Abstract:

The use of near infrared (NIR) spectroscopy coupled with regression analysis prediction method to detect wood biological decay was investigated in this paper. Principal component regression (PCR) analysis and partial least squares regression (PLSR) analysis were compared with the results of extensive research on SIMCA and PLS-DA methods by analysis of the correlation coefficients and the model residuals. The results shown that correlation between the predicted variable of calibration and validation and the measured variable is significant with correlation coefficient (r) over 0.95 with low SEC and SEP (0.07-0.20); the discriminant accuracy for the non-decay, white-rot and brown-rot decay samples are 100% (deviation<0.27) by the PCR and PLSR models based on test set samples; the discriminant accuracy by PLSR is better than that by PCR due to the lower deviation, while both of PCR and PLSR have better discriminant accuracy than that by SIMCA pattern recognition, and has the same discriminant accuracy as PLS-DA method. It’s suggested that NIR spectroscopy coupled with PCR and PLSR analysis prediction methods could be used to rapidly detect wood biological decay.

Key words: near infrared(NIR) spectroscopy, regression analysis prediction method, wood, biological decay, discrimination