There is demand in the Swedish sawmill industry to improve the accuracy of moisture content measurements, both to obtain a better tool to run production and to ensure that the products meet customer expectations. In this study, 240 well-conditioned pieces of Scots pine (Pinus sylvestris), sorted into five different groups by visual inspection, were measured using microwaves and X-rays. Models to predict moisture content of wood were made by measurements of an additional 45 pieces of wood. Using only measured quantities from the microwave system, ie attenuation and phase shift, the root mean square error (RMSE) of the estimated moisture content was 1.00%. By adding total density from the X-ray measurements, RMSE of the estimated moisture content was lowered to 0.89%. Mean errors of the different wood groups varied from -0.65 to 0.18%

Antti, Lena

Oja, Johan

Vikberg, Tommy

English

Wood and Fiber Science (E-Journal)

MOISTURE CONTENT MEASUREMENT IN SCOTS PINEBY MICROWAVE AND X-RAYSTommy Vikberg*PhD StudentDivision of Wood PhysicsE-mail: tommy.vikberg@sp.seJohan OjaAdjunct ProfessorDivision of Wood Science and TechnologyE-mail: johan.oja@norra.seLena AnttiAssociate ProfessorDivision of Wood PhysicsLuleå University of TechnologySE-931 87 Skellefteå, SwedenE-mail: lena.antti@haparanda.se(Received November 2011)Abstract. There is demand in the Swedish sawmill industry to improve the accuracy of moisture contentmeasurements, both to obtain a better tool to run production and to ensure that the products meet customerexpectations. In this study, 240 well-conditioned pieces of Scots pine (Pinus sylvestris), sorted into fivedifferent groups by visual inspection, were measured using microwaves and X-rays. Models to predictmoisture content of wood were made by measurements of an additional 45 pieces of wood. Using onlymeasured quantities from the microwave system, ie attenuation and phase shift, the root mean square error(RMSE) of the estimated moisture content was 1.00%. By adding total density from the X-ray measure-ments, RMSE of the estimated moisture content was lowered to 0.89%. Mean errors of the different woodgroups varied from –0.65 to 0.18%.Keywords: Wood, inline, attenuation, phase shift, knots.INTRODUCTIONHigher production rate and less time from thefelling of trees in the forest to the final product,driven by economic and qualitative factors, haveled to an increased demand during the last fewdecades for accurate and automated measuringdevices. Because wood shows great variations inproperties among individuals and even within thesame individual (Dinwoodie 2000), this task hasproven to be a challenge.Nevertheless, it is impor-tant to obtain as high a value as possible out of thewood to sort the wood to the best designated endproduct and to ensure that the quality demands ofthe end product are fulfilled. Moisture content isone quality factor that is important in the produc-tion chain and in the final use of the wood (Espinget al 2005). Industrial tests of commercial inlinemoisture content meters have shown low accu-racy from individual readings (Esping 2003;Nilsson 2010). All methods for measuring mois-ture content have their pros and cons, and most oftoday’s meters only use one measuring technique(Vikberg 2010). Nilsson (2010) demonstrated thataccuracy of moisture content measurements canbe improved by taking the visual properties ofwood into account. Because sawn wood is oftensorted according to different qualities by visualmethods using parameters such as number ofknots, knot size, and wood defects, it would bestraightforward to use this information togetherwith the moisture content measurements.* Corresponding authorWood and Fiber Science, 44(3), 2012, pp. 280-285# 2012 by the Society of Wood Science and TechnologyMicrowaves have been widely used to predictdifferent wood parameters (Schlemm 2004;Schajer and Orhan 2006). A high microwave fre-quency gives high-resolution measurements, butone needs to be aware of the risk of the phaseshift exceeding 2p (Hansson et al 2005). How-ever, if wood density is known, it is likely thata model will be able to predict the number ofmultiples of 2p that the phase shift has expired.One way to achieve high-resolution density mea-surements of wood is by computer tomography(CT) (Lindgren 1992).This study considers moisture content mea-surements taken with microwaves and combinesthese measurements with density measurementsperformed with a medical CT scanner. The testedmaterial was manually sorted into five differ-ent groups based on its visual properties, andthe potential for increased accuracy in moisturecontent measurements is discussed according tothe results.MATERIALS AND METHODSThe tested material consisted of 195 pieces ofScots pine (Pinus sylvestris) planed on four sidesto dimensions of 44  120  920 mm3 (R, T, z).The pieces were chosen to represent differentkinds of wood, and by visual inspection, theywere divided into the following groups: normal,fine, knot, check, and defect. Each group wasfound in two different moisture content classesconditioned to approximately 13 and 16% MC.Characteristics of the different groups are as fol-lows: the defect group contained large wooddefects, such as top rupture and spike knots; theknot group contained considerably large soundknots; the check group contained checks thatwere easily discovered with the naked eye; thewood classified as fine had very few and smallknots and most of the samples had a high amountof heartwood and an average dry density higherthan the other groups; finally, the normal woodwas chosen to represent the most common woodat a normal production site. From an end user’sviewpoint, the fine group would be suitable forwindow frames, the defect group would be suit-able for packaging, and the other three groupswould normally be used as construction lumber.Typical magnitudes of some of the characteristicproperties of the different groups were measuredand are presented in Table 1.All pieces were measured with Satimo micro-wave equipment (Satimo Microwave VisionSweden, Allingsås, Sweden) using a frequencyof 9.375 GHz (Johansson 2001). The measuredquantities were attenuation and phase shift intwo directions of polarization, corresponding toparallel and cross-grain. The influence of thedielectric properties of the wood on attenuationand phase shift of the microwave was describedby Hansson et al (2005), whereas Schajer andOrhan (2005) described a method to measurethese quantities. Wood density, ru,u, where u ismoisture content of the pieces, was measuredwith a medical CT scanner (Siemens SomatomEmotion Duo; Siemens AB, Upplands Väsby,Sweden), as described by Lindgren (1992).During measurements with the microwave equip-ment, the short ends of the boards were placed onmetal supports, giving rise to some disturbanceTable 1. Typical magnitudes of some characteristics of woodin the different groups.aGroupCL(mm)CW(mm)No. ofKWMax KD(mm)FD(mm)r0,u(kg/m2)Normal 0 0 1.7 23 0 400Fine 0 0 1.3 7.5 0 450Knot 0 0 2.2 34 0 400Check 430 0.5 1.5 11 0 430Defect 0 0 1.5 21 160 400a Only the central 0.6 m of each board is considered because this was wherethe actual measurement took place.CL, check length; CW, check width; KW, knot whirls; KD, knot diameter;FD, fiber disturbance; r0,u, density of dry wood at MC u.Figure 1. Three characteristic boards from the normal group.Vikberg et al—SCOTS PINE MOISTURE CONTENT MEASUREMENT 281of the measured values within the vicinity of thesupports. Therefore, only data from the central0.6 m of each board were used for further analy-sis. To give an idea of the visual appearance ofboard characteristics, three representative boardsof each group are shown in Figs 1-5. Only theparts of the boards from which data were col-lected, ie the central 0.6 m, are shown.Before the wood pieces were removed from theconditioning chambers, each piece was sealedwith glue at the ends to prevent longitudinaldrying. Additionally, all boards were wrapped inplastic together with other boards from the samemoisture content class to prevent large moisturecontent changes.As a calibration set, 45 pieces conditionedto three different moisture content classes ofapproximately 8, 13, and 16% were used. Char-acteristics of the wood used for calibration werethe same as for the normal group. The calibrationset was kept relatively small to correspond to anindustrial calibration procedure. The moisturecontent, used as the reference, of each individualpiece was achieved using the oven-dry method asstated in CEN (2002).Measured data were analyzed by constructinga partial least square regression model usingFigure 2. Three characteristic boards from the fine group.Figure 3. Three characteristic boards from the knot group.Figure 4. Three characteristic boards from the check group(checks are outlined).Figure 5. Three characteristic boards from the defect group.(Note large areas of grain deviations.)Figure 6. Predicted moisture content (MC) using a modelbased on measured microwave attenuation and phase shift.Stars represent calibration boards, and circles representboards for which MC is predicted. The equation in the plotis a linear least square fit to the prediction set.282 WOOD AND FIBER SCIENCE, JULY 2012, V. 44(3)SIMCA (Eriksson et al 2006). Multivariatedata analysis has already shown great potentialand has been widely used in wood research(Danvind 2002; Lundgren and Hansson 2007).Two principal components were used in themodel to span the space to describe significantrelationships in the data. The 45 calibrationboards were used for constructing the model,and prediction of moisture content for theremaining 195 pieces was analyzed. In the firstmodel, the measured phase shift and attenua-tion in the two directions of polarizationby means of microwaves was used; the sec-ond model also included mean density fromCT measurements.RESULTSResults from the model predicting moisturecontent from the measured attenuation andphase shift in the two directions of polarizationby means of microwaves are shown in Fig 6.The calibration set in Fig 6 had a coefficientof determination (Montgomery et al 2004), R2,of 0.92 and a root mean square error (RMSE) of0.90%. The prediction set had an RMSE of 1.00%and, as can be seen in Fig 6, an R2 of 0.68.To obtain more accurate moisture content mea-surements, measured wood density, ru,u, fromthe CT was used together with the microwavemeasurements (Fig 7).Linear regression of the calibration boards inFig 7 had an R2 value of 0.94 and an RMSEof 0.77%. The different slopes of the regressionlines of the calibration and prediction set showthat the calibration was not suitable for allpieces of wood. RMSE of the estimated mois-ture content was 0.89%.To gain an idea of possible improvements in mois-ture content measurements by also using an opti-cal device, mean error and RMSE are presentedfor the five different groups of wood (Table 2).As seen in Table 2, RMSE was the smallestfor the most valuable wood, ie fine group.Figure 7. Predicted moisture content (MC) using a modelbased on measured microwave attenuation and phase shiftwith density from computer tomography measurementsincluded. Stars represent calibration boards, and circles rep-resent boards for which MC is predicted. The equation inthe plot is a linear least square fit to the prediction set.Table 2. Mean error and root mean square error (RMSE) of moisture content measurements for the five different groupsof wood.aGroup "MW "MW;CT RMSEMW RMSEMW;CT RMSEMW RMSEMW;CTNormal –0.65 –0.55 1.06 0.85 0.84 0.64Fine 0.18 –0.21 0.64 0.61 0.62 0.58Knot –0.36 –0.37 1.06 0.95 1.00 0.87Check 0.17 –0.33 1.03 0.84 1.02 0.78Defect –0.31 –0.59 1.12 1.13 1.08 0.97All wood –0.20 –0.41 1.00 0.89 0.92 0.78a Values are shown when only microwave measurements were used and when they were combined with computer tomography (CT) measurements. The twolast columns show RMSE after subtracting the mean error for each group, ie the best possible result if combining the measurements with a visual system.Vikberg et al—SCOTS PINE MOISTURE CONTENT MEASUREMENT 283DISCUSSIONRMSE was decreased by adding density measure-ments determined by CT scanning to the micro-wave measurements. Adding a third measurementtechnique would presumably further improve theresults. Because the mean errors differ among thewood type groups, a visual system would alsoimprove overall accuracy. If the measurementsof a board were taken along the whole length ofthe board, one could filter out regions in whichthe signal is stable, which implies that no dis-turbances such as grain deviation or knots werepresent. In most cases, however, the boards arecross-fed through the final sorting stations in whichthe moisture content should be measured. In thiscase, connecting a visual system to the moisturecontent meter would be beneficial for detectingobjects that are within the meter’s measuringrange, thus affecting the measured amount.In the prediction set, there were two points withconsiderably low reference moisture content. Thisresult was strange because these boards wereplaced in the same climate chamber as the restof the pieces in the 13% MC class. There may besome errors in the reference values for those twoindividuals; excluding them from the predictionset would, however, not produce a remarkablechange in the accuracy of the prediction model.In this study, board thickness was not used as aparameter in the model because the boards wereplaned to the same dimensions. This step shouldnormally be done because measured microwavevalues are related to wood and water surfacedensity (kg/m2), ie the thicker the board, thegreater the attenuation and phase shift. A largeattenuation caused by high-density wood, highmoisture content, or thick boards limits the useof the described system. Generally, the limit isa moisture content of approximately 20% fora 50-mm-thick board with a dry density of500 kg/m3. The vicinity of the board’s edgeswill also cause diffraction of the field, and mea-surements originating from those areas will bedifficult to interpret correctly.An industrial calibration procedure could be sim-plified if only one board dimension could be usedtogether with preprogrammed correlations forother dimensions. This is concluded because therewere problems with the calibration in this study,although only a single dimension with well-defined wood was used. Calibration procedurescould also be simplified by using calibrationdummies with well-defined dielectric properties.In summary, this study shows that the accuracyof moisture content prediction was increased bycombining microwave measurements with CTmeasurements. Mean error also differed amongthe wood type groups, showing the potential tofurther increase the measurement accuracy byadding a visual system.REFERENCESCEN (2002) EN 13183-1. Moisture content of a piece of sawntimber. Part 1: Determination by oven dry method. Euro-pean Committee for Standardization, Brussels, Belgium.Danvind J (2002) PLS prediction as a tool for modelingwood properties. Holz Roh Werkst 60(2):130-140.Dinwoodie JM (2000) Timber: Its nature and behavior.Spon Press, London, UK.Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J,Wikström C, Wold S (2006) Multi and megavariate dataanalysis. Umetrics AB, Umeå, Sweden.Esping B (2003) Test av kommersiella fuktkvotsmätarein-line. Trätek rapport Trätek, Stockholm, Sweden[in Swedish].Esping B, Salin J-G, Brander P (2005) Fukt i trä förbyggindustrin. Trätek, Stockholm, Sweden [in Swedish].Hansson L, Lundgren N, Antti L, Hagman O (2005) Micro-wave penetration in wood using imaging sensor. Mea-surement 38(1):15-20.Johansson J (2001) Property predictions of wood usingmicrowaves. Licentiate, Luleå University of Technology,Skellefteå, Sweden.Lindgren O (1992) Medical CT-scanners for non-destructive wood density and moisture content mea-surements. Doctoral, Luleå University of Technology,Luleå, Sweden.Lundgren N, Hansson L (2007) PLS models for predictingwood properties from microwave measurements based onthree dimensional FEM simulations. Proceedings, Nordicworkshop on wood engineering, Luleå University ofTechnology, Skellefteå, Sweden.Montgomery DC, Runger GC, Hubele NF (2004) Engineer-ing statistics. John Wiley & Sons, Hoboken, NJ.Nilsson M (2010) Evaluation of three in-line wood moisturecontent meters. MS thesis, Luleå University of Technol-ogy, Skellefteå, Sweden.284 WOOD AND FIBER SCIENCE, JULY 2012, V. 44(3)Schajer G, Orhan F (2005) Microwave non-destructivetesting of wood and similar orthotropic materials.Sensing and Imaging: An International Journal 6(4):293-313.Schajer GS, Orhan FB (2006) Measurement of wood grainangle, moisture content and density using microwaves.Holz Roh Werkst 64(6):483-490.Schlemm U (2004) Mass and moisture measurement by useof the microwave resonator technique. Technical bulletin,Tews Elektronik, Hamburg, Germany.Vikberg T (2010) Fuktkvotsmätare för träindustrin: Enkartläggning av metoder för mätning av fuktkvoteri intervallet 7-18 fuktkvotsprocent. Technical report, LuleåUniversity of Technology, Skellefteå, Sweden [in Swedish].Vikberg et al—SCOTS PINE MOISTURE CONTENT MEASUREMENT 285

Moisture Content Measurement in Scots Pine by Microwave and X-Rays

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Moisture Content Measurement in Scots Pine by Microwave and X-Rays

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