Identification of agricultural Mediterranean soils using mid-infrared photoacoustic spectroscopy

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1 Available online at Geoderma 143 (2008) Identification of agricultural Mediterranean soils using mid-infrared photoacoustic spectroscopy C. Du a, R. Linker b,, A. Shaviv b a The National Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing China b Faculty of Civil and Environmental Engineering, Israel Institute of Technology, Haifa Israel Received 19 June 2007; received in revised form 23 October 2007; accepted 25 October 2007 Available online 3 December 2007 Abstract This study investigated the use of Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) for rapid identification of agricultural soil samples. The PAS spectra of 166 air-dried samples belonging to five Mediterranean soil types most common in Israeli agriculture were recorded. The various soil types exhibited distinctive mid-ir bands, especially around the cm 1, cm 1, cm 1 and cm 1 regions. Following smoothing and normalization of the spectra, principal component analysis (PCA) was used to reduce the dimensionality of the data and the PCA scores were used in classifiers based either on linear discriminant analysis or on probabilistic neural networks. The two classifiers based on four PCA scores yielded very similar results and correctly identified over 96% of the 77 validation samples. Comparison with the attenuated total reflectance (ATR) spectra of similar soils used in a previous study showed that the PAS spectra contained more information than the ATR ones, both in terms of the number of soil-specific bands and in terms of the bands' distinctiveness. The results clearly show that FTIR-PAS can be used for rapid soil identification and the abundance of information in the PAS spectra indicates that this technique could be further developed to assess important soil features Elsevier B.V. All rights reserved. Keywords: Fourier transform infrared (FTIR); Probabilistic neural network; Classification; Discrimination 1. Introduction Soil is a complex mixture of primary and secondary minerals, a variety of organic compounds, microorganisms and more, all being produced and transformed during the long process of its formation. This complexity is the source of the large variation of soil types worldwide. For decades, soil scientists have been conducting classification and identification studies, mainly as a basis for surveying soils and understanding their nature. Soil survey was traditionally based on tedious data collection dealing with the many factors associated with soil formation and classification. The need for rapid and inexpensive techniques for characterization (rather than classification) of soils on the basis of their major properties to support agricultural and agroecological management decisions (e.g. precision agriculture) has Corresponding author. Tel.: ; fax: address: linkerr@tx.technion.ac.il (R. Linker). led to the use of modern technologies, in particular those based on remote or in-situ reflectance spectroscopy (e.g.,brown et al., 2006; Dematte et al., 2004; Shepherd and Walsh, 2002; Viscarra Rossel et al., 2006). Infrared spectroscopy is a well established technique for the identification of chemical compounds and/or specific functional groups in compounds, and thus is a useful tool for soil applications (Johnston and Aochi, 1996; Haberhauer and Gerzabek, 2001). In particular, reflectance spectroscopy can be used for nondestructive assessment of soil and crop physical and biochemical properties (e.g. Cozzolino and Moron, 2003, 2006; Chang et al., 2001; Dunn et al., 2002; Shepherd and Walsh, 2002; Shepherd et al., 2003). Although the near-infrared (NIR) range ( nm) is still the most widely used, mid-infrared spectroscopy is becoming increasingly common due to the specificity of the absorbance bands in that spectral range (e.g. Stuart, 1997). In particular, mid-infrared attenuated total reflectance (ATR) spectroscopy can be used for fast and simple determination of nitrate /$ - see front matter 2007 Elsevier B.V. All rights reserved. doi: /j.geoderma

2 86 C. Du et al. / Geoderma 143 (2008) concentration in water and soil pastes (Shaviv et al., 2003; Linker et al., 2004). Linker et al. (2005, 2006) alsoshowedthatmidinfrared ATR spectroscopy could be used to identify major types of agricultural soils based primarily on absorbance bands associated with characteristic soil constituents (e.g. calcium carbonate, clay minerals and possibly organic constituents). Such identification of soil types led to the significant improvement of ATR-based determination of nitrate in soil pastes (Linker et al., 2006). Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) is another spectral technique that can be used for the identification of constituents in complex systems (McClelland et al., 2001). FTIR-PAS is based on the absorption of electromagnetic radiation by the sample and non-radiative relaxation that leads to local warming of the sample. Pressure fluctuations are then generated by thermal expansion, which can be detected by a very sensitive microphone (Fig. 1). FTIR-PAS has been applied to depth profiling of coatings (Wahls et al., 2000), analysis of the chemical composition of materials as diverse as wood (Bjarnestad and Dahlman, 2002) and pesticides (Armenta et al., 2006), and detection of microorganisms in food products (Irudayaraj et al., 2002). A major advantage of photoacoustic spectroscopy is that it is suitable for highly absorbing solid samples without any special pre-treatment. With respect to soil analysis, this is a major advantage compared to transmittance measurements that require time-consuming preparation of KBr pellets, or the ATR configuration that requires a saturated soil paste and suffers from interferences associated with the presence of water (e.g. Linker et al., 2004). The objective of this study is to determine whether FTIR-PAS spectroscopy can be used for automatic discrimination of agricultural soil samples according to a-priori defined classes that reflect the main properties of the upper layer of a cultivated soil. 2. Materials and methods 2.1. Soil samples and FTIR-PAS measurements One hundred and sixty six soil samples representing five Mediterranean soil types commonly used in Israeli agriculture were collected from various locations, and their basic properties are summarized in Table 1. More than 80% of the samples originated from the Israeli National Effluent Irrigation Survey conducted in citrus and avocado orchards (e.g. Tarchitzky et al., Fig. 1. Schematic description of the photoacoustic spectroscopy setup. Table 1 Denomination and basic properties of the soils used in this study Soil name Soil texture/ Classification (FAO) 2004). The sampling depth was 0 30 cm and each sample was a composite of two sub-samples. The locations of the samples were recorded by GPS and the soils were categorized based on the national soil survey map (National Soil Survey Unit, Israel Ministry of Agriculture, private communication). Additional samples were taken from orchards with Terra Rossa and Loess soils, and from a lysimeter experiment with corn (Master et al. 2003). Due to technical constraints, the lysimeter soils were sampled from the 0 to 10 cm layer only. The soil samples were air-dried at room temperature and sieved (2 mm) prior to the FTIR-PAS measurements (Bruker Vector 22 spectrophotometer equipped with a photoacoustic cell (MTEC Photoacoustics Inc)). After filling the stainless steel cup (diameter 1 cm, height 3 mm) with the soil sample, the photoacoustic cell was purged with helium for 30 s to minimize interferences due to water vapor and impurities. Eight successive scans over the range of cm 1 were performed and averaged. The scans were conducted with a modulation frequency of 2.2 khz and a resolution of 8 cm Data processing Number of samples Clay content (%) CaCO 3 content (%) Organic matter content (%) Grumosol Clay (Calcareous)/ Vertisols Hamra Sandy loam/ Luvisols Terra Rosa Clay/Cambisols Loess Loam/Fluvisols Rendzina Clay/Rendzinas The spectra were pre-processed by applying a smoothing filter (first-order Savitzky Golay filter with a 25-point window) and normalizing the amplitudes so that the spectrum integral was equal to one. The data processing itself consisted of two stages, namely data reduction and classification. Data reduction was achieved by the principal component analysis (PCA, e.g. Jackson, 1991), which is commonly used to reduce the dimensionality of infrared spectra and yields a small number of coefficients (socalled PCA scores) that retain most of the variability (information) present in the original data. PCA was applied directly to the normalized spectra without further preprocessing (i.e. no centering or variance standardization). Classification was performed using two supervised classification methods. The first method was based on linear discriminant analysis performed in the PCA scores space, which determined linear partition functions between the different classes (soils) assuming the same covariance estimate for all classes (pooled estimated) (Krzanowski, 2000). The second method used a probabilistic neural network (PNN) that had the PCA scores as inputs. Probabilistic neural networks, which were initially developed by Specht (1990a,b), are a form of Bayesian classifier that use radial basis functions (RBFs) as activation functions in the NN hidden layer. Although PNNs are

3 C. Du et al. / Geoderma 143 (2008) Fig. 2. Mean (bold lines) and standard deviation (thin lines) of the spectra recorded for each soil type. -Grumosol; -Hamra; -Terra Rosa; -Loess; -Rendzina. not as widely used as back-propagation sigmoid-based neural networks, PNNs offer several advantages for classification applications, and in particular they are guaranteed to converge to an optimal classifier as the size of the training set increases. As dictated by the PNN architecture, the number of input and hidden nodes was determined by the number of PCA scores retained from the PCA decomposition and the number of training samples, respectively. The PNN had five output nodes, corresponding to the five soil types included in this study. All classifiers were calibrated (trained) using 55% of the samples randomly chosen (89 soils samples: 26 Grumosol, 31 Hamra, 7 Terra Rosa, 23 Loess and 2 Rendzina). The remaining 77 samples (23 Grumosol, 26 Hamra, 11 Terra Rosa, 14 Loess and 3 Rendzina) were used as validation set. 3. Results and discussion Fig. 2 shows the averages and standard deviations of the spectra of the five soil types investigated. Clear differences between the different soils are clearly visible in several spectral regions, and in particular around cm 1, cm 1, cm 1, cm 1, and cm 1. These differences reflect the differences in clay content and type, calcium carbonate content, organic matter and hygroscopic-water content, and a detailed discussion of these bands has been presented elsewhere (Du et al., 2007). It can be noted that the spectra of the Loess and Rendzina, which both have high clay and calcium carbonate contents, are quite similar and have a distinctive band around 2520 cm 1. Similarly, Hamra and Terra Rosa, which develop under more effective leaching/ dissolution conditions of chalk, share many common qualitative features. The low standard deviation values indicate that the within-type variations are low and typically correspond to less than 10% of the average signal. One noticeable exception is the Grumosol signal around 1450 cm 1, for which a large standard deviation can be observed. This is due to the fact that the Grumosol class appears to include two distinct groups of soils as canbeseeninfig. 3. All the samples with low signal in the cm 1 interval originated from a lysimeter experiment that investigated the long-term effects different effluent and fresh water intensive irrigation regimes (Master et al., 2003). This interval corresponds to calcium carbonate and possibly organic constituents (e.g., Du et al., 2007) and the differences in the spectra can be attributed to variation in these constituents and possibly also to the different sampling depths. Yet, despite the difference observed in this region, the samples were grouped in one class. The PCA scores are shown in Fig. 4. The first and second PCs account for 87.0% and 7.8% of the total variance, respectively, and the first four PCs account for 98.2% of the total variance. The two-dimensional scatter plots of the PCA scores show that each soil type corresponds to a well-defined cluster and cluster overlaps appear to be limited. The relative positions of the clusters agree with the previous observations concerning the similarities of (1) the Loess and Rendzina spectra, and (2) the Hamra and Terra Rosa spectra. Fortunately, the Loess and Rendzina and the Hamra and Terra Rosa soils develop under different and distinct climatic and geological conditions and, practically, are not found in the same geographical regions. The results of the classification analysis are summarized in Tables 2 and 3. Overall, very high percentages of correct classification are achieved, even when the simple linear discriminant method is used (Table 2). In that case, using four PCs yields correct classification of 98% of the validation samples, with the misclassifications corresponding to one Hamra classified as Grumosol and one Loess classified as Hamra. Classifications with the PNNs were performed with various values of the parameter that controls the spread of the RBFs in the NN hidden layer. The PNNs performance appeared to be almost insensitive to this parameter as long as it remained within the range (not shown), and the results presented in Table 3 were obtained with the RBF spread equal to It can be seen that, when using four PCs, the PNN correctly classifies all the validation samples but one Hamra. Fig. 3. Spectra of the Grumosol samples.

4 88 C. Du et al. / Geoderma 143 (2008) Fig. 4. Scatterplot of the scores of the first four principal components. -Grumosol; -Hamra; -Terra Rosa; -Loess; -Rendzina. The empty symbols correpond to the calibration samples and the filled symbols correspond to the validation samples. These results clearly show the potential of photoacoustic spectroscopy for soil discrimination. Such discrimination performances are similar to the ones reported in a previous study (Linker et al., 2006) that involved over 200 ATR spectra of 38 soils. However, from a close examination of the spectra, it appears that the PAS spectra contain a higher number of welldefined bands than the ATR ones. This can be seen by comparing Fig. 2 with Fig. 5 that shows averages and standard deviations of the water-subtracted ATR spectra used by Linker et al. (2006). Although the soil samples used in both studies were not strictly identical, they belonged to the same five classes, and thus the comparison is legitimate. First, it should be noted that due to the very strong absorbance of water around 3000 cm 1, only the cm 1 interval yields a useful ATR signal and thus only this range is shown in Fig. 5. The ATR spectra consist Table 2 Results of the classification based on linear discriminant analysis. Validation data only Soil type Number of spectra Number of samples correctly classified Number of PCs used G H T L R mostly of the calcium carbonate band around 1450 cm 1 and four bands in the cm 1 region, with rather subtle differences between the various soil types. By comparison, the PAS spectra exhibit much more pronounced differences (for instance around 2950 cm 1, 2520 cm 1, 1900 cm 1 and cm 1 ). In addition, the within-type variations in the PAS spectra are smaller than in the ATR spectra. The higher quality of the PAS spectra can be explained by the absence of free-water in the samples. By comparison, the water in the saturated pastes used for ATR spectroscopy (1) yields very strong interfering bands that can be removed only imperfectly (Linker et al., 2004), (2) interacts differently with the various functional groups of soil constituents and causes smearing of the bands as compared to spectra obtained in drier state, and (3) interacts differently with differing soil particles (e.g. swelling) and affects their spatial Table 3 Results of the classification using probabilistic neural networks. Validation data only Soil type Number of spectra Number of samples correctly classified Number of PC used G H T L R

5 C. Du et al. / Geoderma 143 (2008) features of the PAS spectra can be explained by the absence of free-water in the samples, so that the interferences and interactions encountered with the water saturated pastes used for FTIR-ATR spectra are greatly reduced, and more informative signals are obtained. In addition, the PAS spectra include reasonably-sized bands associated with hygroscopic water, which by itself is an important soil feature. Photacoustic spectroscopy requires minimal sample preparation, yields abundant information about the main soil constituents (clay minerals, carbonates, organic matter, hygroscopicwater content) and offers great potential not only for better soil type identification/discrimination but also for rapid and reliable assessment of important soil features. Acknowledgments Fig. 5. Mean (bold lines) and standard deviation (thin lines) of the FTIR-ATR spectra used by Linker et al.(2006). The spectra were water-subtracted and normalized. -Grumosol; -Hamra; -Terra Rosa; -Loess; -Rendzina. distribution within the very thin boundary layer between the ATR crystal and particles. It should also be noticed that whereas in the ATR spectra the strong water bands had to be mathematically removed (Linker et al., 2004), the PAS spectra were taken with air-dried soils and thus include important information related to the amount of hygroscopic water physically bound to soil particles (see intervals cm 1 and cm 1 ). This kind of information by itself could be used for assessing important soil features such as clay content and specific surface area. 4. Conclusions Photoacoustic spectra of 166 air-dried samples representing five Mediterranean soil types most common in Israeli agriculture were utilized to investigate the use of Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) for rapid identification of soil type. The various soil types exhibited distinctive bands, especially around cm 1, cm 1, cm 1 and cm 1, indicating differences between soils in type and amount of clay minerals, carbonates, organic matter and hygroscopic-water content. After smoothing and normalizing the spectra, principal component analysis (PCA) was used to reduce the dimensionality of the data. The PCA scores were used in classifiers based either on linear discriminant functions or on probabilistic neural networks. The clusters resulting from the PCA decomposition were well-defined with very limited overlapping, so that even the simple linear classifier yielded very good results. Using a more complex neural network classifier improved the results only marginally, and the two classifiers based on four PCA scores identified correctly over 96% of the 77 validation samples. Comparison between FTIR-ATR and FTIR-PAS spectra of similar soils indicates that the latter contain a higher number of well-defined bands, and that the within-type variations in the PAS spectra are smaller than in the ATR ones. The enhanced The authors acknowledge the financial support of the Postdoctoral Fellowship from the Israel High Education Committee and the National Basic Research Program of China (Nb. 2005CB121102). References Armenta, S., Moros, J., Garrigues, S., de la Guardia, M., Direct determination of Mancozeb by photoacoustic spectroscopy. Analytica Chimica Acta 567, Bjarnestad, S., Dahlman, O., Chemical compositions of hardwood and softwood pulps employing photoacoustic Fourier transform infrared spectroscopy in combination with partial least-squares analysis. Analytical Chemistry 74, Brown, D.J., Shepherd, K.D., Walsh, M.G., Mays, M.D., Reinsch, T.G., Global soil characterization with VNIR diffuse reflectance spectroscopy. 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