Estimation of Moisture Content and Density of Frozen and Unfrozen Wood Using Near Infrared Spectroscopy

1.0 Introduction

Western hemlock is the single most plentiful tree species on the coast of British Columbia and makes up 18% of the volume of BC’s total growing stock. It grows along both the east and west sides of the coast range, from sea level to mid-elevations, as well as in the Interior wet belt west of the Rocky Mountains. It seldom grows in pure stands, and is usually mixed with Douglas-fir, amablis fir, sitka spruce, and western red cedar and can regenerate well under a closed canopy. Hemlock has a considerably moderate strength, stiffness and large shrinkage (Kenneth, 2001) Hemlock is a medium-density wood which is readily available in BC and used for constructional purposes.

Balsam fir (Abies balsamea [L.]Mill.) is one of the important commercial timber species in North America with its predominant use as pulp. However, it is also an important lumber species which is primarily marketed with spruce (Picea spp.) and in the spruce-pine-fir (SPF) grades (Mullins and McKnight, 1981). The physical characteristics of balsam fir lumber makes it to be well suited for use in producing high quality 2' x 4' and 2' x 6' studs for use in light frame building constructions. Although, customer acceptance and satisfaction with balsam fir studs in some market areas where balsam fir is regularly available as an individual species has been reported (Bendtsen, 1974; Carpenter & Quinney, 1965; Govett, 1982; Govett & Sinclair, 1981). Yet, the utilization of balsam fir sawn-timber is generally below the potential of the resource in eastern North America. In Minnesota, for example, about one-third of the net annual growth of balsam fir sawn-timber is utilized; however, only 6% of this growth winds up at the saw-mill with the remainder primarily used for pulp (Bowyer et al., 1982). Softwood dimension lumber is certainly one product which could potentially lead to increased amounts of balsam fir.

Manufacturing processes in the wood industry requires optimization which involves having knowledge about collection of data better and faster, optimization in the forest products industry needs accurate real-time knowledge about products properties and characteristics. Moisture content and density which affects many physical and mechanical properties of wood are among the most important properties that requires monitoring both in plant and in the sawmill. The change in the moisture content of the material causes significant change in the physical and mechanical properties. Accurate estimation of the moisture content and density of wood will allow for proper utilization especially where weight and transportation cost is required such as in building construction. The moisture content and density of wood are currently poorly monitored, largely due to lack of suitable and rapid measurement methods. Although, attempts have been made to monitor moisture content and density of wood using classical laboratory methods which are time-consuming and expensive to operate. X-ray based instruments such as SilviScan-1 (Evans, 1994), SilviScan-2 (Evans, 1999), and computed tomography (CT) scanners (Schmoldt et al. 1999) have been designed to rapidly measure moisture and density of both hardwoods and softwoods. The limitation to these equipment’s is that they are laboratory-based and not designed for industrial environment, therefore a suitable alternative method which requires minimal sample preparation is Near-infrared spectroscopy which can be used both indoors and outdoors.

NIR spectroscopy is the measurement of the wavelength and intensity of the absorption of near-infrared light by a sample. NIR light spans the 800 nm - 2500 nm range and is energetic enough to excite overtones and combinations of molecular vibrations to higher energy levels. NIR spectroscopy is typically used for quantitative measurement of organic functional groups, especially O-H, N-H, and C-O. However, NIR spectroscopy has advantages which includes minimal sample preparation, fast acquisition times, non-destructive sampling, and the potential for on-line or portable applications. The efforts to lower the cost, time and accuracy involved in wood property analysis have seen the development of rapid, non-destructive assessment methods like NIR. Many studies have explored near-infrared (NIR) spectroscopy as an alternative approach to traditional methods of wood property analysis (Tsuchikawa, 2007).

Adedipe & Dawson-Andoh (2008) examined the feasibility of using NIR spectroscopy to predict moisture between 0.3 and 80% in yellow-poplar (Liriodendron tulipifera L.) veneer sheets. Employing a reduced spectral range (wavelength 1400 to 1900 nm), a region between the two main hydroxyl absorption peaks, gave higher predictive abilities compared to other wavelengths. Also, calibrations was developed for the prediction of moisture in the hygroscopic range in Norway spruce (Picea abies), and the authors concluded that NIR spectroscopy is very versatile for such application (Hoffmeyer and Pedersen, 1995). Thygesen & Lundqvist. (2000a, 2000b) reported the thermal effect on NIR measurement of moisture content in Norway spruce. The results showed that it could not be ignored when NIR combined with partial least square regression was used for determination of moisture under the temperature range between −20 and +25°C.

Watanabe et al. (2010) recently demonstrated that NIR could accurately detect wet-pockets on the surface of kiln-dried western hemlock (Tsuga heterophylla). It was also shown that surface moisture content (5–105%) could be predicted in real time at relatively rapid line speed (up to 1 m/s) without averaging NIR scans into a single spectrum, thus making the system very robust and flexible. Watanable et al. 2012 also developed a PLS regression model that was used to estimate the moisture content of Abies lasiocarpa, the model showed high R2 values of 0.94 and 0.94 for the calibration set and the validation set, respectively; RPD was over 2.5 in both calibration and validation set, indicating that the PLS regression model is useful to sort green lumber by moisture content before drying.

Watanable et al. (2011) also proved that there was a good agreement and excellent moisture predictability, demonstrating the resultant PLS regression model which can predict moisture content of western hemlock ranging from 35% to 105%; the PLS regression model developed also succeeded in predicting moisture content within the range of basic density (298–508 kg/m3) covered by the calibration set, demonstrating that NIR spectroscopy has the advantage of measuring moisture content without the need to correct for wood density. Alves et al. (2010) calculated PLS-R models for wood density based on X-ray micro-density data for each species Pinus pinaster and Larix × eurolepis and for both species together, the common model provided a residual prediction deviation (RPD) of 3.1, and the single models for Pinus pinaster and Larix × eurolepis provided RPDs of 3.5 and 3.2 respectively. Inagaki et al. (2010) has also developed a PLS-R model for the prediction of air-dry density of Eucalyptus camaldulensis with an RPD of 3.8. In their experiment, they proved that the model is quite robust and stable by removing 40% of the samples during the cross-validation step and ended up with an RPD of 3.2.

As reviewed by Leblon et al. (2013), most NIRS studies on wood moisture and density estimation were carried out on strips, wood disk and small clear specimens with little report on lumber which could be suitable for industrial applications. Only one study was found in this review that considered lumber for western hemlock and no report was found for balsam fir. Watanabe et al. (2011) reported the results of moisture-based sorting of hem-fir lumber using NIR.

In this study, NIR will be used to estimate the moisture content and basic density of western hemlock and balsam fir dimension lumber which could be used for structural applications. Dimension lumber is softwood lumber that is nominally 2 inches thick and of various lengths and widths which is mostly used for wood-based housing construction in North America. Partial least square (PLS) regression models will be developed for the moisture content and density of western hemlock and balsam fir. This research will provide information on the estimation of moisture content without the need to correct for density as opposed to other commercial meters that needs correction for density to get accurate values and the knowledge will help the industry in accurate estimation of moisture content and density after drying of lumber.

1.1 Objective of the Study

The objective of the study is to successfully develop PLS models that can be used to predict the moisture content and density of Western hemlock and Balsam fir independently without correcting for density of the species. Also, comparison will be made in the wood property calibrations based on NIR Spectra from different sections; radial, tangential and transverse sections which will help to understand the best section of wood for accurate estimation of moisture content and density for these species.

1.2 Statistical analysis

The spectral data from samples will be labeled SAMPLE_A, SAMPLE_B and SAMPLE_C that is, the radial, tangential and transverse section respectively. Furthermore, the sapwood and heartwood spectral data from the SAMPLE_A will be split randomly into the calibration and prediction sets, consisting of 60 and 40 samples. In the case of SAMPLE_B, samples will also be split randomly into the calibration and prediction sets, consisting of 60 and 40 samples, respectively. Finally, SAMPLE_C will also be split into calibration and prediction set consisting of 60 and 40 samples.

Each sample will be sectioned into different wood type, namely sapwood and heartwood, and three wood surfaces (tangential, transverse and radial) will be prepared. There will be 12 combinations for this study (two species x two wood type x three wood surfaces) and 100 replications for each combination. Therefore, a total of 1200 samples will be prepared.

Samples of each combination will then be divided into two sets of data according to the sample set partitioning based on the joint x-y distance (SPXY) algorithm (Galvão et al., 2005). Three-fifths (60 samples in each combination) will be used as the calibration set, and the remaining two-fifths (40 samples) will be used as the prediction set. Thereafter, the samples in calibration and prediction sets of each combination will be merged into the final calibration (720 samples) and prediction (480 samples) sets, respectively

Sample A Sample B Sample C

Sapwood Calibration Set 60 60 60

Prediction Set 40 40 40

Heartwood Calibration Set 60 60 60

Prediction Set 40 40 40

Sampling Preparation

Kiln-dried lumber (2” x 4”) of Western hemlock (Tsuga heterophylla), and Balsam fir (Abies balsamea) will be procured from a local mill in Vancouver, BC. NIR spectral information will be captured from all three surfaces, offering a range of grain orientations including radial, tangential and transverse section. In addition, two different species and two types of wood (sapwood and heartwood) will be considered; resulting in a total of twelve combinations (two wood species x three orientations × two wood types) for a total of 1,200 samples (100 for each combination).

In order to prevent the specimens from being dominated by the properties of either the early-wood or late-wood, a portion of the specimens will be cut in the radial direction. By cutting the specimens radially, there was approximately an even amount of both earlywood and latewood present since these cell structures have properties that influence other wood properties, specimens will also be cut in the tangential and transverse direction.

Determination of MC and Basic Density

The amount of cell wall material in wood could be measured by basic density, which is a key indicator of wood quality (Panshin & DeZeeuw, 1980). Defo et al. (2007) found relatively good prediction models for the basic density of never-dried red oak. Schimleck et al. (2003) were able to develop good NIRS prediction models for the density of both dry and green loblolly pine, although the fact that the models differ suggests that MC will be a confounding factor in prediction of the density of wood. Although, there are several different methods used to measure wood density, the standard way is to calculate the ratio between the dry weights of wood divided by the green volume of the same wood. This is often referred to as the basic density (Tsoumis, 1991). So far, the number of published models to assess wood density fulfilling the RPD criteria is low. The precise synchronization between the spot used for spectral acquisition and the corresponding density were deemed to play an important role in obtaining these models with RPD values above 2.5. (Alves et al. 2010; Hein et al. 2009). The density of wood is not a fixed value but varies with MC and its mass also changes because the wood swells or shrinks depending on adsorption or desorption. It also varies from species to species. For this reason, the ability to simultaneously predict density and MC is important for the wood industry. To do this, a non-destructive wood inspection tool is highly desirable such as Near Infrared Spectroscopy.

According to the ASTM D2395-17 Standard Test Method (ASTM Intl. 2017),

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Basic Density (ρbasic)=mo/VM*100

Where ρbasic is the basic density (g/cm3), mo is the oven-dried mass (g), and VM is the volume (cm3) of conditioned samples.

2.3 Near-Infrared Measurements

Calibrations will be developed for each wood property by partial least squares (PLS) regression analysis. Methods to be used for preliminary examination of the data includes smoothing the spectral data, standard normal variate, and second-derivative transformation. The measurements will be performed on the radial, tangential and transverse face after completion of tests.

NIR spectra of wood samples will be acquired by an NIR system which consisted of a light source, an optic fiber, a fiber spectrometer (Quality Spec® Pro, Analytical Spectral Devices Inc. Boulder, CO, USA), sample holder, as well as a computer, as Figure 1 shows. The optic fiber connected to the spectrometer, will be oriented at 62º above the sample surface. The samples will be illuminated with a tungsten halogen bulb (ASD Pro Lamp) oriented perpendicular to the sample surface and the distance between the sample surface and the bulb will be 155 mm resulting in NIR spot areas that were approximately 25 mm in diameter. All samples will be scanned in the full wavelength range of 350 - 2500 nm at intervals of 1nm. A piece of commercial micro-porous Teflon will be chosen as the reference material, and reference spectra will be measured and stored prior to spectra collection. Two spectra will be collected from both the upper and lower surface of each sample that will be averaged into a single spectrum. Spectrometer parameters settings, and spectra data collection and storage will be carried out via software RS3 (Analytical Spectral Devices Inc. Boulder, CO, USA). Due to multiple scans to be taken, it will prove more time efficient to set up an automatic timer that would scan the specimen within a specific time interval.

Figure 1. Schematic diagram of the NIRS system

Table 1: Experimental Layout

Factor B (Wood Type) Orientation Factor A (Wood Spp)

Sapwood Hemlock (H) Balsam fir

T HS-T BF-T

R HS-R BF-R

TR HS-TR BF-TR

Heartwood T HH-T BF-T

R HH-R BF-R

TR HH-TR BF-TR

Table 2: Sample Categorization

Sample Categorization

Species Wood Type Wood Type / Orientation Sample Size

Hemlock Sapwood HS-T, HS-R & HS-TR 300

Heartwood HH-T, HH-R & HH-TR 300

Lodgepole pine Sapwood LPS-T, LPS-R & LPS-TR 300

Heartwood LPH-T, LPH-R & LPH-TR 300

Total 1200

2.0 References

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