批量样品的快速分析。     

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.     

Ensemble wavelet modelling for determination of wheat and gasoline properties by near and middle infrared spectroscopy

The wavelet transform has been shown to be a useful tool for multivariate calibration. However, the choice of wavelet transform settings (wavelet family, length and number of decomposition levels) for a given application is still an open problem. The present paper proposes an alternative approach, which consists of generating an ensemble model by combining individual models obtained with different wavelet transform settings. The advantages of the proposed method are demonstrated in two analytical problems, namely the determination of moisture and protein in wheat by near infrared spectroscopy and the determination of specific mass and three distillation temperatures (T10, T50, T90) in gasoline by middle infrared spectroscopy. In these problems, the results varied considerably among individual models, which underlines the risk associated to an inadequate choice of wavelet transform settings. In contrast, the ensemble model always provided adequate results in terms of prediction error and noise sensitivity. The proposed method can be seen as an advantageous alternative for multivariate calibration in the wavelet domain, as it frees the analyst from the need to choose a particular configuration for the wavelet transform.     

有指导的正交投影技术结合斜率/截距校正法实现小试水分近红外定量模型向中试传递

针对小试制剂过程建立的近红外定量模型难以直接应用于中试或大生产过程中的问题,该文以小试和中试条件下多批次药用糊精流化床制粒过程为载体,在线采集其近红外光谱数据并测定水分含量,建立小试过程水分近红外定量模型,提出并应用有指导的正交投影技术结合斜率/截距校正的模型传递方法跨尺度预测中试样本,使中试两个测试集A和B的水分相对预测误差分别由51.04%和26.64%降至4.90%和3.99%,显著提高了模型预测的准确度。将该结果与无指导的正交投影技术结合斜率/截距校正法以及模型更新相比较,该方法能更加有效地去除待测样本光谱中的干扰信息,适用范围广,为小试建立的模型放大应用到中试甚至大生产过程提供了新方案。     

Correction function on biased results due to matrix effects: Application to the routine analysis of pesticide residues

A new strategy to carry out the correction of analytical results affected by systematic errors due to the matrix effect is proposed. Two types of external calibrations must be established with the purpose to estimate the matrix effect: solvent calibration (SC) and matrix-matched calibration (MC). These calibration curves are statistically compared and a correction function (CF) is proposed with the aim to simplify the resolution to the problems associated with the incidence of matrix systematic error in the analytical results. Applying this correction function to the results obtained from the solvent calibration, it is possible to make a prediction of the values that would be obtained when the matrix-matched calibration is applied. On the other hand, a rigorous study of the associated uncertainty is developed and applied to the calculated correction function. Finally, this correction function is validated by means of obtained data of recovery studies carried out by a traditional methodology. The methodology has been satisfactorily applied to the quantification of the pesticide procymidone by HPLC for assessing dermal exposure.     

A discriminant method for classification of Moroccan olive varieties by using direct FT-IR analysis of the mesocarp section

Four cultivars of olives picked up in the Moroccan region of Beni Mellal were subjected to a characterization and classification study. Analytical data were collected by Fourier transform infrared spectroscopy (FTIR), applied on the mesocarp of the fresh olives without any preliminary treatment. The spectral data were pre-treated by derivative elaboration based on the Savitzky-Golay algorithm to reduce noise and increase analytical information. Partial least squares discriminant analysis (PLS-DA) was performed to elaborate the measurement data and assess the discriminant features of the four cultivars. The PLS model was optimized by applying the Martens’ uncertainty test which provided to select the vibrational frequencies giving the most useful information. The optimized model resulted able to separate the four classes and classify new objects into the appropriate defined classes with a percentage prediction of 97%. The proposed method represents a real novelty to classify olives of different varieties by means of a rapid, inexpensive and reliable procedure.     

Development and validation of a near infrared method for the analytical control of a pharmaceutical preparation in three steps of the manufacturing process

A near infrared diffuse reflectance spectroscopy (NIRS) procedure for the quantitative control analysis of the active compound (otilonium bromide) in a pharmaceutical preparation in three steps of the production process (blended product, cores and coated tablets) and a methodology for its validation are proposed. The analytical procedure is composed by two consecutive steps. First, the sample is identified by comparing its spectrum with a second derivative spectral library. If the sample is positively identified, the active compound is quantified by using a previously established partial least squares (PLS) calibration model. The procedure was validated by studying repeatability, intermediate precision, accuracy and linearity. To this end, an adaptation of ICH (International Conference on Harmonisation) validation methodology to an NIR multivariate calibration procedure is proposed. The relative standard error of prediction (RSEP) was < or = 1% and the suitability of the procedure for control analysis was confirmed by the results obtained analysing new production samples produced over a three-month period.     

Development and validation of a near infrared method for the analytical control of a pharmaceutical preparation in three steps of the manufacturing process

A near infrared diffuse reflectance spectroscopy (NIRS) procedure for the quantitative control analysis of the active compound (otilonium bromide) in a pharmaceutical preparation in three steps of the production process (blended product, cores and coated tablets) and a methodology for its validation are proposed. The analytical procedure is composed by two consecutive steps. First, the sample is identified by comparing its spectrum with a second derivative spectral library. If the sample is positively identified, the active compound is quantified by using a previously established partial least squares (PLS) calibration model. The procedure was validated by studying repeatability, intermediate precision, accuracy and linearity. To this end, an adaptation of ICH (International Conference on Harmonisation) validation methodology to an NIR multivariate calibration procedure is proposed. The relative standard error of prediction (RSEP) was ≤ 1% and the suitability of the procedure for control analysis was confirmed by the results obtained analysing new production samples produced over a three-month period.     

近红外光谱分析技术的最新进展与展望

在过去的几十年,近红外光谱是发展最为迅速的分析技术之一。振动光谱基础理论、光谱仪器硬件和化学计量学是现代近红外光谱分析技术的3大支柱,近些年,近红外光谱技术在这3个方面均取得了显著进展。该文结合上述3个方面的应用研究情况,对近红外光谱分析技术的最新进展进行了综述,并对未来发展趋势进行了展望。     

基于交互式自模型混合物分析的近红外光谱波长变量优选方法

为了提高近红外光谱定量分析的预测精度和建模效率,提出了一种基于交互式自模型的混合物分析的波长优选方法,根据光谱各波长变量的纯度值和标准差值,选择含有用信息的波长变量,并引入相关权函数解决变量间共线性问题.通过依次迭代选择的变量建立定量校正模型,由交互验证均方根预测误差(RMSECV)确定最佳波长变量个数.应用该波长变量优选方法对具有不同葡萄糖含量的两组(四成分葡萄糖水溶液实验和人体血浆实验)近红外光谱数据进行分析,两组数据中分别只选择了全部变量的0.3%建立定量校正模型,其验证集葡萄糖浓度的均方根预测误差(RMSEP)分别减少为669和15 mg/L.与全谱范围及优选波段建立的定量校正模型比较,本方法能够通过波长变量优选最小化冗余信息、提高预测精度及建模效率.     

复方氯丙那林质量检控的近红外光谱快速分析

基于近红外光纤漫反射技术,利用偏最小二乘法分别建立了复方氯丙那林胶囊的三种药效成分盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的快速同时测定方法。所建立的盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的定量分析多元校正模型的相关系数分别为99.7%、99.4%和99.0%,校正集的均方根残差分别为0.028、0.145和0.250,预测均方根误差分别为0.055、0.120和0.210。由于该方法是在不经任何预处理的情况下的光纤快速同时分析,因此可用于复方氯丙那林的过程质量控制。     

Assessment uncertainty associated to the analysis of organic compounds linked to particulate matter of atmospheric aerosols

Current work has evaluated uncertainty associated to quantification of several organic compounds present in particulate matter of atmospheric aerosols, setting out the stages of analytical procedure that contribute most to the global uncertainty. Several sources of uncertainty have been identified, which were clustered into five main contributions: sampling, extraction, clean-up, derivatization and analysis. A discussion of the main contributions to the overall uncertainty is reported, allowing authors to locate the largest ones and plan future improvements. Combined uncertainties ranged between 10-18% (alkanes), 12-16% (PAHs), 10-18% (alcohols) and 9-21% (acids). The analytical procedure was validated by analysing a standard reference material (SRM1649a, urban dust). Also, the proposed method was applied to the analysis of four samples of particulate matter.     

粒子群算法结合支持向量机回归法用于近红外光谱建模

研究了最小二乘法支持向量机(LSSVM)应用于烟丝样品和小麦样品的近红外光谱建模,采用粒子群优化算法(PSO)优化LSSVM的参数。通过对烟草样品和小麦样品的近红外光谱建模和预测,并与常规的偏最小二乘法(PLS)比较发现,PSO-LSSVM法具有更好的预测效果和稳健性。     

一种消除在线多通道近红外分析仪各通道光谱差异的方法

针对在线多通道近红外分析仪因光纤耦合器件加工精度和装配过程存在细微差异而引起通道间光谱不一致的问题,在对光谱差异进行解析的基础上,提出了一种运算简捷、且在实际应用中易于实现的平均光谱差值校正(MSSC)方法,并与常用的模型传递算法如斜率／偏差(S／B)算法、分段直接校正(PDS)算法,以及通过偏最小二乘-人工神经网络(PLS-ANN)建立多通道混合校正模型进行了对比。结果表明,该方法可有效消除各通道所测光谱之间存在的差异,实现了多通道分析模型的通用性。