Modeling Wood Crystallinity with Multiple Linear Regression

2011 ◽  
Vol 480-481 ◽  
pp. 550-555
Author(s):  
Yao Xiang Li ◽  
Li Chun Jiang
Keyword(s):  
Infrared Spectroscopy ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Development ◽  
Model Performance ◽  
Chemical Properties ◽  
Nondestructive Method ◽  
And Chemical Properties

The crystallinity of wood has an important effect on the physical, mechanical and chemical properties of cellulose fibers. Crystallinity of larch plantation wood was investigated with near infrared spectroscopy and multiple linear regression. Five typical wave lengths were selected to establish prediction model for wood crystallinity. Full-cross validation was applied to the model development. The model performance is satisfied with prediction correlation coefficient of 0.896 and bias of 0.0004. The results indicated that prediction of wood crystallinity with near infrared spectroscopy and multiple linear regression is feasible, which provides a fast and nondestructive method for wood crystallinity prediction.

scholarly journals Examination of wheat rheological characteristics by near-infrared spectroscopy

2012 ◽  
pp. 99-104
Author(s):  
Éva Kónya ◽  
Zoltán Győri
Keyword(s):  
Infrared Spectroscopy ◽  
Wheat Flour ◽  
Near Infrared Spectroscopy ◽  
Food Industry ◽  
Near Infrared ◽  
Chemical Properties ◽  
Rheological Characteristics ◽  
Physical And Chemical ◽  
Calibration Equations ◽  
And Chemical Properties

Near-infrared spectroscopy has many advantages that make it a widely used analitical method in the different areas, like agricultural and food industry as well. In wheat quality control rheological characteristics of dough made from wheat flour are as important as physical and chemical properties too. In this work we examined rheological properties of wheat flour samples by alveograph, and spectral data of the same samples were collected by FOSS Infratec 1241 instrument. Modified partial least squares analyses on NIR spectra were developed for two alveograph parameter (P/L és W) to get calibration equations.

Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 301-310 ◽  
Author(s):  
Jetsada Posom ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Development ◽  
Volatile Matter ◽  
Ash Content ◽  
Least Squares Regression ◽  
Fixed Carbon ◽  
Bomb Calorimetry ◽  
Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

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