Wood failure evaluation is the key criterion for predicting the long-term durability of plywood. At present, the conventional visual method for plywood wood failure evaluation is slow and subjective. Evaluations can be influenced by factors such as: room lighting, wood species, sample treatment, and readings from prior samples. An automated wood failure evaluation system using image analysis techniques could potentially be programmed to consider all the variables and respond with consistent wood failure values regardless of the machine operator's experience level. This report describes the results of a six-month study in which a system for automated plywood wood failure determination was compared with conventional visual wood failure evaluation. It was built upon research undertaken in the 1996/97 year in which the feasibility of the approach was initially established. In the research reported previously, a colour optical imaging system was assembled and suitable wood failure algorithms were compiled with promising results. The imaging system was 100 % effective in reproducing sample values. The data were discussed with the project liaisons and a three-month comparison with Canply readings was suggested. In this study, machine evaluation of 4,150 samples was compared with readings of monthly plywood mill quality control samples. The sampling was designed to include all British Columbia plywood mills and all categories of commercial plywood production. The differences in average values for wood failure between human and machine evaluation were found to be less than plus or minus 5% in the majority of cases. In addition, 93 % of ‘set average' readings fell in the plus or minus 10% range of deviation expected of human wood failure readers. Agreement on readings of individual samples within each set was not quite as good with 72% falling in the plus or minus 15% range.
Wood failure evaluation is the key criterion for predicting the long-term durability of plywood. At present, the conventional visual method for plywood wood failure evaluation is slow and subjective. Even experienced evaluators can show significant differences in their evaluations on the same plywood specimen and an individual evaluator can make different wood failure estimates on the same specimen at different times. Differences among evaluators can be as high as 50% for some samples. Evaluations can be influenced by room lighting, the wood species, sample treatment, and readings from prior samples. An automatic wood failure evaluation system using image analysis techniques could potentially be programmed to consider all the variables and respond with consistent wood failure values regardless of the experience level of the machine operator. This report describes the results of a one-year project in which a system for automatic plywood wood failure determination was investigated. A color optical imaging system was assembled and the preliminary work of compiling suitable algorithms was completed with promising results. The imaging system was 100% effective in reproducing individual sample values. Samples were sorted according to plywood type and test method to develop appropriate program algorithms for each category. The wood failure program was then further developed to automatically detect wood species and test method, thus avoiding the need for specimen separation prior to evaluation. Based on nearly 1200 samples in four categories, the differences in average values of wood failure between human evaluation and machine vision were found to be less than plus or minus 5%. In addition, a minimum of 85% of individual machine readings fell in the plus or minus 15% range of deviation expected of human wood failure readers. The imaging system was more accurate for light-colored specimens (i.e., Canadian Softwood Plywood) than darker-colored specimens (i.e., Douglas fir ) and for specimens where resin had been applied by spray. In order to make the imaging system more reliable and robust, the algorithm parameters now need to be fine-tuned based on a larger sample database.
On February 13-14, 2001 a Near Infra-Red (NIR) sensor was tested at an OSB mill to measure liquid PF resin level of furnish on two forming lines. This was the final step of the Forintek project titled “Online Measurement of Resin Distribution in OSB”. The NIR sensor was a commercially available NIR moisture gauge modified to measure liquid PF. An earlier pilot plant study  showed that this modified gauge could work in online situations to show trends in resin level using either liquid PF, powder PF or MDI (isocyanate). In this study, the sensor was not calibrated and gave measurements in signal strength units only. This data was later calibrated using a trend equation relating signal strength values to resin level based on pilot plant trials.
In the mill, the sensor was set-up at two separate locations including the top and bottom face forming lines. Once set up and logging data, the mill made several changes to the resin level from 4-5%. The results clearly showed a good correlation between sensor data and the corresponding resin level changes.
To further test the sensor, the mill changed the solids content of the resin from 55% to 45%, while maintaining the same resin application level, to determine whether the resulting moisture content change on the furnish would affect sensor readings. This caused the sensor signal to significantly drop in value, roughly equivalent to a change of one percentage level, and showed that the sensor is vulnerable to changes in moisture content. In addition, the earlier pilot plant study showed that changes in furnish species and fines content could affect readings as well. As discussed in the earlier pilot plant evaluation report, it may be possible to eliminate the influence of non-resin furnish conditions if these factors could be singled out and subtracted from the measurement data. One suggestion is to mount a second sensor before the blender (no resin condition) to obtain base-line data. Many mills already have NIR moisture gauges mounted on the dry bins which could possibly be modified for this purpose.
This study has shown that NIR sensor technology has the capability to measure the level of adhesive resin in furnish under online mill conditions. Further work is recommended to eliminate the sensor’s sensitivity to other (non-resin) variables in the furnish.
On April 1, 2000 the Forintek project titled "Online Measurement of Resin Distribution" was established. Up until that time Forintek had investigated the effects of resin distribution on OSB properties and had developed an off-line instrument (GluScan) for measuring resin distribution. Ideally, however, a more practical instrument for mills would be one which provided continuous, online measurement. Recently a company named Process Sensors Corp. has been producing a commercial NIR sensor specially modified for measuring resin level in OSB, much the same way NIR moisture sensors are used today in OSB mills to measure moisture content. For the purposes of this project, Forintek agreed to evaluate the Process Sensors unit to determine the suitability of NIR technology for measurement of resin in OSB furnish.
Tests were done mainly at the Alberta Research Council (ARC) and Forintek in Vancouver. Some additional blending was done at the Borden Chemical lab facility in Vancouver. Under ideal conditions, test results demonstrated that the NIR sensor data provides good trending of resin level in furnish blended with either PF liquid, PF powder or MDI. For each of these resin types, the NIR sensor gave readings to within approximately ± 2% resin levels. Because of this accuracy range, the sensor would appear more practical for PF liquid applications which generally operate with higher resin levels than MDI or powder PF.
Further testing, however, showed that the NIR is sensitive to changes in furnish conditions, other than resin, which can interfere with measurements of resin level. These factors include furnish species, moisture content and fines content. Changes to these conditions can cause the NIR sensor to provide erroneous data. To help compensate for these variable conditions, tests showed that NIR data can be corrected by subtracting measurement values for control furnish (0% resin). This may be possible to accomplish in a mill by using two NIR sensors; one to measure control furnish before the blender and one after the blender or on the forming line to measure resin level of furnish.
Additional results showed that the NIR sensor was unable to detect any significant differences in resin level variability between the good and poor blends. Also the NIR sensor showed that it is not capable of measuring PF or MDI when both resins are used in the same furnish.
It is recommended that further pilot plant testing be continued with mill trials to determine whether NIR technology is a practical way to measure resin level online, and whether it can be used in conjunction with the GluScan offline system to measure resin distribution as a full mill quality control program to optimize resin usage.
The objective of this report is to provide a brief overview of what QRT-PCR is, a brief summary of the studies done in wood durability, and protection and a brief description of the two main options to consider when purchasing a real-time PCR thermocycler: single channel or multi-channel for multiplexing reactions. Quantitative real-time polymerase chain reaction (QRT-PCR) is a PCR method with the built in capability to monitor each cycle of the reaction allowing for simultaneous amplification, detection and quantification of DNA as the reaction is being run. This can significantly reduce research time by eliminating the electrophoresis step for detection. In addition, the technique is also more sensitive, enabling detection of minute amounts of DNA. A few groups involved in durability and protection research have successfully utilized QRT-PCR to measure the extent of decay and to make comparisons between the efficacies of different wood preservation treatments. Some potential uses of the technology at FPInnovations include 1. quantifying fungal colonization in bio-assays designed to assess toxic thresholds for wood preservatives and 2. detection and quantification of pests of Phytosanitary concern in different commodities. The addition of real-time capabilities to FPInnovations would broaden our DNA analysis capabilities and strengthen some of our current testing methods. It is recommended that FPInnovations obtain a single channel thermal cycler and develop the methods to measure and compare fungal colonization and decay for some standard testing.