微波辅助-光催化降解氯酚的QSPR研究

本文运用PM3算法计算得到氯酚(CPs)的分子结构参数,并结合光解速率进行PLS分析,建立了速率与量子化参数之间的关系模型,模型的Q2cum分别为0.8776和0.9545(>0.5),具有良好的预测能力。研究结果表明,参数Ehomo、Elumo Ehomo、Elumo、N、Mw、Elumo-Ehomo、(Elumo-Ehomo)2是决定CPs光解速率的主要因素;CPs的光解速率随Ehomo、Elumo Ehomo、Elumo、Elumo-Ehomo、(Elumo-Ehomo)2的增大而增大,随N、Mw的增大而减小。     

Fluorescence spectroscopy for monitoring deterioration of extra virgin olive oil during heating

The potential of fluorescence spectroscopy for characterizing the deterioration of extra virgin olive oil (EVOO) during heating was investigated. Two commercial EVOO were analysed by HPLC to determine changes in EVOO vitamin E and polyphenols as a result of heating at 170°C for 3 h. This thermal oxidation of EVOO caused an exponential decrease in hydroxytyrosol and vitamin E (R²=0.90 and 0.93, respectively) whereas the tyrosol content was relatively stable. At the same time, amounts of preformed hydroperoxides (ROOH), analysed by an indirect colorimetric method, decreased exponentially during the heating process (R²=0.94), as a result of their degradation into secondary peroxidation products. Fluorescence excitation spectra with emission at 330 and 450 nm were recorded to monitor polyphenols and vitamin E evolution and ROOH degradation, respectively. Partial least-squares calibration models were built to predict these indicators of EVOO quality from oil fluorescence spectra. A global approach was then proposed to monitor the heat charge from the overall fluorescence fingerprint. Different data pretreatment methods were tested. This study indicates that fluorescence spectroscopy is a promising, rapid, and cost-effective approach for evaluating the quality of heat-treated EVOO, and is an alternative to time-consuming conventional analyses. In future work, calibration models will be developed using a wide range of EVOO samples.     

Multivariate monitoring of soybean oil ethanolysis by FTIR

In this work, an analytical procedure was developed to monitor the ethanolysis of degummed soybean oil (DSO) using Fourier-transformed mid-infrared spectroscopy (FTIR) and methods of multivariate analysis such as principal component analysis (PCA) and partial least squares regression (PLS). The triglycerides (reagents) and ethyl esters (products) involved in ethanolysis were shown to have similar FTIR spectra. However, when the FTIR spectra derived from seven standard mixtures of triolein and ethyl oleate were treated by PCA at the region that represents the CO stretching vibration of ester groups (1700-1800 cm⁻¹), only two principal components (PC) were shown to capture 99.95% of the total spectral variance (92.37% for the former and 7.58% for the latter PC). This observation supported the development of a multivariate calibration model that was based on the PLS regression of the FTIR data. The prevision capability of this model was measured against 40 reaction aliquots whose ester content was previously determined by size exclusion chromatography. Only small discrepancies were observed when the two experimental data sets were treated by linear regression (R²=0.9837) and these deviations were attributed to the occurrence of non-modeled transient species in the reaction mixture (reaction intermediates), particularly at short reaction times. Therefore, the FTIR/PLS model was shown to be a fast and accurate method to predict reaction yields and to follow the in situ kinetics of soybean oil ethanolysis.     

Simultaneous kinetic spectrophotometric determination of cyanide and thiocyanate using the partial least squares (PLS) regression

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of CN⁻ and SCN⁻ ions is described. The method is based on the difference in the rate of the reaction between CN⁻ and SCN⁻ ions with chloramine-T in a pH 4.0 buffer solution and at 30 °C. The produced cyanogen chloride (CNCl) reacts with pyridine and the product condenses with barbituric acid and forms a final colored product. The absorption kinetic profiles of the solutions were monitored by measuring absorbance at 578 nm in the time range 20-180 s after initiation of the reaction with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 10.0-900.0 and 50.0-1200.0 ng mL⁻¹ for CN⁻ and SCN⁻ ions, respectively. The proposed method was successfully applied to the simultaneous determination of cyanide and thiocyanate in water samples.     

Determination of ethanol in fuel ethanol and beverages by Fourier transform (FT)-near infrared and FT-Raman spectrometries

Fourier transform-near infrared (FT-NIR) and FT-Raman spectrometries have been used to design partial least squares (PLS) calibration models for the determination of the ethanol content of ethanol fuel and alcoholic beverages. In the FT-NIR measurements the spectra were obtained using air as reference, and the spectral region for PLS modeling were selected based on the spectral distribution of the relative standard deviation in concentration. In the FT-Raman measurements hexachloro-1,3-butadiene (HCBD) has been used as an external standard. In the PLS/FT-NIR modeling for ethanol fuel analysis 50 ethanol fuel standards (84.9-100% (w/w)) were used (25 in the calibration, 25 in the validation). In the PLS/FT-Raman modeling 25 standards were used (13 in the calibration, 12 in the validation). The PLS/FT-NIR and FT-Raman models for beverage analysis made use of 24 standards (0-100% (v/v)). Twelve of them contained sugars (1-5% (w/w)), one-half was used in the calibration and the other half in the validation. Different spectral pre-processing were used in the PLS modeling, depending on the type of sample investigated. In the ethanol fuel analysis the FT-NIR pre-processing was a 17 points smoothed first derivative and for beverages no spectral pre-processing was used. The FT-Raman spectra were pre-processed by vector normalization in the ethanol fuel analysis and by a second derivative (17 points smoothing) in the beverage analysis. The PLS models were used in the analysis of real ethanol fuel and beverage samples. A t-test has shown that the FT-NIR model has an accuracy equivalent to that of the reference method (ASTM D4052) in the analysis of ethanol fuel, while in the analysis of beverages, the FT-Raman model presents an accuracy equivalent to the reference method. The limits of detection for NIR and Raman calibration models were 0.05 and 0.2% (w/w), respectively. It has also been shown that both techniques, present better results than gas chromatography (GC) in evaluating the ethanol content of beverages.     

Durbin-Watson statistic as a morphological estimator of information content

The present work proposes a new approach for the evaluation of the information content in latent variables, and therefore, for the determination of the regression model dimensionality. Several examples are provided, using simulated, real-world, and reference datasets. The results showed that the application of the Durbin-Watson (DW) criterion could be used for the determination of the number of latent variables. Moreover, the method is straightforward in its implementation and could help in the understanding of model behaviour, particularly in complex datasets. A comparison is made with cross-validation techniques for the case of reference datasets, showing the potential of the Durbin-Watson criterion in the characterisation of the regression model. The advantages and disadvantages of this procedure (compared to cross-validation) are discussed. The properties of the information content of the regression vectors (loadings p, w and b vectors) are shown as well as how to use them for the current purpose.     

Non-destructive and clean prediction of aviation fuel characteristics through Fourier transform-Raman spectroscopy and multivariate calibration

The combination of Fourier transform (FT)-Raman spectroscopy and partial least squares (PLS) regression is proposed to be used in off-line kerosene quality control. Here, six important physico-chemical properties have been studied: Abel flash point, initial boiling point (IBP), 10% of distilled sample, final boiling point (FBP), total percentage of aromatic compounds (% aromatics) and viscosity. The Raman spectra were obtained directly from standard 2 ml glass vials ( mm), using a Bruker RFS 100 FT-Raman spectrometer, equipped with a 1064 nm Nd:YAG laser and a Ge detector, in back-scattering mode and accumulating 25 scans (150 s acquisition time) with a laser power of 300 mW. All Raman spectra were unit normalized in order to avoid the influence of the laser power into the regression models. Different Raman shift spectral ranges have been evaluated, the 193.5-1688.1 cm⁻¹ region being the most satisfactory one. Corrected standard errors of prediction of 1.9, 2.3, 2.8 °C, 0.19 cSt and 0.7% were obtained for flash point, IBP, FBP, viscosity and % aromatics, respectively. The influences of the glass vials, sample positioning and laser power have been studied, as well.