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Author Hart, F. ♦ Avramidis, S. ♦ Mansfield, S.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Moisture Content ♦ Nir Technology ♦ Principal Component Regression Model ♦ Accurate Reading ♦ 50-mm Thick Material ♦ Strong Statistical Evidence ♦ Predict Lumber Shell Moisture Content ♦ Resultant Model ♦ Post-sorting Kiln ♦ Preliminary Study ♦ Calibration Model ♦ Accuracy Limit ♦ Lumber Shell Moisture Content ♦ Average Moisture Content Prediction ♦ Correlation Coefficient ♦ Different Pre-processing Method ♦ Average Moisture Content ♦ Surface Wet-pockets ♦ Hemlock Lamina ♦ Oven-dry Method ♦ Shell Moisture Content
Abstract The objective of this preliminary study was to assess and develop a method of detecting the presence of wet-pockets in hemlock lamina based on near infrared (NIR) spectroscopy. Three principal component regression models with different pre-processing methods capable of predicting moisture content of hemlock within the accuracy limits of the calibration models were developed. The pre-processing of data to the first derivative resulted in the highest correlation coefficient (0.9855). This means that the use of NIR technology can provide a 99 % accurate reading of the lumber’s shell moisture content within the range from 0 to 27%. The resultant model is expected to give higher average moisture content predictions (they can range between 0 and 2.4%) than the oven-dry method for calculating moisture content of specimens containing wet-pockets located on the shell. Although NIR technology is able to accurately predict lumber shell moisture content, it is less accurate when predicting the average moisture content of 50-mm thick material. Regardless of that, the strong statistical evidence suggests that the technology using NIR-spectroscopy to determine shell moisture content in applications such as identifying surface wet-pockets for post-sorting kiln dried lumber or lam-stock should be developed.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study