Volume 20
Issue 4
Wood Technology
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES
DOI:10.30825/5.ejpau.29.2017.20.4, EJPAU 20(4), #02.
Available Online: http://www.ejpau.media.pl/volume20/issue4/art-02.html
THE USE OF RELATIVE PERMITTIVITY AND LOSS COEFFICIENT AS PARAMETERS FOR WOOD SPECIES DIFFERENTIATION
DOI:10.30825/5.EJPAU.29.2017.20.4
Katarzyna Pento¶, Deta £uczycka, Tobiasz Wysoczański
Institute of Agricultural Engineering, The Faculty of Life Sciences and Technology, Wroc³aw University of Environmental and Life Sciences, Poland
Electrical and dielectric wood properties are used in many
applications. In this work, the complex impedance of seven wood species was measured
for frequency range 1 kHz–1 MHz. The measurements were conducted with
parallel and perpendicular orientation of the electrical field with respect to
specimen visible grain cut from sapwood and heartwood. Based on the complex impedance,
values of relative permittivity and loss coefficient were calculated. These parameters
of various wood types differs significantly below 10 kHz for relative permittivity
and below 200 kHz for loss coefficient. For wood samples classification, the
relative permittivity values measured at frequency 5.1 kHz and the loss coefficient
values measured at frequency 110 kHz were used in this work. Three different
classification methods were employed for clustering. Results show that relative
permittivity looks more promising parameter for wood species differentiation.
Key words: dielectric wood properties, relative permittivity, loss coefficient, clustering, wood classification.
INTRODUCTION
Wood is a natural material widely used for its versatility and strength in construction, for furniture manufacturing and as biomass in combustion process. It is a light material with low thermal conductivity, it can be easily treated, but it has some disadvantages, namely, non-humidity-resistance, and can decay and deform [26]. The main components of wood are cellulose, lignin and hemicellulose. It is porous material with porosities ranging from 50 to 80 %vol. There is significant difference between hardwood and softwood microstructure. The open microstructure of softwoods generally results in its higher volume porosity than hardwoods [10]. Several factors affect electrical and dielectric wood properties: wood species, moisture content, density, temperature, chemical properties, fiber direction and field frequency [3, 4, 7–9, 12, 19, 28]. Therefore, these parameters can be used for nondestructive and accurate measurements of wood features. Depending on frequency, electrical and dielectric wood properties have been used in a variety of applications such as moisture content or thickness measurement [20, 31, 32], defects detection [29], strength characteristics estimation and dendrochronology [21]. Forrer and Funck [11] reported that the dielectric properties of wood may be useful parameters for dielectric-based scanning of wood surfaces [11]. It was reported by Tiitta et al. [33] that complex impedance was sufficient to distinguish between the samples from the brown-rot resistant and susceptible Scots pine trees [33]. According to the literature review, there is still lack of research concerning both, dielectric properties of wood at the frequency below 1 MHz and the possibility of the practical use of these parameters. Therefore, in this research, relative permittivity and loss coefficient of wood measured at low frequency were investigated as parameters for wood species identification. For this purpose, different clustering methods were used.
Clustering methods have been applied to a wide variety of research fields, such as agriculture, medicine, biology and many others. This technique is used to classify objects or samples into relatively homogenous groups called clusters. Wide range of clustering algorithms are presented in scientific reports and employed in real-world applications. Hierarchical clustering was used by Goncalves et al. [13] to determine the division of the state of Pará (Brasil) into homogenous groups according rainfall data [13], by Lin et al. [24] to characterize the aberrantly hypermethylated regions that are highly associated with breast cancer [24], by Vernone et al. [34] for human protein cluster analysis using amino acid frequencies [34]. Clustering techniques literature shows that other algorithms, called artificial intelligence methods, are also used by researchers. Adaptive resonance theory neural network models ART2 can surpass the dilemma of plasticity and stability of the existing clustering models. Therefore, this technique has many interesting applications, e.g., for group selection of interim product in shipbuilding [36], for enhancing metrology data-quality evaluation [18] or for clustering of multiple web objects for qualitative web prefetching [16]. Fuzzy clustering is an objective function based method which assigns each data point to more than one cluster with the degree of membership. This method was recently used for detection of masses and macro calcification in mammogram images [23], for the segmentation of brain tumor [2] and for magnetic resonance image brain segmentation [1].
MATERIAL AND METHODS
Wood samples
Seven native softwoods and hardwoods were included in the tests. Samples parameters
are detailed in Table 1. As is presented in Table 1, specimens with relatively
narrow range of moisture content were chosen for the measurement because dielectric
wood parameters differ according to moisture content. The moisture content was
measured by means of Brookhuis Micro-Electronics FME moisture meter. Taking into
account the results reported by Goreshnev et al. [15], it can be stated that
the differences in moisture content presented in Table 1 have insignificant influence
on both, relative permittivity (several %) and loss coefficient (few %).
Table 1. Wood species used for determination of dielectric parameters |
For each wood species, four rectangular specimens (60 mm width × 60 mm length × 15 mm thickness) were cut from sapwood and heartwood, with the thickness either in the tangential or longitudinal direction of wood fiber. The surfaces of specimens adjacent to electrodes were oriented parallel or perpendicular with respect to visible grain. Depending on electrical field orientation with respect to the visible grain and specimen origin, specimens were labeled as 1A, 1B, 2A and 2B what is shown in Figure 1.
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Fig. 1. The method of specimen labelling |
For each specimen, the complex impedance was measured at a room temperature and at frequency ranging from 1 kHz to 1 MHz. The ATLAS 0441 HIA apparatus with parallel plate electrodes was used for the measurements. A good contact between wood surface and electrode was ensured by smoothing a surface with a sand paper. Based on complex impedance values and electrode dimensions, two parameters that characterize wood material depending only on frequency: a relative permittivity (epsilon’) and a loss coefficient (tg delta) were determined as follows:
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where C is capacitance of capacitor with wood sample (F), epsilon0 is permittivity of vacuum (F·m-1), S is upper electrode surface area (m2), h is distance between electrodes (m), omega is angular frequency of electromagnetic field (Hz) and R is resistance of wood sample (Ohm). Values of C and R were calculated based on real and imaginary part of impedance.
Cluster analysis
In this study three cluster analysis methods which are described below were
used.
Joining (Tree Clustering, Hierarchical clustering)
In this research the Statistica 10 software was used for Tree Clustering.
This algorithm groups data into clusters iteratively in order to build a cluster
hierarchy displayed as a tree diagram (dendrogram). At a first step, each object
represents a separate cluster. At each next step, the two most similar clusters
are joined into a new cluster. The horizontal axis of the dendrogram represent
the distance (dissimilarity) between clusters. The vertical axis represent objects.
The algorithm requires the definition of two parameters: a way to quantify the
similarity of two objects (linkage distance) and linkage rule. The most common
metric for calculating distance between two points in Euclidean space is Euclidean
distance. As a linkage rules the following can be used:
- Single linkage (nearest neighbor) where the distance between two clusters is calculated as the distance of the two closest objects from these clusters.
- Complete linkage (furthest neighbor) where the distance between two clusters is determined using the greatest distance between any two objects in these clusters.
- Centroid linkage where the distance between two clusters is calculated as the distance between the centroids of these clusters. The centroid of a cluster is the average point in the multidimensional space defined by the dimensions.
- Average linkage where the distance between two clusters is determined by the use of average distance between all pairs of objects in the two separate clusters [35].
ART2 Neural Network
The ‘RSNNS’ Package
for R software was used for clustering with the use of ART2 neural
network [6]. ART2 (Adaptive
Resonance Theory) network is a kind of non-supervised neural
network. The network is composed of two fully connected layers (in both
directions), the input/comparison layer F1 and the recognition layer F2. The
nodes of F2 layer compete with each other to produce a winning unit. The winning
unit returns the signal to the F1 layer. Then, the similarity between activation
in F1 and input signal is calculated and compared with the vigilance value. Based
on this comparison, weights in the network can be updated or a new node in F2
layer can be produced. ART2 networks offer rapidly learning, adaptation to a
nonstable environment, stability and plasticity. The number of clusters is determined
exactly and automatically.
Fuzzy Analysis Clustering
In this research the ‘cluster’ Package
for R software was used for clustering with the use of Fuzzy Analysis Clustering
[27]. This technique permits each data point to belong to more than one cluster.
A degree of membership to each cluster is specified by a membership coefficient
that range from 0 to 1. Therefore, this method yields much more detailed information
on the data structure than hard clustering methods. On the other hand, this can
also make results interpretation more complicated. During the algorithm the objective
function is minimized:
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where n is the number of observations, k is the number of clusters, r is the membership exponent and d(i, j) is the dissimilarity between observations i and j.
The algorithm is described in details by Kaufman and Rousseeuw [22].RESULTS AND DISCUSSION
A relative permittivity and a loss coefficient of the samples were measured for frequency range from 1 kHz to 1MHz. In Figures 2 and 3 the dependence of relative permittivityand loss coefficient on frequency is presented for samples 1B of all wood species. In plots concerning relative permittivity, the frequency range was reduced for greater readability of the Figure. For higher frequencies, the parameters presented in plots are constant.
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Fig. 2. Effect of frequency on relative permittivity values for samples 1B
of all wood species |
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Fig. 3. Effect of frequency on loss
coefficient values for samples 1B of all wood species |
The differences in both, relative permittivity and loss coefficient between wood species are observed over the entire frequency range. However, the most significant differences appear for lower frequencies (below 10 kHz for relative permittivity and below 200 kHz for loss coefficient). Therefore, for wood samples classification, the relative permittivity values measured at frequency 5.1 kHz and the loss coefficient values measured at frequency 110 kHz were used in this work. Classification was performed separately for relative permittivity and for loss coefficient. Each sample was described by four parameters measured for specimens labeled as 1A, 1B, 2A and 2B.
Three different classification methods were used for clustering because different methods and even different parameters of these methods can cause results dissimilarities.
Joining
The typical result of this method is a dendrogram. In Figures 4 and 5 the
horizontal dendrograms describing clustering results are presented.
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Fig. 4. Cluster dendrogram generated for relative permittivity |
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Fig. 5. Cluster dendrogram generated for loss coefficient |
The results presented in Figure 4 show that, according to relative permittivity, samples of ash, larch and Norway spruce are assigned to separate clusters with a low Euclidian distance (not exceeding 0.1). Higher distance is observed for oak samples (0.33) which are also joined together in one cluster and are different from other wood species. The samples of birch and Scotch pine are assigned to the separate clusters, however, single sweet cherry samples are linked to these groups. Only sweet cherry samples are not linked together into separate cluster. The graph shows a high similarity between birch and ash samples (the distance not exceeding 0.22) as well as between Scotch pine, larch and Norway spruce (the distance not exceeding 0.17).
The results shown in Figure 5 suggest that, according to loss coefficient, only samples of larch and Norway spruce are assigned to the separate clusters representing single wood species. Samples of ash and oak are linked into separate cluster, however, they are joined with other wood species.
ART2 Neural Network
The results
of clustering by ART2 for both, relative permittivity and loss coefficient are
presented in Table 2. One of ART2 parameters is a number of clusters. In this
research, it was set as eight.
The results presented in Table 2 differ slightly from these obtained by joining method. According to relative permittivity, samples of oak, ash and larch are assigned to the separate clusters. The samples of birch and Norway spruce are linked together. One sample of birch, two samples of sweet cherry and three samples of Scotch pine are connected into one group. In the case of loss coefficient as a clustering parameter, oak samples are connected with Scotch pine and ash samples are connected with Norway spruce. Scotch pine samples are joined together into separate cluster. Birch and sweet cherry samples are assigned to different groups.
Table 2. The results of clustering by ART2 |
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Fuzzy Analysis Clustering
This method, in addition to the information
about cluster number set for certain sample, gives also a degree of membership
to this cluster. In Table 3 and 4, the results for relative permittivity and
loss coefficient as a clustering parameter are presented. Since the number of
groups is the parameter of Fuzzy Analysis Clustering method, in this work it
was set as eight. As results of the method, the membership coefficients (in %,
rounded) for each of eight clusters is presented, as well as the number of group
with the highest degree of membership (“Cluster
of the sample” column).
Table 3. Fuzzy Analysis Clustering results for relative permittivity |
Table 4. Fuzzy Analysis Clustering results for loss coefficient |
According to the data presented in Table 3, when relative permittivity is a clustering parameter, results are similar to these obtained with the use of other methods. Samples of ash, larch, Scotch pine and Norway spruce are grouped into separate clusters with quite high membership coefficients (49–82% for ash, 48–81% for larch, 39–81% for Scotch pine and 77–93% for Norway spruce). Birch samples are connected with two samples of sweet cherry, and in the case of oak, only three samples are joined together in separate cluster.
In the case of loss coefficient as a clustering parameter, ash, larch and Norway spruce samples are joined together into separate clusters according to wood species, with quite high membership coefficients (39–79% for ash, 61–86% for larch and 71–92% for Norway spruce). Groups of oak and Scotch pine are composed of only three samples of these wood species. Samples of birch and sweet cherry are assigned to different groups. Similar results were obtained with the use of other methods.
The loss coefficient represents the ability of material to convert electromagnetic energy into heat at a certain temperature and frequency. The relative permittivity shows the ability of molecule to become polarized under the electric field and the differences in relative permittivity between wood species are a result of a different volume porosity. However, according to Hilfer [17], the differences in the actual pore size distribution existing among woods with the same volume porosity may cause slight differences in relative permittivity [17]. A lower relative permittivity is produced by higher porosity. Taking into account that volume porosity is correlated with density, at constant moisture content, wood species with higher density have higher relative permittivity [5]. The density is the characteristic parameter for wood species, therefore in the result of clustering, samples of certain wood species (or wood species with the similar density) should be joined together. In the case of relative permittivity, wood samples were generally grouped according to wood species by all clustering algorithms. Additionally, combining the density values reported by Lis and Rapp [25] with clustering results shown in the dendrogram, it can be seen that there is similarity between birch and ash (the density equals 650 kg/m3 and 750 kg/m3 respectively) as well as between Scotch pine (the density equals 520 kg/m3), larch (the density equals 690 kg/m3) and Norway spruce (the density equals 470 kg/m3). This is in agreement with our previous results [30], where we reported that wood samples classified as similar according to complex impedance are different according to density.
When a loss coefficient was a clustering parameter, less wood species form separate groups (only ash, larch and Norway spruce). These results can lead to assumption that some other physical or chemical parameters which differentiate wood species influence significantly on relative permittivity and loss coefficient.
Other scientific reports prove the potential applicability of electrical wood parameters for wood species differentiation. Gora and Yanoviak [14] pointed out that the resistivity differs among species and growth forms without respect to regional origin of wood. Pentoś et al. [30] reported that complex impedance can be used as a parameter for wood species differentiation.
CONCLUSIONS
Two dielectric wood parameters were used for species separation, namely, relative permittivity and loss coefficient. Three methods of clustering were employed and generally, they produced similar results. Relative permittivity seems to be a better species differentiation parameter because only cherry samples were not joined together into separate clusters according to this parameter. The Tree Clustering method showed similarities between certain wood species. Different similarities between wood species are observed depending on dielectric parameter. In the case of relative permittivity similarity between birch and ash as well as between Scotch pine, larch, and Norway spruce are observed. In the case of loss coefficient larch is similar to Scotch pine. Dielectric wood parameters are correlated with density. Therefore, similarities between wood species of different density can lead to assumption that some other parameters influence significantly on dielectric parameters.
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Accepted for print: 13.10.2017
Katarzyna Pento¶
Institute of Agricultural Engineering, The Faculty of Life Sciences and Technology, Wroc³aw University of Environmental and Life Sciences, Poland
ul. J. Che³mońskiego 37/41
51-630 Wroc³aw
Poland
email: katarzyna.pentos@upwr.edu.pl
Deta £uczycka
Institute of Agricultural Engineering, The Faculty of Life Sciences and Technology, Wroc³aw University of Environmental and Life Sciences, Poland
ul. J. Che³mońskiego 37/41
51-630 Wroc³aw
Poland
email: deta.luczycka@upwr.edu.pl
Tobiasz Wysoczański
Institute of Agricultural Engineering, The Faculty of Life Sciences and Technology, Wroc³aw University of Environmental and Life Sciences, Poland
ul. J. Che³mońskiego 37/41
51-630 Wroc³aw
Poland
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