NIR measurement of moisture content in wood under unstable temperature conditions. Part 2. Handling temperature fluctuations

Fluctuations in sample temperature cause peak shifts in near infrared (NIR) spectra of moist, solid wood samples, especially when the temperature vari es around 0 degrees C (the freezing point of water). These thermal effects cannot be ignored when NIR and Partial Least Squares (PLS) regression is us ed for determination of the moisture content of mood outside the laboratory . In this paper, a number of different approaches to the problem are invest igated. The approaches may be divided into two different classes according to their basic strategy. One strategy, the soft model strategy, is to repre sent all relevant temperatures in the calibration set and then produce a gl obal model or a set of local models based on raw or pretreated spectra, Thi s strategy does not require knowledge of the structure of the thermal effec ts, but a large calibration set representing all relevant temperatures is n eeded. Three approaches based on this strategy mere tested. The other strat egy, the transformation strategy, is to develop the moisture content model at one temperature and transform spectra recorded at other temperatures to this temperature. If an effective transformation algorithm can be found, th is strategy should require less calibration data, Four new approaches based on the transformation strategy were developed and tested. The three soft m odel approaches gave similar prediction errors (RMSEP) for unknown samples (8-9%, expressed as moisture ratio, i.e. the moisture content in percent of the dry weight), None of the approaches based on the transformation strate gy gave smaller prediction errors than the soft model approaches, but two o f them gave only slightly larger prediction errors (RMSEP) than the soft mo del approaches (9-10% moisture).