Analysis of Contaminants in Lubricant Oil by near Infrared Spectroscopy and Interval Partial Least-Squares

2003 ◽  
Vol 11 (3) ◽  
pp. 211-218 ◽  
Author(s):  
Juliana Paschoal ◽  
Fernando D. Barboza ◽  
Ronei J. Poppi
Keyword(s):  
Ethylene Glycol ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Correlation Coefficients ◽  
Full Spectrum ◽  
Lubricant Oil ◽  
Mean Square Errors

The feasibility of using near infrared (NIR) transmission spectroscopy for rapid and conclusive determination of contaminants in lubricant oil was investigated. The NIR spectrum in the region from 1300 to 1700 nm was used to predict gasoline and ethylene glycol concentrations present in lubricant oil. A graphically-oriented local multivariate calibration modelling procedure called interval partial least-squares (iPLS) was applied to find variable intervals that featured the lowest prediction error. When compared with the full spectrum PLS model, better results were obtained through the iPLS program. High correlation coefficients and low root mean square errors of cross-validation ( RMSECV) were obtained for gasoline ( R = 0.98, RMSECV = 0.38%, range = 0.2–8.0% w/w) and ethylene glycol determinations ( R = 0.97, RMSECV = 0.04%, range = 0.06 to 0.7% w/w), indicating that the proposed methodology can be used for contaminant determinations in lubricant oil.

scholarly journals Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Full Spectrum ◽  
Lattice Design ◽  
The Mean

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

scholarly journals Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

2018 ◽  
Vol 11 (02) ◽  
pp. 1850006 ◽  
Author(s):  
Xuan Chu ◽  
Wei Wang ◽  
Chunyang Li ◽  
Xin Zhao ◽  
Hongzhe Jiang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Vegetable Oils ◽  
Near Infrared ◽  
Corn Oil ◽  
Correlation Coefficients ◽  
Full Spectrum ◽  
Camellia Oil ◽  
Infrared Spectral ◽  
Mean Square Errors

In this paper, a methodology based on characteristic spectral bands of near infrared spectroscopy (1000–2500[Formula: see text]nm) and multivariate analysis was proposed to identify camellia oil adulteration with vegetable oils. Sunflower, peanut and corn oils were selected to conduct the test. Pure camellia oil and that adulterated with varying concentrations (1–10% with the gradient of 1%, 10–40% with the gradient of 5%, 40–100% with the gradient of 10%) of each type of the three vegetable oils were prepared, respectively. For each type of adulterated oil, full-spectrum partial least squares partial least squares (PLS) models and synergy interval partial least squares (SI-PLS) models were developed. Parameters of these models were optimized simultaneously by cross-validation. The SI-PLS models were proved to be better than the full-spectrum PLS models. In SI-PLS models, the correlation coefficients of predition set (Rp) were 0.9992, 0.9998 and 0.9999 for adulteration with sunflower oil, peanut oil and corn oil seperately; the corresponding root mean square errors of prediction set (RMSEP) were 1.23, 0.66 and 0.37. Furthermore, a new generic PLS model was built based on the characteristic spectral regions selected from the intervals of the three SI-PLS models to identify the oil adulterants, regardless of the adultrated oil types. The model achieved with Rp[Formula: see text] 0.9988 and RMSEP [Formula: see text] 1.52. These results indicated that the characteristic near infrared spectral regions could determine the level of adulteration in the camellia oil.

scholarly journals Simultaneous Detemination of Atorvastatin Calcium and Amlodipine Besylate by Spectrophotometry and Multivariate Calibration Methods in Pharmaceutical Formulations

2011 ◽  
Vol 8 (4) ◽  
pp. 1670-1679 ◽  
Author(s):  
Amir H. M. Sarrafi ◽  
Elahe Konoz ◽  
Maryam Ghiyasvand
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Sample Pretreatment ◽  
Partial Least Square ◽  
Least Square ◽  
Full Spectrum ◽  
Calibration Methods

Resolution of binary mixture of atorvastatin (ATV) and amlodipine (AML) with minimum sample pretreatment and without analyte separation has been successfully achieved using a rapid method based on partial least square analysis of UV–spectral data. Multivariate calibration modeling procedures, traditional partial least squares (PLS-2), interval partial least squares (iPLS) and synergy partial least squares (siPLS), were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. The simultaneous determination of both analytes was possible by PLS processing of sample absorbance between 220-425 nm. The correlation coefficients (R) and root mean squared error of cross validation (RMSECV) for ATV and AML in synthetic mixture were 0.9991, 0.9958 and 0.4538, 0.2411 in best siPLS models respectively. The optimized method has been used for determination of ATV and AML in amostatin commercial tablets. The proposed method are simple, fast, inexpensive and do not need any separation or preparation methods.

Using Genetic Algorithm Interval Partial Least Squares Selection of the Optimal near Infrared Wavelength Regions for Determination of the Soluble Solids Content of “Fuji” Apple

2007 ◽  
Vol 15 (3) ◽  
pp. 153-159 ◽  
Author(s):  
Zou Xiaobo ◽  
Li Yanxiao ◽  
Zhao Jiewen
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Selection Of

A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 °Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.

Simultaneous fluorimetric determination of acetylsalicylic acid metabolites in urine by partial least squares multivariate calibration

1995 ◽  
Vol 353 (2) ◽  
pp. 211-214 ◽  
Author(s):  
Arsenio Muñoz de la Peña ◽  
Isabel Durán-Merás ◽  
María D. Moreno ◽  
Francisco Salinas ◽  
María Martínez Galera
Keyword(s):  
Acetylsalicylic Acid ◽  
Least Squares ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Fluorimetric Determination ◽  
Acid Metabolites

scholarly journals Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3855 ◽  
Author(s):  
Lin Bai ◽  
Cuizhen Wang ◽  
Shuying Zang ◽  
Changshan Wu ◽  
Jinming Luo ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Soil Ph ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Songnen Plain ◽  
Mean Square Errors

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

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