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.     

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.     

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     

Development of a Measuring Method for the Determination of Bisulfite and Sulfite in Seawater

In conventional reverse osmosis processes for seawater desalination, a disinfection of the process stream with chlorine compounds is carried out for antifouling. After disinfection the reduction agent Na₂S₂O₅ is used for the removal of residual chlorine in a strongly overstoichiometric way in order to protect the membranes from oxidational damages. To save chemicals a controlled dosing of Na₂S₂O₅ based on a reliable concentration measurement is desirable. Therefore, a measuring method for the determination of the sulfur(IV) components bisulfite and sulfite in seawater is developed based on the combination of UV spectroscopy and a PLS regression method. Experimental results as well as the development of the regression model for sulfur species in ultrapure and seawater is described.     

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     

Multivariate model for the prediction of total phenolic acids in crude extracts of polyphenols from canola and rapessed meals: A preliminary study

The feasibility of using UV spectrophotometry to develop multivariate models for prediction of total phenolic acids content in crude polyphenol extracts from defatted canola and rapeseed meals was investigated. The polyphenols were extracted from the meals with methanol/acetone/water (7∶7∶6, by vol). Partial least squares regression was used to correlate the spectral data of the crude polyphenols in methanol between 320 and 355 nm with the total phenolic acid content in canola and rapeseed meals. The Folin-Denis assay was used to provide reference data for creating the model. The predictive ability of the model is good, as indicated by the RPD value (the ratio of the SD of data to the standard error of calibration) of 3.84.