Regulation of the Emulsion Particle Size Distribution to an Optimal Trajectory Using Partial Least Squares Model-Based Predictive Control

2004 ◽  
Vol 43 (23) ◽  
pp. 7227-7237 ◽  
Author(s):  
Myung-June Park ◽  
Mustafa T. Dokucu ◽  
Francis J. Doyle
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Least Squares ◽  
Size Distribution ◽  
Partial Least Squares ◽  
Predictive Control ◽  
Optimal Trajectory ◽  
Model Based ◽  
Partial Least Squares Model ◽  
Emulsion Particle

Effects of soil composition and preparation on the prediction of particle size distribution using mid-infrared spectroscopy and partial least-squares regression

Soil Research ◽  
2016 ◽  
Vol 54 (8) ◽  
pp. 889 ◽  
Author(s):  
Leslie J. Janik ◽  
José M. Soriano-Disla ◽  
Sean T. Forrester ◽  
Michael J. McLaughlin
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Least Squares ◽  
Size Distribution ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Calcareous Soils ◽  
Least Squares Regression ◽  
Soil Composition ◽  
Mid Infrared

Soil composition and preparation can affect prediction accuracy using diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS). In the present study, we evaluated the effect of soil composition, preparation and carbonate content on the accuracy of particle size distribution (PSD) predictions in four contrasting sets of soils, including calcareous soils, using partial least-squares regression (PLSR). The soils were scanned as <2- and <0.1-mm fine-ground samples. Regression calibrations were derived for individual soil sets, as well as a composite of the four sets. Predictions for clay and sand for the <2-mm composite calibration resulted in good accuracy (coefficient of determination R2=0.78; ratio of the standard deviation of reference values to the prediction error (RPD)=2.2), but were less accurate for clay in the calcareous soils (R2=0.70–0.78; RPD=1.8–1.1) and similarly accurate for sand (R2=0.68–0.80; RPD=1.7–2.2). Predictions for silt were poor. Accuracies improved by fine grinding (R2=0.88, RPD=2.9 for clay; R2=0.84, RPD=2.9 for sand). It was concluded that single, large and highly variable sets rather than site-specific calibrations could be used for the PSD predictions of specific soil sets. Changes in the PLSR loading weights, resulting from grinding, could be linked to an improved access of the infrared beam to the soil matrix by removal or dilution of surface coatings, resulting in a reduction of inter- and intraparticulate heterogeneity.

Influences of Ion Abundance of Cationic Hydrophilic Polyurethane Modified with Polysiloxane on its Emulsion Properties

2013 ◽  
Vol 781-784 ◽  
pp. 2685-2689
Author(s):  
Ru Yi Song ◽  
Yong Yan Li ◽  
Xin Ping Li ◽  
Jing Zhang ◽  
Heng Quan
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Coupling Agent ◽  
Reactive Dyes ◽  
Positive Contribution ◽  
Water Based ◽  
Optimal Efficiency ◽  
Emulsion Particle ◽  
Hydrophilic Polyurethane

Several cationic water-based polyurethanes end-capped with silicone coupling agent with different ion abundance are prepared. The effects of ion abundance of those polyurethanes on its emulsion particle size distribution, wet rubbing fastness improvement, hydrophility and soft handle are studied. The results show that the sample with moderate cationic ion abundance has optimal efficiency for color fixing of reactive dyes, and the quaterized cationic groups in polyurethane molecule should has positive contribution to hydrophility and soft handle of the treated fabric, especially for the color fabric treated with higher concentration polyurethane emulsion.

scholarly journals Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4645
Author(s):  
Asma Khan ◽  
Muhammad Tajammal Munir ◽  
Wei Yu ◽  
Brent Young
Keyword(s):  
Particle Size ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Principal Components ◽  
Milk Powder ◽  
Complete Spectrum ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Partial Least Squares Model

Hyperspectral imaging (HSI) in the spectral range of 400–1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC1 and PC2) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (Rp2) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had Rp2 of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.

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