Prediction of Aged Red Wine Aroma Properties from Aroma Chemical Composition. Partial Least Squares Regression Models

2003 ◽  
Vol 51 (9) ◽  
pp. 2700-2707 ◽  
Author(s):  
Margarita Aznar ◽  
Ricardo López ◽  
Juan Cacho ◽  
Vicente Ferreira
Keyword(s):  
Chemical Composition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Red Wine ◽  
Wine Aroma ◽  
Least Squares Regression ◽  
Aroma Chemical

scholarly journals Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Gifty E. Acquah ◽  
Brian K. Via ◽  
Oladiran O. Fasina ◽  
Lori G. Eckhardt
Keyword(s):  
Fourier Transform ◽  
Chemical Composition ◽  
Ftir Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Fourier Transform Infrared ◽  
Least Squares Regression ◽  
Forest Logging ◽  
Logging Residue

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e.,R2> 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

Prediction of Long-Residue Properties of Potential Blends from Mathematically Mixed Infrared Spectra of Pure Crude Oils by Partial Least-Squares Regression Models

2009 ◽  
Vol 23 (4) ◽  
pp. 2164-2168 ◽  
Author(s):  
Peter de Peinder ◽  
Tom Visser ◽  
Derek D. Petrauskas ◽  
Fabien Salvatori ◽  
Fouad Soulimani ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Crude Oils ◽  
Least Squares Regression

Optimization Study of Stepwise Regression and Partial Least Squares Regression Models for Dam Security Monitoring

2014 ◽  
Vol 578-579 ◽  
pp. 1101-1107 ◽  
Author(s):  
Wei Ling Hu ◽  
Nian Wu Deng ◽  
Qiu Shi Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Regression Models ◽  
Stepwise Regression ◽  
Partial Least Squares Regression ◽  
Monitoring Data ◽  
Least Squares Regression ◽  
Security Monitoring ◽  
Optimization Study ◽  
Optimized Model

Both Stepwise Regression (SR) and Partial Least Squares Regression (PLSR) can be applied in data analysis of dam security monitoring, and achieve in fitting and forecasting. However, SR and PLSR models still can be optimized. A variety of programs are studied and compared based on actual dam security monitoring data. The results show that the optimized-model is better in fitting and forecasting the monitoring data.

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