scholarly journals PREDICTION OF WATER-BASED PAINT PROPERTIES BASED ON THEIR MINERAL FILLERS: SIMPLEX-PLSR COUPLING APPLICATION

2018 ◽  
Vol 2 ◽  
pp. 2-15
Author(s):  
Paulo Ricardo Nunes da Conceição ◽  
Carlos Otávio Petter ◽  
Carlos Hoffmann Sampaio
Keyword(s):  
Numerical Model ◽  
Partial Least Squares Regression ◽  
Contrast Ratio ◽  
Mineral Filler ◽  
Least Squares Regression ◽  
Water Based ◽  
Paint Industry ◽  
Mineral Fillers ◽  
Dry Film ◽  
Response Variables

The present study shows the optimization of mineral filler blends for use in water-based paints. The aim of the modelling is to evaluate the possibility of creating a model of prediction of the contrast ratio and brightness of the dry film, where the fillers that participate in the system are fillers used commercially by the paint industry. The differential of the use of the mixtures design, called Simplex, is that the predictions are made by carrying out a linear combination between the proportions of the mineral fillers and the response variables. The proposed procedure consists in generating a numerical model that can predict final paint properties depending on the concentrations of the mineral fillers (PCC, GCC and kaolin) used. As support for simplex mixture design, the methods of Partial Least Squares regression and Response Surface were used in the calculations of the models and in the visualization of the response variables.

scholarly journals Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow’s Lactation

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2033
Author(s):  
Amira Rachah ◽  
Olav Reksen ◽  
Valeria Tafintseva ◽  
Felicia Judith Marie Stehr ◽  
Elling-Olav Rukke ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Partial Least Squares Regression ◽  
Principal Component ◽  
Milk Composition ◽  
Dietary Energy ◽  
Least Squares Regression ◽  
Subclinical Ketosis ◽  
Milk Samples ◽  
Fa Composition ◽  
Dry Film

The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows’ lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

Noise source identification for industrial sewing machines based on non-linear partial least squares regression model

2016 ◽  
Vol 230 (16) ◽  
pp. 2817-2827 ◽  
Author(s):  
Zhi-yong Zhang ◽  
Xin Liu ◽  
Cai-xia Huang ◽  
Da Pan
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Sound Pressure ◽  
Predictive Accuracy ◽  
Least Squares Regression ◽  
Explanatory Variables ◽  
Non Linear ◽  
Response Variables

This paper introduces an application of non-linear partial least squares for vibro-acoustic regression modeling and for an industrial sewing machine. In the vibro-acoustic regression model, the vibration accelerations of reference points are defined as explanatory variables, while the noise sound pressure of target points is defined as response variables, and the number of explanatory variables is determined initially by a correlation analysis in the time domain. To improve predictive accuracy while a non-linear relationship exists between the explanatory and response variables, the explanatory variables are preprocessed by kernel function transformation. The comparison of regressive noise sound pressure to experimental data indicates that the non-linear partial least squares regression model has high predictive accuracy. Furthermore, the contributions of vibration accelerations to noise sound pressure are analyzed, by which the structure optimizations are guided and practiced. The comparison of noise test results before and after optimization testifies to the effectiveness of the contribution analysis.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

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