基于主成分分析和偏最小二乘回归的烟煤水分近红外检测

基于近红外光谱技术对烟煤水分分析的快速、无损性。采集了100个烟煤样品,分成验证集和预测集,验证集85个,预测集15个。利用主成分分析对烟煤的近红外光谱数据进行压缩,然后以主成分为输入,采用偏最小二乘回归建立烟煤水分预测模型。烟煤水分平均绝对相对误差为0.0728,表明该方法用于预测烟煤水分含量是可行的。     

Formation of Polar Compounds During Deep‐frying—Determination by 1H NMR and ESR

The present study aims to elucidate the factors influencing the generation of polar compounds in oils during deep‐frying. Oils with different fatty acid compositions, including palm oil (PO), refined palm kernel oil (RPKO), and refined coconut oil (RCO), are applied in successive frying processes. ¹H NMR spectra reveal that heated PO has a higher percentage of allyl acyl group and is more prone to formation of non‐polar dimeric triglycerides as compared to other types of oils. In addition, electron spin resonance (ESR) spectra indicate that alkyl radicals are more predominant than alkoxy radicals in heated PO. In contrast, RPKO and RCO are inclined to generation of alkoxy radicals during the thermal treatment. The results reveal that oils with high unsaturated fatty acid content are more prone to generation and oxidation of non‐polar dimeric triglycerides. Practical Applications: The change in free radical profile and concentration is one of the indicators of lipid oxidation and polymerization. Alterations in the levels of alkyl and alkoxyl radicals, revealed by ESR, can be used to illustrate the formation of polar compounds in deep‐fried oils with different fatty acid compositions. The percentage of allyl acyl group, revealed by ¹H NMR, can be used to predict the generation of polar compounds. Therefore, this study provides useful information for the development of different methods to reduce polar compound formation in oils during thermal processing depending on the fatty acid composition of different deep‐fried oils.     

Determination of formaldehyde/urea molar ratio in amino resins by near‐infrared spectroscopy

New processes for synthesis of urea‐formaldehyde (UF) and melamine‐fortified urea‐formaldehyde (mUF) resins have been developed in the last years, motivated by the current concerns about the effects of formaldehyde on human health. All these formulations are quite susceptible to possible operation error, which can significantly influence the characteristics of the final product. The main objective of this work was to implement chemometric techniques for off‐line monitoring of the product's formaldehyde/urea (F/U) molar ratio using near infrared (NIR) spectroscopy. This allows the timely implementation of the necessary corrections in case the product is off‐specification. Calibration models for F/U molar ratio were developed taking into account the most relevant spectral regions for these resins, individually or in combination (7502–6098 cm^?1 and 5000–4246 cm^?1) and using different preprocessing methods. When the appropriate spectral range and preprocessing methods are selected, it is possible to obtain calibration models with high correlation values for these resins. The best preprocessing methods were identified for three cases: UF resin (produced by strongly‐acid process), mUF resin (alkaline‐acid process), and a combined model that involves both UF and mUF resins. It was concluded that significantly better accuracy is obtained when a new model is developed for each particular resin system. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

偏最小二乘-神经网络光度法同时测定水中的NO3-和NO2-

应用偏最小二乘-神经网络直接解析硝酸根和亚硝酸根的紫外光谱,不经分离紫外吸光光度法同时测定硝酸根和亚硝酸根。在BP算法上,引用改进的误差传递函数,并采用均匀试验设计法确定了最佳网络运行参数。用于水样中硝酸根和亚硝酸根的同时测定,回收率分别为99.0%,105.0%。     

Determination of the Hydroxyl Value of Soybean Polyol by Attenuated Total Reflectance/Fourier Transform Infrared Spectroscopy

A rapid method for the quantitative determination of the hydroxyl value (OHV) of hydroxylated soybean oils by HATR/FTIR spectroscopy is described. Calibration standards were prepared by the formic acid/hydrogen peroxide method and OH values were determined by the official method of AOCS Tx 1a-66, covering an analytical range of 3.5–125 mg of KOH/g of sample. A partial least squares (PLS) calibration model for the prediction of the hydroxyl value (OHV) was developed based on eight different spectral subregions between 3,150 and 990 cm⁻¹ and combinations of them. On average, 36 samples were used for the modeling and 17 were used for external validation. The resulting calibration was linear over the analytical range and had a standard deviation of 2.334. Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oils predicted by the PLS model to the values obtained by the AOCS standard method. A correlation coefficient of R ² = 0.9843 and RMSEC and RMSEP values of, respectively, 3.393 and 3.643 were obtained. After the calibration of the spectrometer, the OHV could be obtained in 2–3 min per sample, a major improvement over conventional wet chemical methods. The advantages of these methodologies are that they do not destroy the sample, have a lower cost, expedite the analysis and do not produce residues. Therefore, they may yield excellent results when used to quantify OHV of soybean polyols obtained by hydroxylation reaction.     

Detection of Extra Virgin Olive Oil Adulteration With Edible Oils Using Front‐Face Fluorescence and Visible Spectroscopies

Synchronous front‐face fluorescence and visible spectroscopies are utilized for the simple, rapid, and nondestructive quantification of extra virgin olive oil (EVOO) adulteration with corn, soybean, and sunflower oils. For each adulterant, 42 adulterated EVOO samples in the adulterant amount in the range 1.0–50 g/100 g were prepared. The partial‐least‐squares regression was executed for quantification. Both full (leave‐one‐out) cross‐validation and external validation were performed to evaluate the predictive ability. The plots of observed vs. predicted values exhibit high linearity. The coefficient of determination (R²) values are larger than 0.99. The root mean square errors of both cross‐validation and prediction are no more than 2%. The detection limits for the three seed oils using fluorescence and visible spectroscopies are in the range of 1.5–2.2% and 1.8–2.4%, respectively. The merit of this method is that both the front‐face fluorescence and visible spectroscopies are recorded toward neat oils, avoiding any sample pretreatment including dilution.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Novel,Rapid Identification,and Quantification of Adulterants in Extra Virgin Olive Oil Using Near‐Infrared Spectroscopy and Chemometrics

A new, rapid Fourier transform near infrared (FT‐NIR) spectroscopic procedure is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT‐NIR index based mainly on the relative intensities of two unique carbonyl overtone absorptions in the FT‐NIR spectra of EVOO and other mixtures attributed to volatile (5280 cm^?1) and non‐volatile (5180 cm^?1) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models. Some adulterated mixtures could be identified provided the FA profile was sufficiently different from those of EVOO. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking EVOO with different concentrations of each adulterant. Based on FT‐NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants, each with a characteristic FA composition. Using these different PLS1 calibration models for prediction, plots of predicted vs. gravimetric concentrations of an adulterant in EVOO yielded linear regression functions with four unique sets of slopes, one for each group of adulterants. Four corresponding slope rules were defined that allowed for the determination of the nature and concentration of an adulterant in EVOO products by applying these four calibration models. The standard addition technique was used for confirmation.