Rapid determination of phospholipid content of vegetable oils by FTIR spectroscopy combined with partial least-square regression

2014 ◽  
Vol 147 ◽  
pp. 272-278 ◽  
Author(s):  
Xianghe Meng ◽  
Qiuyue Pan ◽  
Yang Ding ◽  
Lianzhou Jiang
Keyword(s):  
Ftir Spectroscopy ◽  
Vegetable Oils ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Phospholipid Content ◽  
Least Square Regression

scholarly journals Identification and quantification of additives in bituminous binders based on FTIR spectroscopy and multivariate analysis methods

2021 ◽  
Vol 54 (4) ◽  
Author(s):  
Sandra Weigel ◽  
Michael Gehrke ◽  
Christoph Recknagel ◽  
Dietmar A. Stephan
Keyword(s):  
Ftir Spectroscopy ◽  
Climatic Conditions ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Linear Discriminant ◽  
Bituminous Binders ◽  
Identification And Quantification

AbstractBitumen is a crucial building material in road construction, which is exposed to continuously higher stresses due to higher traffic loads and changing climatic conditions. Therefore, various additives are increasingly being added to the bitumen complicating the characterisation of the bituminous binder, especially concerning the reuse of reclaimed asphalt. Therefore, this work aimed to demonstrate that the combination of Fourier transform infrared (FTIR) spectroscopy with attenuated total reflexion (ATR) technique and multivariate evaluation is a very well-suited method to reliable identify and quantify additives in bituminous binders. For this purpose, various unmodified and modified binders, directly and extracted from laboratory and reclaimed asphalts, were investigated with FTIR-ATR spectroscopy. The determined spectra, pre-processed by standard normal variate (SNV) transformation and the determination of the 1st derivation, were evaluated using factor analysis (FA), linear discriminant analysis (LDA) and partial least square regression (PLSR). With this multivariate evaluation, first, a significant model with a very high hit rate of over 90% was developed allowing for the identification of styrene-butadiene copolymers (SBC), ethylene-copolymer bitumen (ECB) and different waxes (e.g. amide and Fischer-Tropsch wax) even if the additives do not show any additional peaks or the samples are multi-modified. Second, a quantification of the content is possible for SBC, ECB, and amide wax with a mean error of RMSE ≤ 0.4 wt% and a coefficient of determination of R2 > 90%. Based on these results, FTIR identification and quantification of additives in bituminous binders is a very promising method with a great potential.

Application of MALDI-TOFMS Combined with Partial Least Square Regression for the Determination of Mercury and Copper in Canned Tuna, Using Dithizone as the Complexing Agent and Ag(I) as Internal Standard

2018 ◽  
Vol 11 (10) ◽  
pp. 2835-2846 ◽  
Author(s):  
Manuel Mendez Garcia ◽  
Kazimierz Wrobel ◽  
Alejandra Sarahi Ramirez Segovia ◽  
Eunice Yanez Barrientos ◽  
Alma Rosa Corrales Escobosa ◽  
...  
Keyword(s):  
Internal Standard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Complexing Agent ◽  
Canned Tuna ◽  
Least Square Regression

Mechanical Parameter Analysis of Rock Material via PLSR Model

2011 ◽  
Vol 467-469 ◽  
pp. 1826-1831 ◽  
Author(s):  
Zao Bao Liu ◽  
Wei Ya Xu ◽  
Fei Xu ◽  
Lin Wei Wang
Keyword(s):  
Regression Model ◽  
Principal Component ◽  
Partial Least Square ◽  
Rock Material ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Mechanical Parameter ◽  
Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

scholarly journals Raman Spectral Analysis for Quality Determination of Grignard Reagent

2020 ◽  
Vol 10 (10) ◽  
pp. 3545
Author(s):  
Rahul Joshi ◽  
Ritu Joshi ◽  
Changyeun Mo ◽  
Mohammad Akbar Faqeerzada ◽  
Hanim Z. Amanah ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Grignard Reagent ◽  
Rapid Determination ◽  
Spectroscopic Method ◽  
Spectroscopic Technique ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Toluene Concentration

Grignard reagent is one of the most popular materials in chemical and pharmaceutical reaction processes, and requires high quality with minimal adulteration. In this study, Raman spectroscopic technique was investigated for the rapid determination of toluene content, which is one of the common adulterants in Grignard reagent. Raman spectroscopy is the most suitable spectroscopic method to mitigate moisture and CO2 interference in the molecules of Grignard reagent. Raman spectra for the mixtures of toluene and Grignard reagent with different concentrations were analyzed with a partial least square regression (PLSR) method. The combination of spectral wavebands in the prediction model was optimized with a variables selection method of variable importance in projection (VIP). The results obtained from the VIP-based PLSR model showed the reliable performance of Raman spectroscopy for predicting the toluene concentration present in Grignard reagent with a correlation coefficient value of 0.97 and a standard error of prediction (SEP) of 0.71%. The results showed that Raman spectroscopy combined with multivariate analysis could be an effective analytical tool for rapid determination of the quality of Grignard reagent.

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