Rapid determination of biomass and polypropylene in three types of wood plastic composites (WPCs) using FTIR spectroscopy and partial least squares regression (PLSR)

Holzforschung ◽  
2015 ◽  
Vol 69 (4) ◽  
pp. 399-404 ◽  
Author(s):  
Gaiyun Li ◽  
Wanli Lao ◽  
Tefu Qin ◽  
Luohua Huang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Rapid Determination ◽  
External Validation ◽  
Practical Importance ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Wood Plastic Composites ◽  
Plastic Composites

Abstract The biomass/plastic ratio in wood plastic composites (WPCs) has been evaluated because of the great practical importance of this topic. To this purpose, FTIR spectra of 59 polypropylene (PP)-based WPCs from three biomass species (Chinese fir, poplar, and bamboo) were recorded and the spectral dates were evaluated by means of the partial least squares regression (PLSR) approach aiming at the prediction of the biomass/PP ratio in the WPCs. The results of the full cross-validation of the data showed that first derivative spectra corrected by standard normal variate (SNV) yielded the optimal model for prediction of the WPC composition. For both biomass and PP prediction, the coefficients of determination (R2) of external validation were above 0.94. The standard errors of prediction (SEP) were between 1.38 and 1.39. And the ratios of performance to deviation (RPD) were about 4.20. The relative prediction errors in this context were lower than ±6%. FTIR combined with PLSR is a useful tool for a rapid and reliable estimation of the biomass and PP contents in different types of PP-based WPCs.

Rapid Determination of Ethanol in Sugarcane Spirit Using Partial Least Squares Regression Embedded in Smartphone

2018 ◽  
Vol 11 (7) ◽  
pp. 1951-1957 ◽  
Author(s):  
Fernanda C. Böck ◽  
Gilson A. Helfer ◽  
Adilson B. da Costa ◽  
Morgana B. Dessuy ◽  
Marco F. Ferrão
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Rapid Determination ◽  
Least Squares Regression

scholarly journals Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 378 ◽  
Author(s):  
Nerea Núñez ◽  
Xavi Collado ◽  
Clara Martínez ◽  
Javier Saurina ◽  
Oscar Núñez
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Partial Least Squares Regression ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Chromatographic Fingerprints ◽  
Sample Classification ◽  
Chemical Descriptors

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

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.

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