Raman spectra of high‐density,low‐density,and linear low‐density polyethylene pellets and prediction of their physical properties by multivariate data analysis

Raman spectra have been measured for pellets of five samples of high‐density polyethylene (HDPE), seven samples of low‐density polyethylene (LDPE), and six samples of linear low‐density polyethylene (LLDPE). The obtained Raman spectra have been compared to find out characteristic Raman bands of HDPE, LDPE, and LLDPE. Principal component analysis (PCA) was applied to the Raman spectra in the 1600–650 cm^?1 region after multiplicative scatter correction (MSC) to discriminate the Raman spectra of the three different PE species. They are classified into three groups by a score plot of PCA factor 1 vs. 2. HDPE with high density and high crystallinity gives high scores on the factor 1 axis, while LDPE with low density and low crystallinity yields negative scores on the same axis. It seems that factor 1 reflects the density or crystallinity. A PC weight loadings plot for factor 1 shows six upward peaks corresponding to the bands arising from the crystalline parts or all‐trans ? (CH₂)_n? groups and seven downward peaks ascribed to the bands of the amorphous or anisotropic regions and those arising from the short branches. Partial least‐squares (PLS‐1) regression was applied to the Raman spectra after MSC to propose calibration models that predict the density, crystallinity, and melting points of the polyethylenes. The correlation coefficient was calculated to be 0.9941, 0.9800, and 0.9709 for the density, crystallinity, and melting point, respectively, and their root‐mean‐square error of cross validation (RMSECV) was found to be 0.0015, 3.3707, and 2.3745, respectively. The loadings plot of factor 2 for the prediction of melting point is largely different from those for the prediction of density and crystallinity. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 443–448, 2002     

Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy

The use of ethanol and biodiesel, which are alternative fuels or biofuels, has increased in the last few years. Modern official standards list 25 parameters that must be determined to certify biodiesel quality, and these analyses are expensive and time-consuming. Near infrared (NIR/NIRS) spectroscopy (4000-12,820 cm⁻¹) is a cheap and fast alternative to analyse biodiesel quality, when compared with infrared, Raman, or NMR methods, and quality control can be done in realtime (on-line).We compared the performance of linear and non-linear calibration techniques - namely, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS), polynomial and Spline-PLS versions, and artificial neural networks (ANN) - for prediction of biodiesel properties from near infrared spectra. The model was created for four important biodiesel properties: density (at 15 °C), kinematic viscosity (at 40 °C), water content, and methanol content. We also investigated the influence of different pre-processing methods (Savitzky-Golay derivatives, orthogonal signal correction) on the model prediction capability. The lowest root mean squared errors of prediction (RMSEP) of ANN for density, viscosity, water percentage, and methanol content were 0.42 kg m⁻³, 0.068 mm² s⁻¹, 45 ppm, and 51 ppm, respectively. The artificial neural network (ANN) approach was superior to the linear (MLR, PCR, PLS) and “quasi”-non-linear (Poly-PLS, Spline-PLS) calibration methods.     

On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods

A rapid and non‐intrusive on‐line NIR imaging sensor is developed for monitoring spatio‐temporal crystallinity variations across the surface of polymer films. A multivariate image analysis and regression (MIA/MIR) approach is proposed and compared with standard NIR calibration techniques using averaged spectra or second order derivatives combined with PLS regression. Predictions of both the local and global crystallinity variations of HDPE, LDPE, and PP polymer samples were obtained with each approach. Our results show that small variations in crystallinity introduced by changes in cooling rates can be predicted within experimental errors. Crystallinity spatial distributions were also validated and found consistent with processing conditions.     

Multivariate calibration of fourier transform infrared spectra for determining thiobarbituric acid-reactive substance content in palm oil

Fourier transform infrared (FTIR) spectra of palm oil samples between 2900 and 2800 cm⁻¹ and 1800 and 1600 cm⁻¹ were used to compare different multivariate calibration techniques for quantitative determination of their thiobarbituric acid-reactive substance (TBARS) content. Fifty spectra (in duplicate) of palm oil with TBARS values between 0 and 0.25 were used to calibrate models based on partial least squares (PLS) and principal components regression (PCR) analyses with different baselines. The methods were compared for the number of factors, coefficients of determination (R ²), and accuracy of estimation. The standard errors of prediction (SEP) were calculated to compare their predictive ability. The calibrated models generated three to eight factors, R ² of 0.9414 to 0.9803, standard error of estimation (SEE) of 0.0063 to 0.0680, and SEP of 1.20 to 6.67.     

Monitoring PV in corn and soybean oils by NIR spectroscopy

NIR spectroscopy was used successfully in our laboratory to monitor oxidation levels in vegetable oils. Calibration models were developed to measure PV in both soy and corn oils, using partial least squares (PLS) regression and forward stepwise multiple linear regression, from NIR transmission spectra. PV can be measured successfully in both corn and soy oils using a single calibration. The most successful calibration was based on PLS regression of first derivative spectra. When this calibration was applied to validation sample sets containing equal numbers of corn and soy oil samples, with PV ranging from 0 to 20 meq/kg, a correlation coefficient of 0.99 between titration and NIR values was obtained, with a standard error of prediction equal to 0.72 meq/kg. For both types of oil, changes occurred in the 2068 nm region of the NIR spectra as oxidation levels increased. These changes appear to be associated with the formation of hydroperoxides during oxidation of the oils.     

Robust on‐line measurement of conversion and molecular weight using NIR spectroscopy during solution polymerization

Fiber‐optic near‐infrared (NIR) spectroscopy was used to monitor the monomer conversion and the weight‐average molecular weight of the polymer produced during solution polymerization of methyl methacrylate (MMA) carried out in a lab‐scale reactor. NIR spectra were recorded during batch and semi‐continuous reactions using an in situ transmission probe. Off‐line gravimetry and GPC were used as reference methods to provide the conversion and the average molecular weight data set required for the calibration procedure. A statistical model was generated using partial least‐squares regression (PLS) to relate the NIR spectral data to the two polymerization variables of interest. The measurements were then validated for various operating conditions (i.e., different solvent, initiator, MMA, and chain‐transfer agent concentrations) and for both batch and semi‐continuous modes. The conversion was predicted during three validation experiments with an average standard error of prediction (SEP) of 2.1%. The on‐line evaluation of M?_w was obtained with an average relative SEP of 5.7%; such on‐line NIR measurement was thus demonstrated to be robust and accurate, even in the case of versatile use of the polymerization plant. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 2510–2520, 2002     

Near-Infrared Spectroscopy and Chemometric Analysis for Determining Oxygen Delignification Yield

Abstract

Oxygen delignification studies were carried out using a softwood kraft pulp under varying reaction temperatures (80–140°C) and alkaline charges (1–12%). Near-infrared (NIR) spectroscopy combined with chemometric methods was employed to analyze oxygen delignification pulp yields, which were compared to gravimetric analysis. Principal component analysis (PCA) of the NIR spectra data was performed and a partial least-square (PLS) regression model was developed to predict the pulp yield of oxygen delignified pulps based on the NIR spectra data. PCA analysis indicated that 99.1% of total variances of NIR spectra data in the range of 1100–2266 nm could be expressed by three principle components. A PLS1 model based on the NIR spectra data had good predictive ability and appeared to be an effective tool for pulp yield prediction for the oxygen delignification process.     

Near Infrared Spectroscopy for On-line Monitoring of Alkali- Free Cloth/Phenolic Resin Prepreg During Manufacture

A NIR method was developed for the on-line monitoring of alkali-free cloth/phenolic resin prepreg during its manufacturing process. First, the sizing content of the alkali-free cloth was analyzed, and then the resin, soluble resin and volatiles content of the prepreg was analyzed simultaneously using the FT-NIR spectrometer. Partial least square (PLS) regression was used to develop the calibration models, which for the sizing content was preprocessed by 1stDER +MSC, for the volatile content by 1stDER +VN, for the soluble resin content by 1stDER +MSC and for the resin content by the VN spectral data preprocessing method. RMSEP of the prediction model for the sizing content was 0.732 %, for the resin content it was 0.605, for the soluble resin content it was 0.101 and for volatiles content it was 0.127. The results of the paired t-test revealed that there was no significant difference between the NIR method and the standard method. The NIR spectroscopy method could be used to predict the resin, soluble resin and the volatiles content of the prepreg simultaneously, as well as sizing content of alkali-free cloth. The processing parameters of the prepreg during manufacture could be adjusted quickly with the help of the NIR analysis results. The results indicated that the NIR spectroscopy method was sufficiently accurate and effective for the on-line monitoring of alkali-free cloth/phenolic resin prepreg.     

Simultaneous determination of molecular weight and crystallinity of recycled HDPE by infrared spectroscopy and multivariate calibration

An attempt of correlating molecular weight (M_n) of recycled high‐density polyethylene (HDPE) as measured by size‐exclusion chromatography (SEC) with diffuse reflectance near and mid‐infrared spectroscopy (NIR/MIR) was made by means of multivariate calibration. The spectral data obtained was also used to extract information about the degree of crystallinity of the recycled resin. Differential scanning calorimetry (DSC) was used as the reference method. Partial least‐squares (PLS) calibration was performed on the MIR and NIR spectral data for prediction of M_n. Four PC factors described fully the PLS models. The root‐mean‐square error of prediction (RMSEP) obtained with MIR data was 360, whereas a RMSEP of 470 was achieved when calibration was carried out on the diffuse reflectance NIR data. A PLS calibration for prediction of degree of crystallinity was performed on the NIR data in the 1100–1900‐nm region, but the ability of prediction of this model was poor. However a PLS calibration in the region 2000–2500 nm yield better results. Four PC factors explained the most of the variance in the spectra and the RMSEP was 0.4 wt %. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 321–327, 2002     

基于拉曼光谱的高密度聚乙烯质量检测

密度和熔融指数是高密度聚乙烯（HDPE）产品最重要的质量指标。本文通过拉曼光谱,结合偏最小二乘法（PLS）分析,实现了对HDPE密度和熔融指数的同时检测。通过对2700～2970 cm^-1范围内HDPE的拉曼光谱进行PLS分析,发现了HDPE的密度与短支链数量的负相关,并建立了HDPE密度的PLS回归模型。模型所得密度预测值与真实值的相关系数（r）、平均相对误差（ARD）和预测标准误差（SEP）分别为0.950、0.09%和1.02,优于近红外光谱和基于凝聚态结构分析的拉曼光谱的检测结果。利用HDPE乙烯基含量与熔融指数的正相关,通过分析1288～1650 cm^-1范围内的拉曼光谱,建立了HDPE熔融指数的PLS回归模型,所得熔融指数的预测值与真实值的r、ARD和SEP分别为0.966、8.61%和0.99。与熔融指数的红外光谱检测结果相比,拉曼光谱的检测结果具有更高的精度。     

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     

Determining Frying Oil Degradation by Near Infrared Spectroscopy Using Chemometric Techniques

A rapid method for the determination of some important physicochemical properties in frying oils has been developed. Partial least square regression (PLS) calibration models were applied to the physicochemical parameters and near infrared spectroscopy (NIR) spectral data. PLS regression was used to find the NIR region and the data pre-processing method that give the best prediction of the chemical parameters. Calibration and validation were appropriated by leave one out cross validation and test set validation techniques for predicting free fatty acids (FFA), total polar materials (cTPM; measured by chromatographic method and iTPM measured by an instrumental method), viscosity and smoke point of the frying oil samples. For PLS models using the cross validation techniques, the best correlations (r) between NIR predicted data and the standard method data for iTPM in oils were 93.79 and root mean square error of prediction (RMSEP) values were 5.53. For PLS models using the test set validation techniques, the best correlations (r) between NIR predicted data and standard method data for FFA, cTPM, viscosity and smoke point in oils were 92.58, 94.61, 81.95 and 84.07 and RMSEP values were 0.121, 3.96, 22.30 and 8.74, respectively. In conclusion, NIR technique with chemometric analysis was found very effective in predicting frying oil quality changes.     

Determination of moisture content in nylon 6,6 by near‐infrared spectroscopy and chemometrics

The effects of moisture on the morphology and mechanical properties of polyamides have been extensively studied by a number of researchers. However, the assessment of water content in the resins has been carried out by thermal or thermogravimetric methods, which are destructive. In the present work partial least‐squares (PLS) calibration models based on near‐infrared (NIR) spectroscopy were produced in order to predict the moisture content of nylon 6,6. Differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and the loss‐on‐drying (LOD) method were used as reference methods. TGA, LOD, DSC, and NIR analysis were performed in parallel, and the obtained data were used for multivariate calibration purposes. Data pretreatment techniques such as derivation and multiplicative scattering correction (MSC) successfully eliminated the baseline offset present in the raw spectra and compensated for differences in thickness and light scattering of the analyzed samples. Calibration models were validated by full cross validation with the help of a test set. A comparison of the prediction ability of PLS models based on pretreated data was also done. NIR spectroscopy is a rapid and nondestructive method for the determination of moisture in recycled nylon. The moisture content can be predicted with a RMSEP = 0.05 wt %. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 87: 2165–2170, 2003     

近红外光谱技术在含能材料成分分析中的建模研究

近红外光谱法在对含能材料成分的分析中采用不同波数以及不同区间的变量，并用偏最小二乘法建立数学模型。建模结果说明模型精度较高，缩减后的自变量矩阵X中提取的主成分对原因变量有着更好的解释能力，并提高了计算速度。     

Multivariate determination of cloud point in palm oil using partial least squares and principal component regression based on FTIR spectroscopy

A rapid FTIR spectroscopic method was developed for quantitative determination of the cloud point (CP) in palm oil samples. Calibration samples were prepared by blending randomized amounts of palm olein and palm stearin to produce a wide range of CP values ranging between 8.3 and 47.9°C. Both partial least squares (PLS) and principal component regression (PCR) calibration models for predicting CP were developed by using the FTIR spectral regions from 3000 to 2800 and 1800 to 1600 cm⁻¹. The prediction capabilities of these calibration models were evaluated by comparing their standard errors of prediction (SEP) in an independent prediction set consisting of 14 palm oil samples. The optimal model based on PLS in the spectral range 1800-1600 cm⁻¹ produced lower SEP values (2.03°C) than those found with the PCR (2.31°C) method. FTIR in conjunction with PLS and PCR models was found to be a useful analytical tool for simple and rapid quantitative determination of CP in palm oil.     

Assessment of resin formulations and determination of the formaldehyde to urea molar ratio by near‐ and mid‐infrared spectroscopy and multivariate data analysis

The molar ratios of formaldehyde (F) to urea (U) of three resin formulations in the range from 0.90 to 1.49 have been analyzed by means of Attenuated Total Reflection‐Fourier Transform Infrared (ATR‐FTIR) spectroscopy and Fourier Transform‐Near‐Infrared (FT‐NIR) spectroscopy. Application of Principal Component Analysis (PCA) to the spectra (MIR and NIR) allowed to separate them according to the molar ratio and to distinguish between two groups of resins. Soft Independent Modeling of Class Analogy (SIMCA) allowed classification of new resin samples with high model distances between the classes. Partial Least Squares Regression (PLS‐R) models based on MIR (NIR) spectra resulted in high coefficients of determination (R²) values, low errors, and high residual prediction deviations (RPD). To confirm the reproducibility of the process and to carefully evaluate twice all multivariate analysis results obtained, different batches of resins have been prepared to have an additional independent sample set. The number of samples required for MIR and possible applications of MIR and NIR spectroscopy in this context including limitations have been discussed. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2013     

Application of Near-Infrared Spectroscopy for Evaluation of Drying Stress on Lumber Surface: A Comparison of Artificial Neural Networks and Partial Least Squares Regression

This study aimed to examine the feasibility of evaluating the stress level at the surface of lumber during drying using near-infrared (NIR) spectroscopy combined with artificial neural networks (ANNs). Sugi (Cryptomeria japonica D. Don) lumber with an initial moisture content ranging from 41.1 to 85.8% was dried using a commercial drying schedule. An ANN model for predicting surface-released strain (SRS) was developed based on NIR spectra collected from the lumber during drying. The predictive ability of the ANN model was compared with a partial least squares (PLS) regression model.

The ANN model showed good correlation between laboratory-measured SRS and predicted SRS with an R ² of 0.79, a root mean square error of prediction (RMSEP) of 0.0009, and a ratio of performance to deviation (RPD) of 1.81. The PLS regression model gave a lower R ² of 0.69, a higher RMSEP of 0.0010, and a lower RPD of 1.38 than the ANN model, suggesting that the predictive performance of the ANN model was superior to the PLS regression model. The SRS evolution during drying as predicted by the models showed a similar trend to the laboratory-measured one. The predicted elapsed times to reach maximum tensile SRS and stress reversal roughly coincided with the laboratory-measured times. These results suggest that NIR spectroscopy combined with multivariate analysis has the potential to predict the drying stress level on the lumber surface and the critical periods during drying, such as the points of maximum tensile stress and stress reversal.     

Method for determining oxidation of vegetable oils by near-infrared spectroscopy

Use of near-infrared (NIR) transmittance spectroscopy for rapid determination of the oxidation level in soybean oils (SBO) was investigated, and calibrations were developed for quantitative determination of peroxide value (PV), conjugated diene value (CD), and anisidine value (AV) of SBO. Partial least squares (PLS) regression and forward stepwise multiple linear regression were used to develop calibration models from spectral data in log 1/T, first derivative and second derivative of log 1/T formats for both 1- and 2-mm path lengths. The models were validated by comparing NIR results from independent sample sets to the values obtained by official methods. The spectral region from 1100 to 2200 nm was best for measuring oxidation when using a 2-mm path length. PLS regression using first-derivative spectra gave the best results for PV. For the validation sets, linear relationships were obtained for PV (r=0.99), and CD (r=0.95), compared with accepted reference procedures. However, measurement of AV by NIR was less successful than measurement of the other two indices of oxidation, especially for an external validation sample set. Results obtained in this study indicate that NIR spectroscopy is a useful technique for measuring oxidation in soybean oil.     

In‐process monitoring in industrial olive mill by means of FT‐NIR

A total of 287 olive lots and 161 olive oil samples were analyzed for fat content, moisture and free acidity, using a Fourier transform near‐infrared (FT‐NIR) instrument located in an industrial mill. Samples having a wide range of both reference values and olive lot sizes (from <0.5 to >4 t) were collected at three industrial mill plants, located in the same Italian region, which utilize different technological equipment for virgin olive oil production. Olive paste spectra were acquired in diffuse reflectance, while oil samples were measured in transmission. Calibration models for oil content and moisture of olives as well as free acidity of virgin olive oils were developed using partial least squares (PLS) regression, first derivative and straight line subtraction. Results of calibration and validation of the PLS models selected were good. The PLS results indicate good similarity between data obtained from FT‐NIR and reference laboratory methods, allowing a rapid and less expensive screening analysis. Unfortunately, the correlation between the oil yield values recorded for all olive lots at the industrial mills and the oil content predicted by FT‐NIR was not satisfactory (R² = 0.605).     

Raman Spectroscopic Barcode Use for Differentiation of Vegetable Oils and Determination of Their Major Fatty Acid Composition

In this study, differentiation of vegetable oils and determination of their major fatty acid (FA) composition were performed using Raman spectral barcoding approach. Samples from seven different sources (sunflower, corn, olive, canola, mustard, soybean and palm) were analyzed using Raman spectroscopy. Second derivative of the spectral data was utilized to generate unique barcodes of oils. Chemometric analyses, namely, principal component analysis (PCA) and partial least square (PLS) methods were used for data analysis. PCA was applied for classification of the samples according to the differences in their levels arising from their barcode data. A successful differentiation based on second derivative barcodes of Raman spectra (2D‐BRS) of vegetable oils was obtained. In addition, PLS method was applied on 2D‐BRS in order to determine the major FA composition of these samples. Coefficient of determination values for palmitic, stearic, oleic, linoleic, α‐linolenic, cis‐11 eicosenoic, erucic and nervonic acids were in the range of 0.970–0.989. Limit of detection and limit of quantification values were found to be satisfactory (0.09–8.09 and 0.30–26.95 % in oil) for these fatty acids . Advantages of both chemometric analysis and spectral barcoding approach have been utilized in the present study. Taking the second derivative of the Raman spectra has minimized background variability and sensitivity to intensity fluctuations. Spectral conversion to the barcodes has further increased the quality of information obtained from Raman spectra and also made it possible to improve the visualization of the data. Converting Raman spectra of oils into barcodes enables simpler presentation of the valuable information, and still allows further analysis such as classification of vegetable oils and prediction of their major fatty acids with high accuracy.