scholarly journals Determination of the Degree of Degradation of Frying Rapeseed Oil Using Fourier-Transform Infrared Spectroscopy Combined with Partial Least-Squares Regression

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jie Yu Chen ◽  
Han Zhang ◽  
Jinkui Ma ◽  
Tomohiro Tuchiya ◽  
Yelian Miao
Keyword(s):  
Fourier Transform ◽  
Ftir Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cross Validation ◽  
Standard Errors ◽  
Calibration Model ◽  
Second Derivative ◽  
Pls Regression ◽  
Degree Of Degradation

This rapid method for determining the degree of degradation of frying rapeseed oils uses Fourier-transform infrared (FTIR) spectroscopy combined with partial least-squares (PLS) regression. One hundred and fifty-six frying oil samples that degraded to different degrees by frying potatoes were scanned by an FTIR spectrometer using attenuated total reflectance (ATR). PLS regression with full cross validation was used for the prediction of acid value (AV) and total polar compounds (TPC) based on raw, first, and second derivative FTIR spectra (4000–650 cm−1). The precise calibration model based on the second derivative FTIR spectra shows that the coefficients of determination for calibration(R2)and standard errors of cross validation (SECV) were 0.99 and 0.16 mg KOH/g and 0.98 and 1.17% for AV and TPC, respectively. The accuracy of the calibration model, tested using the validation set, yielded standard errors of prediction (SEP) of 0.16 mg KOH/g and 1.10% for AV and TPC, respectively. Therefore, the degradation of frying oils can be accurately measured using FTIR spectroscopy combined with PLS regression.

Determination of Iodine Value of Palm Oils Using Partial Least Squares Regression-Fourier Transform Infrared Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Nor Fazila Rasaruddin ◽  
Mas Ezatul Nadia Mohd Ruah ◽  
Mohamed Noor Hasan ◽  
Mohd Zuli Jaafar
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Iodine Value ◽  
Cross Validation ◽  
Pls Regression ◽  
Pure Sample

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

scholarly journals Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Gifty E. Acquah ◽  
Brian K. Via ◽  
Oladiran O. Fasina ◽  
Lori G. Eckhardt
Keyword(s):  
Fourier Transform ◽  
Chemical Composition ◽  
Ftir Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Fourier Transform Infrared ◽  
Least Squares Regression ◽  
Forest Logging ◽  
Logging Residue

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e.,R2> 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

Determination of Petroleum Properties by Fiber-Optic Fourier Transform Raman Spectrometry and Partial Least-Squares Analysis

1995 ◽  
Vol 49 (12) ◽  
pp. 1766-1771 ◽  
Author(s):  
Christopher J. de Bakker ◽  
Peter M. Fredericks
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Octane Number ◽  
Cross Validation ◽  
Fiber Optic ◽  
Petroleum Products ◽  
Fuel Properties ◽  
Raman Spectrometry ◽  
Ft Raman

The petroleum products unleaded gasoline and reformate have been analyzed by Fourier transform (FT) Raman spectrometry with the use of a fiber optic cable that allows a measurement to be carried out remotely from the spectrometer. Samples were contained in glass cuvettes, and collection of high-quality spectra was simple. Fuel properties such as research octane number (RON), motor octane number (MON), density, benzene content, and flexible volatility index (FVI) were determined by application of the partial least-squares multivariate statistical approach. Analytically useful calibrations ( R2 > 0.97) were obtained for all of the significant fuel properties studied. Cross-validation results were, as expected, worse than the calibration results but still indicated the usefulness of the method. Standard errors of prediction with the use of cross validation for models that contained 14–28 samples include: MON = 0.12, RON = 0.16, benzene % = 0.09, and density = 0.0018. These results demonstrate that it is feasible to undertake on-line analyses of petroleum fuels by fiber-optic FT-Raman spectrometry.

Spectral Variable Selection for Partial Least Squares Calibration Applied to Authentication and Quantification of Extra Virgin Olive Oils Using Fourier Transform Raman Spectroscopy

2005 ◽  
Vol 59 (10) ◽  
pp. 1286-1294 ◽  
Author(s):  
H. Michael Heise ◽  
Uwe Damm ◽  
Peter Lampen ◽  
Antony N. Davies ◽  
Peter S. McIntyre
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Olive Oil ◽  
Least Squares ◽  
Partial Least Squares ◽  
Sunflower Oil ◽  
Cross Validation ◽  
Calibration Models ◽  
Fourier Transform Raman Spectroscopy ◽  
Spectral Variable

The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years.

scholarly journals Interval partial least squares and moving window partial least squares in determining the enantiomeric composition of tryptophan by using UV-Vis spectroscopy

2016 ◽  
Vol 81 (2) ◽  
pp. 209-218 ◽  
Author(s):  
Long Jiao ◽  
Shan Bing ◽  
Xiaofeng Zhang ◽  
Hua Li
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Cross Validation ◽  
Test Validation ◽  
Enantiomeric Composition ◽  
Calibration Model ◽  
Moving Window ◽  
Vis Spectroscopy ◽  
Leave One Out ◽  
External Test

The application of interval partial least squares (IPLS) and moving window partial least squares (MWPLS) to the enantiomeric analysis of tryptophan (Trp) was investigated. A UV-Vis spectroscopy method for determining the enantiomeric composition of Trp was developed. The calibration model was built by using partial least squares (PLS), IPLS and MWPLS respectively. Leave-one-out cross validation and external test validation were used to assess the prediction performance of the established models. The validation result demonstrates the established full-spectrum PLS model is impractical for quantifying the relationship between the spectral data and enantiomeric composition of L-Trp. On the contrary, the developed IPLS and MWPLS model are both practicable for modeling this relationship. For the IPLS model, the root mean square relative error (RMSRE) of external test validation and leave-one-out cross validation is 4.03 and 6.50 respectively. For the MWPLS model, the RMSRE of external test validation and leave-one-out cross validation is 2.93 and 4.73 respectively. Obviously, the prediction accuracy of the MWPLS model is higher than that of the IPLS model. It is demonstrated UV-Vis spectroscopy combined with MWPLS is a commendable method for determining the enantiomeric composition of Trp. MWPLS is superior to IPLS for selecting spectral region in UV-Vis spectroscopy analysis.

Sign in / Sign up

Export Citation Format

Share Document