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NIR data based on the theoretical model Acacia Software Design

Author: WangCong
Tutor: ZhangWenJie
School: Beijing Forestry University
Course: Biophysics
Keywords: Acacia Near Infrared Spectroscopy Mathematical model java language
CLC: S781
Type: Master's thesis
Year: 2011
Downloads: 19
Quote: 0
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Abstract


The chemical composition of wood and wood pulp properties are closely related. Using traditional chemical analysis of the chemical composition of the timber operation is complex , time-consuming , human error, is a destructive analysis method consumes a lot of manpower and material resources. Using near infrared spectroscopy (NIR) analysis of this non-destructive testing technology can achieve fast, high efficiency, low cost, good reproducibility test , measurement and easy . This paper summarizes the traditional NIR modeling and analysis of its strengths and weaknesses , explore the NIR technology in the application of wood science , proposed the establishment of a stable , strong anti-jamming capability , suitable for a wide range of wood models of optimum chemical analysis methods that, according to the Lambert - Beer law established by near-infrared spectral data acacia holocellulose predictive model formula , using the Java language developed using near -infrared spectral data to predict the chemical composition of wood a web version of the software V-1.0 . First of all near-infrared spectral data preprocessing and grouped , and then use a lot of non-linear fitting method to establish sub-model , list each absorbance values ??and their corresponding sub-model parameters among the coefficients , using the language of the above Java5.0 data and compiled into a software model formula . The software is easy to use , simple operation, accurate predictions ( eg Holocellulose between measured and predicted values ??of the correlation coefficient is 0.9352 ) . The software for the wood pulp performance analysis, to provide a reliable basis and operational means , and is expected to be used for other species of the chemical composition of the forecast .

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CLC: > Agricultural Sciences > Forestry > Forest harvesting and utilization > Wood Science
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