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Mid-infrared Spectroscopic Determination of Soil Nitrate Content
ARTICLE IN PRESS Biosystems Engineering (2006) 94 (4), 505–515 doi:10.1016/j.biosystemseng.2006.05.011 AE—Automation and Emerging Technologies
Mid-infrared Spectroscopic Determination of Soil Nitrate Content B.R. Jahn1; R. Linker2; S.K. Upadhyaya1; A. Shaviv2; D.C. Slaughter1; I. Shmulevich2 1
Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA; e-mail of corresponding author: [email protected] 2 Faculty of Civil and Environmental Engineering, Lowdermilk Division of Agricultural Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel (Received 22 September 2005; accepted in revised form 17 May 2006; published online 14 July 2006)
Mid-infrared (mid-IR) spectroscopy experiments were conducted to detect added nitrate in various soil types both in the laboratory and field. Soil pastes from ten different soils, including sandy loam, clay, and peat soils, were analysed for soil nitrate contents using the Fourier transform infrared (FTIR) attenuated total reflectance (ATR) technique. Nitrate concentrations for the laboratory experiments varied from approximately 0–1000 ppm. NO3-N while concentrations for the field experiments varied from approximately 0–140 ppm. NO3-N. Three-dimensional plots were created by graphing the wavelet deconvoluted values at 32 scales for each sample. From each plot, the volume of the nitrate peak was determined and correlated to nitrate concentrations. Results of the laboratory experiments indicated values for the coefficient of determination R2 as high as 099 and standard errors as low as 24 ppm. NO3-N for soil-specific calibrations. Results of the field experiments gave values for R2 as high as 098 and standard errors as low as 5 ppm NO3-N for soil-specific calibrations. An alternative technique to determine nitrate content was developed in which wavelet analysis was used to identify a few wavenumbers at which interferences from other ions were minimal. This method produced calibration equations that were soil independent and gave superior results to those obtained based on correlating wavelet deconvoluted volumes to nitrate concentrations. r 2006 IAgrE. All rights reserved Published by Elsevier Ltd
1. Introduction Precision farming, a technique which involves managing agricultural inputs on a site-specific basis, has received much attention over the last decade due to its potential to decrease inputs such as fertilisers, protect the environment, enhance product quality, and/or to increase yields. This technique attempts to use all available information from a field, such as soil nutrient levels, moisture contents, pH, and texture to manage added inputs on a site-specific basis. One of the main obstacles to implementing precision farming techniques is the absence of accurate and easy-to-use soil sensors to gather information about a field. Currently there are field portable commercial sensors available for in situ measurements of pH and soil moisture but none is available for accurate determination of soil nitrate content. 1537-5110/$32.00
Soils with abundant nitrate amounts and shallow groundwater tables pose a high risk to nitrate leaching into drinking water supplies. The greatest concern of nitrate in groundwater is for infants less than 1 yr old and for pregnant animals (Killpack & Buchholz, 1993). The nitrate consumed is converted to nitrite which combines with heamoglobin reducing the oxygen-carrying ability of the blood often resulting in death (Weisenburger, 1993). Currently, the drinking water standard in the United States sets the limit on nitrate at less than 10 ppm of NO3-N. Petit (1988) conducted a study in Oregon in which 28 of 82 drinking water wells tested exceeded this amount. Studies conducted in the Salinas Valley Watershed in California found 35% of the wells tested contained nitrate concentrations greater than 10 ppm (ALBA, 2005). Knowing the nitrate variability across a field enables the desired amount of fertiliser to be applied for a specific area and prevent 505
r 2006 IAgrE. All rights reserved Published by Elsevier Ltd
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Notation R2 s Tcw t
coefficient of determination scale of wavelet continuous wavelet transform time, s
excess application, which can minimise nitrate leaching into groundwater and decrease the incidence of health problems and environmental issues such as algal bloom and greenhouse effect due to N2O (Ehsani et al., 1999). Site-specific crop management combined with variablerate application technology allows one to apply the right amount of fertiliser at the correct location in the field. The main reason for sensing nitrate in fields is to determine how much, if any, fertiliser to apply to meet the needs of the plants and to prevent over-applying which can lead to nitrate leaching. One may reason that the best way to determine the needs of the plant is to obtain information from the plant directly. Stresses due to moisture and nitrogen deficiencies cause the plant leaves to change colour. Grinenko (1987) discovered that moisture and nitrogen deficiencies correlated with colour changes in corn foliage, but leaf colour was unable to distinguish between the two deficiencies. Ercoli et al. (1993) found that absorbance and transmittance measurements in the visible range correlate well with nitrogen and chlorophyll contents, under a controlled condition where stress was due to nitrogen deficiency alone. There are many commercial chlorophyll meters available which basically measure the greenness of the plant. However chlorophyll meters are subject to variability resulting from changes in light intensity from shade, cloud cover, and sun intensity. Due to these concerns Kruse et al., (2004) cautioned ‘while the chlorophyll content can be related to the nitrogen status in the plant, you should be careful basing fertility programmes on these readings.’ There are two potential problems associated with using plant colour to determine its nitrogen needs. First, if a nitrogen deficiency is apparent, then it may be too late to apply fertiliser and expect a positive plant response resulting in increased yield. The other major problem is that different plants exhibit different colours in response to similar nutrient deficiencies, i.e. there is a lack of nutrient deficiency-specific colour. Maize leaves become yellow-green in colour if either nitrogen or moisture deficiencies exist. This makes it difficult to distinguish the particular deficiency for a plant based solely on colour. Also, standards for multiple plants may be required as the same nutrient deficiencies manifest in varying colours.
x(t) time-representation of signal c mother wavelet t translation of wavelet
Conventional methods of soil nitrate analysis involve collecting soil samples in the field, performing the necessary soil preparation steps such as oven drying, grinding, sieving, and conducting laboratory analyses. This process is tedious, time-consuming, and expensive. Often, it takes months to obtain laboratory results. A method of quantifying soil nitrate content in the field has the potential to save valuable time and money. Approaches to nitrate prediction in the past included ion-selective membranes, ion-sensitive field effect transistors, and infrared spectroscopy (Ehsani et al., 1999). Adsett and Zoerb (1991) used the nitrate-selective membrane approach to develop a portable soil nitrate sensor. A chainsaw bar and chain were used to collect the soil and nitrate extraction was performed with deionised water on a portable platform in the field. Problems with inadequate mixing of the soil and water were observed and the system did not produce repeatable results. Moreover, the calibration procedures were tedious and possibly inaccurate due to changing potentials on the electrodes. Adsett et al. (1999) redesigned the system and used a wood saw blade to collect and a conveyer to transport soil samples of known volume and density to the extraction unit. However, problems with unacceptable levels of signal noise and clogging up of the extractor outlet with plant residue were observed. Birrell and Hummel (1993) attempted to use ionselective field effect transistors (ISFET) in a multiISFET sensor chip to measure soil nitrate. The advantages of using ISFET chips include fast response times and a need for low sample volumes. A decade later, Kim et al. (2003) continued with the ISFET approach. They successfully used ISFET chips to predict nitrate concentrations in manually obtained soil extracts with correlation coefficients of 09 and greater. However, when they attempted to incorporate this extraction procedure into a portable unit, they also experienced blockage problems due to plant residues. Recently, Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy in the midinfrared (mid-IR) region has shown great promise for detecting low concentrations of nitrate. The FTIR/ATR technique applied to mid-IR spectra has advantages in terms of minimal sample preparation needed even for low
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nitrate contents (o10 ppm NO3-N) and increased sensitivity of nitrate peaks due to the fundamental modes of vibration of the nitrate molecule that occur in this region. Linker et al. (2004) attempted to use principal component regression (PCR), partial least squares (PLS), and cross-correlation to predict nitrate contents in FTIR/ATR spectra of soil pastes. They experimented with eight soils ranging in NO3-N concentrations from 0 to 1000 ppm Three of the soils were calcareous, containing large amounts of carbonate. When three calcareous soils were not considered, they obtained the best results using PLS (four components, standard error of 32 ppm NO3-N), followed by PCR (seven components, 32 ppm NO3-N), and cross-correlation with reference libraries (using six spectra, 35 ppm NO3-N). When calcareous soils were included in the analysis, the standard errors increased approximately two-fold. In this paper, wavelet analysis and an alternative technique that uses absorbance values at a few selected wavenumbers to determine nitrate contents were used on these same soil spectra. 2. Objectives The overall objective of this research is to develop a real-time soil nitrate sensor for detecting nitrate concentrations in situ. The specific objectives of this study are: (1) to apply wavelet analysis to the FTIR/ATR mid-IR spectra of the soils used by Linker et al. (2004), and compare the performances of the PLS and wavelet approaches; (2) to investigate the applicability of wavelet analysis to deconvolute FTIR/ATR mid-IR spectra of several soil types treated with nitrate fertilisers in situ by (a) working directly with field soils containing nitrate and (b) working only with soil pastes on a 1:1 soil to water on a weight basis; and (3) to investigate the feasibility of using a few selected wavenumbers rather than a continuum of wavelengths to predict nitrate contents. The motivation for the third objective is that currently there is a lack of reasonably-priced mid-infrared spectrometers available for detecting low levels of nitrate typically found in agricultural soils and fixed filter-based systems might be less expensive.
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interfering compounds such as bicarbonate and organic matter were conducted. The experimental design was set up as a split plot treatment structure. The main factor was soil type (Capay clay and Yolo loam) and the subfactor was nitrate source [Ca(NO3)2 or NH4NO3]. Six plots were used for each soil type and five levels of nitrate concentration were used within each plot (15 m long). On half of the plots, NH4NO3 was applied as the source of nitrate, and on the other half Ca(NO3)2 was applied as the nitrate source. This approach resulted in three replications of each treatment combination. Each plot was divided into five 18 m wide strips and each strip received a different amount of nitrate varying from approximately 0–140 ppm NO3-N. This range of nitrate concentrations is typically found in agricultural fields during the growing seasons. For each plot, soluble nitrate was sprayed on the soil and the soil was rotary tilled to allow the nitrate to mix with the soil. Five days were allocated to allow the soil and added nitrate to further mix, after which soil samples were collected and stored in a freezer for further processing and analysis. To allow for uniform distribution of nitrate, water was added to the samples to bring the moisture contents to approximately 20%. This moisture content represented a compromise between adding sufficient moisture to allow movement of the nitrate molecules as well as to allow for ease of drying of the soil later. The soil samples were then oven dried at 55 1C for 48 h, ground using a modified meat grinder, and passed through a sieve (75 mm). A number 200 (75 mm) sieve was used in order to prevent shattering or scratching the ATR crystal by larger sharp particles in the soil sample such as rocks. Then the samples were mixed with distilled water on a 1:1 weight basis to form a paste. Ehsani et al. (2001) attempted to use mid-infrared FTIR/ATR spectra from dry soil samples spiked with nitrate to determine nitrate contents but had difficulties obtaining adequate contact between the soil and the ATR crystal. However, it was found that a soil paste or slurry provided the contact needed to produce spectra with identifiable nitrate peaks for the low nitrate concentrations. Linker et al. (2004) also found that soil paste is preferable over dry soil. A randomly selected sample from each strip was sent to an analytical laboratory for nitrate content determinations, where a flow injection analysis was used. Table 1 shows the range of pH values, CO2 contents, HCO 3 3 contents, and NO3-N contents found in the loam and clay soils.
3. Overview of experimental techniques 3.1. Soil preparations
3.2. Experimental spectroscopy methods
Experiments that involved adding nitrate to Capay clay and Yolo loam soils in the field in the presence of
A Mattson Galaxy 3000 RS-1 FTIR spectrometer with ATR crystal was used for all the field spectra
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Table 1 Range of pH values, CO2 3 contents, HCO3 contents, and NO3 contents for clay and loam soils Soil type Capay clay Yolo loam
pH
CO32, ppm
HCO3, ppm
NO3, ppm
59–63 68–70
o3 o3
488–854 793–1098
50–1400 101–967
measurements. The spectrometer was equipped with a Ge/KBr beam splitter, water-cooled Globar ceramic source, and mercury cadmium telluride (MCT) detector. Each sample was divided into ten subsamples, and the spectra of each subsample was obtained individually. Soil solutions were spread over the crystal and argon was used to purge the cell of CO2. The ATR spectra were measured with 64 scans per sample using 4 cm1 resolution. Since equal weights of dry soil and distilled water were used to create each paste, the soil spectra were corrected for moisture by taking the negative logarithm of the ratio of soil sample and background (water) single beam spectra. Since spectra collected in similar conditions are slightly scaled, biased, or tilted relative to each other, baseline correction procedures must be applied in order to compare different spectra. A two-point baseline correction was applied to each spectrum. This procedure involved fitting a line between minimums located at approximately 1300 and 1500 cm1 and adjusting the slope of this line to be zero. The spectra were then offset so these minimums occurred at an absorbance value of zero. This interval included the nitrate peak of interest, which is located in the 1370–1380 cm1 range.
most measured signals are non-stationary, where different frequencies are present at different times. With Fourier analysis, both the time and frequency information of the signal cannot be known at any instant since the length of the windowing function used is infinite. A technique developed to deal with this limitation is called windowed-Fourier transform or short-time Fourier transform (STFT). This technique assumes a small portion or window of the signal is stationary. However, the STFT is known for resolution problems due to the fixed window width used. Rather than knowing the exact frequencies present in the signal, only a band of frequencies is known. The Heisenberg uncertainty principle states that the exact time and frequency representation of the signal cannot be known (Haykin & Van Veen, 2003). Using a wide window with the STFT gives good frequency resolution but poor time resolution. On the contrary, a narrow window gives good time resolution but poor frequency resolution. A solution to the problems associated with STFT and FT is to use a window of varying width, depending on the magnitude of the frequency components and where in time these components are located relative to the signal. This is the basis of wavelet analysis and is discussed in the following section.
4. Overview of analytical techniques 4.1. Wavelet analysis Measured signals are usually of a two-dimensional form containing time- and amplitude-related information. The time domain representation of a signal does not always present all the information, so some sort of a mathematical transform is often applied to obtain a more useful representation. In many cases, the frequency domain contains much more useful information than does the time domain. There are many different transforms that can be applied, including the Fourier transform, the Radon transform, and the wavelet transform. A limitation of Fourier analysis is that the time information is lost in the transformed domain, i.e. a plot of amplitude versus frequency is possible but information about where these frequency components exist in time domain is not available. In dealing with stationary signals where all frequency components exist at all times, this limitation presents no problem. However,
Wavelet analysis was developed to overcome the limitations of the Fourier transform with non-stationary signals and the resolution problems of the STFT. The procedure is similar to Fourier analysis where the signal is multiplied and integrated by a function. However, rather than using sine and cosine functions, wavelet analysis uses scaled and shifted versions of a base function called a mother wavelet. Unlike sine and cosine functions these mother wavelets are local and finite, making them ideal for approximating signals with sharp peaks and discontinuities. Selecting a mother wavelet that represents the general shape of the signal is important. The continuous wavelet transform Tcw is defined as Z t t 1 T cw ðt; sÞ ¼ pffiffiffiffiffi xðtÞc dt (1) s jsj
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where: x(t) is the time representation of the signal; t is time; and stands for the complex conjugate (Haykin & Van Veen, 2003). The continuous wavelet transform of the signal is a function of two factors, s and t. The factor s is the scale factor and causes the mother wavelet to either stretch (s41) or dilate (so1). The translation factor t is related to the location of the window as it is shifted along the signal. The term c represents the mother wavelet. The wavelet procedure used for the soil spectral analyses involved deconvoluting with a Coiflet-three (Daubechies, 1992) mother wavelet as the basis function. Coiflet wavelets have the highest number of vanishing moments for a given support width. The number of vanishing moments determines the order of the polynomial used to approximate the signal (Tan, 2005). Therefore, a Coiflet wavelet approximates a specific type of signal more precisely than many other wavelets. A Coiflet wavelet is also compactly supported which allows for distinguishing discontinuities or singularities in a signal. It is commonly used for spectral signals of soils and other biological materials. A MATLAB (2002) program was written that utilised the wavelet toolbox to perform a continuous wavelet decomposition analysis of the spectral signals. A three-dimensional plot was created to display the peaks in each of the signals (Fig. 1). The nitrate peak was identified in each plot (in the 1370 to 1390 cm1 range) and the volume of this peak was estimated.
The interaction of the nitrate ion with other metals can cause the nitrate peak to shift from 1390 cm1 to as far as 1360 cm1, but the scale coordinate remained constant (scale 3) for all the soil samples. Therefore, another MATLAB program was written to detect the wavenumber coordinate of the nitrate peak using a Savitsky and Golay routine (Savitsky & Golay, 1964). Once the peak coordinates were identified, the volume of the peak was estimated by integrating over a wavenumber range of 10 cm1 on both sides of the peak and scale range from 2 to 4 using MATLAB. The range was experimentally determined to adequately represent the nitrate peak volume with minimal influence from adjacent peaks and this same range was used for all the samples.
5. Results and discussion 5.1. Wavelet analysis of the samples Wavelet analysis was applied to the eight soils used by Linker et al. (2004). These soils included sandy loam, peat and clay soils, with varying levels of carbonate and organic matter. The texture-and carbonate-related information for these soils can be found in Table 2. Since Linker et al. (2004) has pointed out to the interfering effect of carbonate, the analysis was first
0.06 Magnitude
0.05 0.04 0.03 0.02 0.01 16 15 14 12
NO3− 11 10 9 Sc 8 ale 7 6
1800 1700 5
4
3
2
1400 1
1600 −1 1500 ber, cm m u n Wave
Fig. 1. Three-dimensional wavelet deconvoluted graph of a soil paste contained 59 ppm NO3-N showing peak due to nitrate (circled), organic matter, water, and other components
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subsequently overcome by seeking a second wavenumber to account for carbonate content of soil as described later. 1200
NO3-N, p.p.m.
1000 800 600 400 200 0
0
1
2
3 4 5 6 Volume of nitrate peak
7
8
9
Fig. 2. Plot of nitrate concentration versus volume of nitrate peak for the five non-calcareous soils from Linker et al. (2004); the standard error was 244 ppm NO3-N; coefficient of determination (R2), was 099
1200 1000 NO3-N, p.p.m.
performed separately on calcareous and non-calcareous soils. Strong linear correlations between wavelet deconvoluted nitrate peak volumes and nitrate concentrations were found. The values for the coefficient of determination R2 were 086 and higher, with standard errors less than 112 ppm NO3-N (nitrate concentrations range in samples: 0–1000 ppm NO3-N). Figure 2 is plot of nitrate content versus nitrate peak volume for the five noncalcareous soils pooled together while Fig. 3 is for the three calcareous soils pooled together. The standard error for the five non-calcareous soils was 244 ppm NO3-N while that for the three calcareous soils was 1120 ppm NO3-N. The reason for the larger standard error in the calcareous soils can be explained as follows. The nitrate peak volumes for the calcareous soils were nearly an order of magnitude less than those for the noncalcareous soils. This is because the large carbonate peak found at approximately 1450 cm1 tended to overshadow with the smaller nitrate peak located around 1370 cm1. Rather than having a clearly defined nitrate peak in the absorbance spectra, the response due to nitrate now showed up as a shoulder on the much larger carbonate peak, as shown in Fig. 4. This, in turn, caused the nitrate peak to both be shifted to a lower wavenumber and to become smaller in magnitude. Separating these peaks in the third dimension (scale) using wavelet analysis still resulted in peak overlap issues that caused problems when estimating peak volumes, as shown by the relatively poor correlation coefficient for the three calcareous soils (R2 ¼ 086) compared to the correlation coefficient for the five non-calcareous soils (R2 ¼ 099). Pooling the calcareous and non-calcareous data together resulted in poor correlation, due to the significantly different slopes in the data from Figs 2 and 3. Therefore it was concluded that separate calibration equations depending on the soil carbonate content are required for correlating wavelet decomposed peak volumes to nitrate concentrations. This unfortunate situation was
800 600 400 200 0
0
0.2
0.4 0.6 0.8 1 Volume of nitrate peak
1.2
1.4
Fig. 3. Plot of nitrate concentration versus volume of nitrate peak for the three calcareous soils from Linker et al. (2004); the standard error was 1120 ppm NO3-N; coefficient of determination (R2), was 085
Table 2 Soil texture and CaCO3 concentrations for soils from Israel Soil name
Soil type
Clay, %
Silt, %
Sand, %
CaCO3 concentration, %
Beit Shean Bsor Shaalabim H1 H2 Germany Columbia Tourba
Calcareous clay Loam Clay Sandy loam Sandy loam Loam Clay Peat
55 15 54 1 1 24 N/A N/A
22 9 19 6 5 23 N/A N/A
23 76 27 93 94 53 N/A N/A
47 13 9 0 0 0 1 0
N/A, not available.
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0.12
ATR absorbance
0.1
CO32−
0.08 0.06
NO3−
0.04 0.02 0
1600 1550 1500 1450 1400 1350 1300 1250 1200 Wavenumber, cm−1
slightly larger range of nitrate concentrations and nonlinear trend for the clay plots provide likely explanations for the slightly higher standard error obtained for these plots. Even though the prediction capabilities looked promising, the slopes of the correlation lines were significantly different. Pooling the data from Figs 5 and 6 together and fitting a linear trendline resulted in a value for R2 of 078 and standard error of 154 ppm NO3-N. Therefore, although a fertiliser specific calibration is not needed, soil-specific calibration is required for accurate nitrate prediction using wavelet decomposed volumes.
Fig. 4. Baseline- and water-corrected attenuated total reflection (ATR) absorbance spectra of calcareous soil with 872 ppm 1 NO3-N showing large CO2 and shoulder due 3 peak at 1450 cm 1 to NO 3 at approximately 1350 cm
NO3-N, p.p.m.
The split plot experiment was analysed using Statistical Analysis Software (SAS, 2001) revealed a significant effect (probability a ¼ 005) due to soil type and none due to nitrate source (fertiliser). The homogeneity of variance assumption was verified by Hartley’s Test (Milliken & Johnson, 1992). Note that the error structure of this model required that the main factor —soil type, be tested against the main plot error and the subfactor—fertilizer type, be tested against the experimental error. Compared to the laboratory experiments conducted, the range of nitrate concentrations for the field plots (0–140 ppm NO3-N) was significantly less. As with the previous experiments, the wavelet decomposed nitrate peak volume for each sample was correlated to nitrate concentration. Ten subsamples were used as a compromise between reduced measurement error and workload required to deal with many subsamples. The volume was determined for each subsample and then all ten values were averaged to represent that sample. A correlation graph for all six clay plots pooled together for both fertilisers is shown in Fig. 5, and a graph for all loam plots pooled together for both fertilisers is shown in Fig. 6. Figure 5 indicates a possible non-linear relationship between nitrate peak volume and nitrate concentration. Fitting a quadratic to the data resulted in a value for R2 of 097 and standard error of 56 ppm NO3-N. A linear regression line was plotted, however, to compare regression equations among the two soil types. In both cases, relatively high correlation values (R2 ¼ 093 for clay and 096 for loam) and low standard errors (95 and 58 ppm NO3-N, respectively) were obtained. The
140 120 100 80 60 40 20 0
0
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Volume of nitrate peak
2
Fig. 5. Plot of nitrate concentration versus volume of nitrate peak for Capay clay soil for two fertilisers pooled together; each point on the graph is an average of 10 subsamples; the standard error was 95 ppm NO3-N; coefficient of determination (R2), was 093
160 140 120 NO3-N, p.p.m.
5.2. Wavelet analysis of the Californian field spectra
160
100 80 60 40 20 0 0
0.2
0.4 0.6 0.8 1 1.2 1.4 Volume of nitrate peak
1.6 1.8
2
Fig. 6. Plot of nitrate concentration versus volume of nitrate peak for Yolo loam soil for two fertilisers pooled together; each point on the graph is an average of 10 subsamples. the standard error was 58 ppm NO3-N; coefficient of determination (R2), was 096
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5.3. Analysis using limited number of wavenumbers Several wavenumbers in the 1300 to 1400 cm1 range were investigated for correlating the absorbance values at these locations to nitrate contents. Referring to Figs 7 and 1, one can see that there is an interference problem with basically one peak around 1400 cm1. The wavelet decomposed plot in Fig. 8 shows that the nitrate peak and interfering peak (most likely due to carbonate) are separated in the scale (frequency) dimension with the nitrate peak occurring at scale 3 and the interfering peak at scale 2. Figure 8 shows a two-dimensional representation of Fig. 1 for scales 2 and 3 including two different concentrations of nitrate in soil pastes. The curve due to carbonate (scale 2) crosses the horizontal axis at approximately 1350 cm1 for all samples. Therefore at this location, the influence from the interfering peak at 1400 cm1 is virtually non-existent and represents the most promising location for predicting nitrate content with minimal interference. A similar technique was performed by Weyer (1986) where a derivative approach was used to locate zero crossover points where interfering components have no absorption. She analysed NIR reflectance spectra involving mixtures of hydrocarbon polymers and was able to avoid most of the interfering effects by using absorbance values and corresponding derivatives at these zero crossover points. Plots of nitrate concentration versus absorbance at 1350 cm1 for Capay clay and Yolo loam soils are shown in Figs 9 and 10. The Capay clay soils gave a value for R2 of 097 and standard error of 63 ppm NO3N. For the Yolo loam soil spectra, the value for R2 was
098 and standard error was 36 ppm NO3-N. Perhaps, what is more important for these two plots is that the correlation lines have similar slope and intercept values. Pooling the data from these two plots together gave a value for R2 of 095 and standard error of 74 ppm NO3-N. Out of the 47 spectra available (note that each point on the plots is an average of 10 spectra), 24 were randomly chosen to develop a calibration line and the remaining 23 spectra were used as validation. Standard errors of 82 ppm NO3-N and 62 ppm NO3-N were obtained for the calibration and validation sets, respectively. Similar procedures were used for the soils of Linker et al. (2004) and it was found that the calibration equations of nitrate concentration versus absorbance at 1350 cm1 for the five non-calcareous soils were both similar to each other as well as to the equations developed from the field spectra. In addition, the slopes of the calibration equations for the three calcareous soils were similar to the slopes for the non-calcareous and field soils. However, the intercept values for the noncalcareous soils were found to differ both from each other and from the calcareous and field soils. There are several reasons for the different intercept values among the three groups of spectra: (1) The large peak at approximately 1450 cm1 (Fig. 4) tended to deform the much smaller nitrate peak located at approximately 1370 cm1; (2) difference in spectrometers used in the two studies, in particular, with Linker et al. (2004) using a deuterated triglycine sulphate (DTGS) detector while a MCT detector was used in this study; and (3) differences in moisture content of soil pastes [1:1 weight basis pastes in this study and Linker et al. (2004) using variable moisture contents].
0.014
ATR absorbance
0.012 0.01 0.008 0.006 0.004 0.002 0 1600
1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 Wavenumber, cm−1
Fig. 7. Two-dimensional attenuated total reflection (ATR) absorbance spectra for soil paste showing nitrate and carbonate peaks merged together
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Wavelet decomposed value
0.025 0.02 0.015 0.01 0.005 0 1300 1320 1340 1360 1380 1400 1420 1440 1460 1480 1500 . − 0 005 − 0.01 − 0.015 − 0.02 − 0.025
Wavenumber, cm−1
Fig. 8. Wavelet decomposed values at scale 2 possibly due to carbonate and at scale 3 due to nitrate; the ppm values are in terms of NO3-N: , 24 ppm NO3-N; , 120 ppm NO3-N; , carbonate
160 140
NO3-N, p.p.m.
120 100 80 60 40 20 0
0
0.001 0.002 0.003 0.004
0.005 0.006
0.007 0.008 0.009
Absorbance at 1350 cm−1 Fig. 9. Plot of nitrate concentration versus absorbance at 1350 cm1 for Capay clay field experiments; the standard error was 63 ppm NO3-N; each point on the graph is an average of 10 subsamples; coefficient of determination (R2), was 097
160 140 NO3-N, p.p.m.
120 100 80 60 40 20 0
0
0.001 0.002
0.003 0.004
0.005
0.006 0.007
0.008 0.009
Absorbance at 1350 cm−1 Fig. 10. Plot of nitrate concentration versus absorbance at 1350 cm1 for Yolo loam field experiments; the standard error was 36 ppm NO3-N; each point on the graph is an average of 10 subsamples; coefficient of determination (R2), was 098
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Due to these intercept differences, a method based on indicator variables was used to pool all the data into one calibration plot. There were four distinct sets of soil spectra: one set for each of the three calcareous soils from Linker et al. (2004) and one set for both the non-calcareous soils from Linker et al. (2004) and for the field soils. The four distinct calibration lines were combined into one equation by using indicator variables for each of the three sets of soil spectra. Figure 11 shows a calibration plot of nitrate concentration versus absorbance at 1350 cm1 for the ten soils pooled together. The resulting value for R2 was 098 and the standard error was 40 ppm NO3-N. It was found that the intercept value for each of the three sets of spectra correlated to the absorbance at 1500 cm1, which is possibly related to the organic and/ or carbonate amount in the soil. Therefore, a multiple linear regression procedure was used to develop a calibration equation based on the absorbance values at 1350 and 1500 cm1. This calibration equation resulted in a value for R2 of 098 and standard error of 45 ppm NO3-N. Wavelet analysis was essentially the tool used to locate the wavenumber where nitrate could be predicted with minimal interference from other components. Even though wavelet analysis was able to predict nitrate contents reasonably well, using a single absorbance value is much more practical for developing a real-time soil nitrate sensor. Wavelet analysis uses a continuum of absorbance values which requires a spectrometer to obtain. With a limited number of wavebands, a tunable diode laser or IR light source with filters is sufficient to develop a nitrate sensor.
6. Conclusions Wavelet analysis was applied to soil spectra collected by Linker et al. (2004) and soil nitrate calibration equations were developed, resulting in values for the coefficient of determination R2 as high as 099. Due to a large interfering absorbance peak near the nitrate peak, soil-specific calibration equations were needed depending on the carbonate contents of the soils to allow for accurate prediction. A similar wavelet analysis technique was applied to field soil samples obtained near the UC Davis campus. Correlating nitrate concentrations with wavelet decomposed nitrate peak volumes resulted in values for R2 as high as 096 and relatively low standard errors (as low as 58 ppm NO3-N). However, the calibration equations obtained for non-calcareous and calcareous soils contained significantly different slopes indicating that soil-specific calibrations were needed. Further exploration of the data revealed that the interference from carbonate content of soils can be minimised if absorbance values at 1350 cm1 wavenumber were used to determine soil nitrate content rather than wavenumber corresponding to the peak absorbance of nitrate ion (i.e. 1370 cm1). Although the slope of the calibration was insensitive to the carbonate content of the soil even when it was very high such as in calcareous soils, the intercept of the calibration equation was sensitive to high carbonate content of calcareous soils. However, this intercept was found to be related to absorbance values corresponding to a wavenumber of 1500 cm1. It was possible to develop a single calibration equation valid for 10 different soils using absorbance
1200
Actual NO3-N, p.p.m.
1000 800 600 400 200 0
0
200
400 600 800 Predicted NO3-N, p.p.m.
1000
1200
Fig. 11. Calibration plot for 10 soils pooled together using indicator variables method; the standard error was 45 ppm NO3-N; coefficient of determination (R2), was 098
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values corresponding to 1350 and 1500 cm1 wavenumbers. Standard errors as low as 36 ppm NO3-N were obtained, which would suffice for precision farming applications. This result is an encouraging step towards the development of a real-time, in situ soil nitrate sensor.
7. Future Work The next step is to apply this technique of using a limited number of wavebands to predict nitrate concentration from spectra collected with a rugged, filterbased spectrometer that was recently purchased. This spectrometer has a sapphire-coated ATR crystal and no moving parts (Wilks Model VFA spectrometer). These characteristics make this spectrometer suitable for field use, allowing for the possibility of interfacing with a soil sampler. This combination of a spectrometer with a soil sampling device will allow for in-situ nitrate determination. Alternately, this type of sensor can be used in a ‘mobile laboratory’ that can analyse soil samples at the test site rapidly. Moreover, such a sensor can be employed for environment monitoring purposes such as determining nitrate content in agricultural drainage, streams, or rivers.
Acknowledgements This project is supported by a US–Israel Binational Research and Development Fund (BARD Project US3293-20c). References Adsett J F; Thottan J A; Sibley K J (1999). Development of an automated on-the-go soil nitrate monitoring system. Applied Engineering in Agriculture, 15(4), 351–356 Adsett J F; Zoerb G C (1991). Automated field monitoring of soil nitrate levels. Proceedings of the 1991 Symposium on Automated Agriculture for the 21st Century, Chicago, Il., pp 326–335 ALBA (2005). Nitrate leaching and groundwater contamination. Agricultural and Land-Based Training Association, Salinas, Cal., Available at: http://www.albafarmers.org/ Homepage.html. Accessed 10 August 2005 Birrell S J; Hummel J W (1993). Multi-ISFET sensors for soil nitrate analysis. In: Soil Specific Crop Management (Robert P C, ed), p 349. ASA Miscellaneous Publication (ASACSSA-SSSA), Madison, WI
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Daubechies I (1992). Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia, PA, pp 258-325 Ehsani M R; Upadhyaya S K; Fawcett W R; Protsailo L V; Slaughter D (2001). Feasibility of detecting soil nitrate content using a mid-infrared technique. Transactions of the ASAE, 44(6), 1931–1940 Ehsani M R; Upadhyaya S K; Slaughter D C; Shafii S; Pelletier M (1999). A NIR technique for rapid determination of soil mineral nitrogen. Precision Agriculture, 1, 217–234 Ercoli L; Mariotti M; Masoni A; Massantini F (1993). Relationship between nitrogen and chlorophyll content and spectral properties in maize leaves. European Journal of Agronomy, 2(2), 113–117 Grinenko L A (1987). Possibilities of determining nitrogen in plants from their color. Proceedings of Academy of Sciences of USSR, 294(3), 767–768 Haykin S; Van Veen B (2003). Signals and Systems, (2nd Edn). John Wiley & Sons, New York Killpack S C; Buchholz D (1993). Nitrogen in the environment: leaching. Project No. 89-EWQI-1-9203. United States Department of Agriculture, Extension Service, Washington, DC Kim H; Hummel J W; Birrell SJ (2003). Evaluation of ionselective membranes for real-time soil nutrients sensing. ASAE Paper No. 03-1075. St. Joseph, MI. Kruse J; Christians N; Chaplin M (2004). Remote sensing. In: Grounds Maintenance (Ratcliff C, ed). Primedia Business Magazines and Media, Inc., Overland Park, KS Linker R; Kenny A; Shaviv A; Singher L; Shmulevich I (2004). Fourier transform infrared-attenuated total reflection nitrate determination of soil solutions using principal component regression, partial least squares, and crosscorrelation. Applied Spectroscopy, 58(5), 516–520 MATLAB (2002). The MathWorks, Inc. Version 6.5. Natick, Massachusetts. Milliken G A; Johnson D E (1992). Analysis of Messy Data. Chapman & Hall, New York Petit G (1988). Assessment of Oregon’s ground-water for agricultural chemicals. Report to the State of Oregon. Oregon Department of Environmental Quality, Portland, OR SAS (2001). Statistical Analysis Software. Version 8.02. SAS Institute, Cary, NC. Savitsky A; Golay J E (1964). Smoothing and differentiation of data by simplified least square procedure. Analytical Chemistry, 36, 1627–1639 Tan Y H (2005). Vanishing Moments, Support, Regularity, and Symmetry. Watermarking World, Switzerland. Available at: www.watermarkingworld.org/WMMLArchive/ 0208/msg00026.html. Accessed 16 June 2005 Weisenburger D D (1993). Human health effects of agrichemical use. Human Pathology, 24(6), 571–576 Weyer L G (1986). Utilizing zero crossover points in the near infrared reflectance analysis of industrial polymers. Journal of Applied Polymer Science, 31(8), 2417–2431
Mid-infrared Spectroscopic Determination of Soil Nitrate Content B.R. Jahn1; R. Linker2; S.K. Upadhyaya1; A. Shaviv2; D.C. Slaughter1; I. Shmulevich2 1
Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA; e-mail of corresponding author: [email protected] 2 Faculty of Civil and Environmental Engineering, Lowdermilk Division of Agricultural Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel (Received 22 September 2005; accepted in revised form 17 May 2006; published online 14 July 2006)
Mid-infrared (mid-IR) spectroscopy experiments were conducted to detect added nitrate in various soil types both in the laboratory and field. Soil pastes from ten different soils, including sandy loam, clay, and peat soils, were analysed for soil nitrate contents using the Fourier transform infrared (FTIR) attenuated total reflectance (ATR) technique. Nitrate concentrations for the laboratory experiments varied from approximately 0–1000 ppm. NO3-N while concentrations for the field experiments varied from approximately 0–140 ppm. NO3-N. Three-dimensional plots were created by graphing the wavelet deconvoluted values at 32 scales for each sample. From each plot, the volume of the nitrate peak was determined and correlated to nitrate concentrations. Results of the laboratory experiments indicated values for the coefficient of determination R2 as high as 099 and standard errors as low as 24 ppm. NO3-N for soil-specific calibrations. Results of the field experiments gave values for R2 as high as 098 and standard errors as low as 5 ppm NO3-N for soil-specific calibrations. An alternative technique to determine nitrate content was developed in which wavelet analysis was used to identify a few wavenumbers at which interferences from other ions were minimal. This method produced calibration equations that were soil independent and gave superior results to those obtained based on correlating wavelet deconvoluted volumes to nitrate concentrations. r 2006 IAgrE. All rights reserved Published by Elsevier Ltd
1. Introduction Precision farming, a technique which involves managing agricultural inputs on a site-specific basis, has received much attention over the last decade due to its potential to decrease inputs such as fertilisers, protect the environment, enhance product quality, and/or to increase yields. This technique attempts to use all available information from a field, such as soil nutrient levels, moisture contents, pH, and texture to manage added inputs on a site-specific basis. One of the main obstacles to implementing precision farming techniques is the absence of accurate and easy-to-use soil sensors to gather information about a field. Currently there are field portable commercial sensors available for in situ measurements of pH and soil moisture but none is available for accurate determination of soil nitrate content. 1537-5110/$32.00
Soils with abundant nitrate amounts and shallow groundwater tables pose a high risk to nitrate leaching into drinking water supplies. The greatest concern of nitrate in groundwater is for infants less than 1 yr old and for pregnant animals (Killpack & Buchholz, 1993). The nitrate consumed is converted to nitrite which combines with heamoglobin reducing the oxygen-carrying ability of the blood often resulting in death (Weisenburger, 1993). Currently, the drinking water standard in the United States sets the limit on nitrate at less than 10 ppm of NO3-N. Petit (1988) conducted a study in Oregon in which 28 of 82 drinking water wells tested exceeded this amount. Studies conducted in the Salinas Valley Watershed in California found 35% of the wells tested contained nitrate concentrations greater than 10 ppm (ALBA, 2005). Knowing the nitrate variability across a field enables the desired amount of fertiliser to be applied for a specific area and prevent 505
r 2006 IAgrE. All rights reserved Published by Elsevier Ltd
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Notation R2 s Tcw t
coefficient of determination scale of wavelet continuous wavelet transform time, s
excess application, which can minimise nitrate leaching into groundwater and decrease the incidence of health problems and environmental issues such as algal bloom and greenhouse effect due to N2O (Ehsani et al., 1999). Site-specific crop management combined with variablerate application technology allows one to apply the right amount of fertiliser at the correct location in the field. The main reason for sensing nitrate in fields is to determine how much, if any, fertiliser to apply to meet the needs of the plants and to prevent over-applying which can lead to nitrate leaching. One may reason that the best way to determine the needs of the plant is to obtain information from the plant directly. Stresses due to moisture and nitrogen deficiencies cause the plant leaves to change colour. Grinenko (1987) discovered that moisture and nitrogen deficiencies correlated with colour changes in corn foliage, but leaf colour was unable to distinguish between the two deficiencies. Ercoli et al. (1993) found that absorbance and transmittance measurements in the visible range correlate well with nitrogen and chlorophyll contents, under a controlled condition where stress was due to nitrogen deficiency alone. There are many commercial chlorophyll meters available which basically measure the greenness of the plant. However chlorophyll meters are subject to variability resulting from changes in light intensity from shade, cloud cover, and sun intensity. Due to these concerns Kruse et al., (2004) cautioned ‘while the chlorophyll content can be related to the nitrogen status in the plant, you should be careful basing fertility programmes on these readings.’ There are two potential problems associated with using plant colour to determine its nitrogen needs. First, if a nitrogen deficiency is apparent, then it may be too late to apply fertiliser and expect a positive plant response resulting in increased yield. The other major problem is that different plants exhibit different colours in response to similar nutrient deficiencies, i.e. there is a lack of nutrient deficiency-specific colour. Maize leaves become yellow-green in colour if either nitrogen or moisture deficiencies exist. This makes it difficult to distinguish the particular deficiency for a plant based solely on colour. Also, standards for multiple plants may be required as the same nutrient deficiencies manifest in varying colours.
x(t) time-representation of signal c mother wavelet t translation of wavelet
Conventional methods of soil nitrate analysis involve collecting soil samples in the field, performing the necessary soil preparation steps such as oven drying, grinding, sieving, and conducting laboratory analyses. This process is tedious, time-consuming, and expensive. Often, it takes months to obtain laboratory results. A method of quantifying soil nitrate content in the field has the potential to save valuable time and money. Approaches to nitrate prediction in the past included ion-selective membranes, ion-sensitive field effect transistors, and infrared spectroscopy (Ehsani et al., 1999). Adsett and Zoerb (1991) used the nitrate-selective membrane approach to develop a portable soil nitrate sensor. A chainsaw bar and chain were used to collect the soil and nitrate extraction was performed with deionised water on a portable platform in the field. Problems with inadequate mixing of the soil and water were observed and the system did not produce repeatable results. Moreover, the calibration procedures were tedious and possibly inaccurate due to changing potentials on the electrodes. Adsett et al. (1999) redesigned the system and used a wood saw blade to collect and a conveyer to transport soil samples of known volume and density to the extraction unit. However, problems with unacceptable levels of signal noise and clogging up of the extractor outlet with plant residue were observed. Birrell and Hummel (1993) attempted to use ionselective field effect transistors (ISFET) in a multiISFET sensor chip to measure soil nitrate. The advantages of using ISFET chips include fast response times and a need for low sample volumes. A decade later, Kim et al. (2003) continued with the ISFET approach. They successfully used ISFET chips to predict nitrate concentrations in manually obtained soil extracts with correlation coefficients of 09 and greater. However, when they attempted to incorporate this extraction procedure into a portable unit, they also experienced blockage problems due to plant residues. Recently, Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy in the midinfrared (mid-IR) region has shown great promise for detecting low concentrations of nitrate. The FTIR/ATR technique applied to mid-IR spectra has advantages in terms of minimal sample preparation needed even for low
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nitrate contents (o10 ppm NO3-N) and increased sensitivity of nitrate peaks due to the fundamental modes of vibration of the nitrate molecule that occur in this region. Linker et al. (2004) attempted to use principal component regression (PCR), partial least squares (PLS), and cross-correlation to predict nitrate contents in FTIR/ATR spectra of soil pastes. They experimented with eight soils ranging in NO3-N concentrations from 0 to 1000 ppm Three of the soils were calcareous, containing large amounts of carbonate. When three calcareous soils were not considered, they obtained the best results using PLS (four components, standard error of 32 ppm NO3-N), followed by PCR (seven components, 32 ppm NO3-N), and cross-correlation with reference libraries (using six spectra, 35 ppm NO3-N). When calcareous soils were included in the analysis, the standard errors increased approximately two-fold. In this paper, wavelet analysis and an alternative technique that uses absorbance values at a few selected wavenumbers to determine nitrate contents were used on these same soil spectra. 2. Objectives The overall objective of this research is to develop a real-time soil nitrate sensor for detecting nitrate concentrations in situ. The specific objectives of this study are: (1) to apply wavelet analysis to the FTIR/ATR mid-IR spectra of the soils used by Linker et al. (2004), and compare the performances of the PLS and wavelet approaches; (2) to investigate the applicability of wavelet analysis to deconvolute FTIR/ATR mid-IR spectra of several soil types treated with nitrate fertilisers in situ by (a) working directly with field soils containing nitrate and (b) working only with soil pastes on a 1:1 soil to water on a weight basis; and (3) to investigate the feasibility of using a few selected wavenumbers rather than a continuum of wavelengths to predict nitrate contents. The motivation for the third objective is that currently there is a lack of reasonably-priced mid-infrared spectrometers available for detecting low levels of nitrate typically found in agricultural soils and fixed filter-based systems might be less expensive.
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interfering compounds such as bicarbonate and organic matter were conducted. The experimental design was set up as a split plot treatment structure. The main factor was soil type (Capay clay and Yolo loam) and the subfactor was nitrate source [Ca(NO3)2 or NH4NO3]. Six plots were used for each soil type and five levels of nitrate concentration were used within each plot (15 m long). On half of the plots, NH4NO3 was applied as the source of nitrate, and on the other half Ca(NO3)2 was applied as the nitrate source. This approach resulted in three replications of each treatment combination. Each plot was divided into five 18 m wide strips and each strip received a different amount of nitrate varying from approximately 0–140 ppm NO3-N. This range of nitrate concentrations is typically found in agricultural fields during the growing seasons. For each plot, soluble nitrate was sprayed on the soil and the soil was rotary tilled to allow the nitrate to mix with the soil. Five days were allocated to allow the soil and added nitrate to further mix, after which soil samples were collected and stored in a freezer for further processing and analysis. To allow for uniform distribution of nitrate, water was added to the samples to bring the moisture contents to approximately 20%. This moisture content represented a compromise between adding sufficient moisture to allow movement of the nitrate molecules as well as to allow for ease of drying of the soil later. The soil samples were then oven dried at 55 1C for 48 h, ground using a modified meat grinder, and passed through a sieve (75 mm). A number 200 (75 mm) sieve was used in order to prevent shattering or scratching the ATR crystal by larger sharp particles in the soil sample such as rocks. Then the samples were mixed with distilled water on a 1:1 weight basis to form a paste. Ehsani et al. (2001) attempted to use mid-infrared FTIR/ATR spectra from dry soil samples spiked with nitrate to determine nitrate contents but had difficulties obtaining adequate contact between the soil and the ATR crystal. However, it was found that a soil paste or slurry provided the contact needed to produce spectra with identifiable nitrate peaks for the low nitrate concentrations. Linker et al. (2004) also found that soil paste is preferable over dry soil. A randomly selected sample from each strip was sent to an analytical laboratory for nitrate content determinations, where a flow injection analysis was used. Table 1 shows the range of pH values, CO2 contents, HCO 3 3 contents, and NO3-N contents found in the loam and clay soils.
3. Overview of experimental techniques 3.1. Soil preparations
3.2. Experimental spectroscopy methods
Experiments that involved adding nitrate to Capay clay and Yolo loam soils in the field in the presence of
A Mattson Galaxy 3000 RS-1 FTIR spectrometer with ATR crystal was used for all the field spectra
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Table 1 Range of pH values, CO2 3 contents, HCO3 contents, and NO3 contents for clay and loam soils Soil type Capay clay Yolo loam
pH
CO32, ppm
HCO3, ppm
NO3, ppm
59–63 68–70
o3 o3
488–854 793–1098
50–1400 101–967
measurements. The spectrometer was equipped with a Ge/KBr beam splitter, water-cooled Globar ceramic source, and mercury cadmium telluride (MCT) detector. Each sample was divided into ten subsamples, and the spectra of each subsample was obtained individually. Soil solutions were spread over the crystal and argon was used to purge the cell of CO2. The ATR spectra were measured with 64 scans per sample using 4 cm1 resolution. Since equal weights of dry soil and distilled water were used to create each paste, the soil spectra were corrected for moisture by taking the negative logarithm of the ratio of soil sample and background (water) single beam spectra. Since spectra collected in similar conditions are slightly scaled, biased, or tilted relative to each other, baseline correction procedures must be applied in order to compare different spectra. A two-point baseline correction was applied to each spectrum. This procedure involved fitting a line between minimums located at approximately 1300 and 1500 cm1 and adjusting the slope of this line to be zero. The spectra were then offset so these minimums occurred at an absorbance value of zero. This interval included the nitrate peak of interest, which is located in the 1370–1380 cm1 range.
most measured signals are non-stationary, where different frequencies are present at different times. With Fourier analysis, both the time and frequency information of the signal cannot be known at any instant since the length of the windowing function used is infinite. A technique developed to deal with this limitation is called windowed-Fourier transform or short-time Fourier transform (STFT). This technique assumes a small portion or window of the signal is stationary. However, the STFT is known for resolution problems due to the fixed window width used. Rather than knowing the exact frequencies present in the signal, only a band of frequencies is known. The Heisenberg uncertainty principle states that the exact time and frequency representation of the signal cannot be known (Haykin & Van Veen, 2003). Using a wide window with the STFT gives good frequency resolution but poor time resolution. On the contrary, a narrow window gives good time resolution but poor frequency resolution. A solution to the problems associated with STFT and FT is to use a window of varying width, depending on the magnitude of the frequency components and where in time these components are located relative to the signal. This is the basis of wavelet analysis and is discussed in the following section.
4. Overview of analytical techniques 4.1. Wavelet analysis Measured signals are usually of a two-dimensional form containing time- and amplitude-related information. The time domain representation of a signal does not always present all the information, so some sort of a mathematical transform is often applied to obtain a more useful representation. In many cases, the frequency domain contains much more useful information than does the time domain. There are many different transforms that can be applied, including the Fourier transform, the Radon transform, and the wavelet transform. A limitation of Fourier analysis is that the time information is lost in the transformed domain, i.e. a plot of amplitude versus frequency is possible but information about where these frequency components exist in time domain is not available. In dealing with stationary signals where all frequency components exist at all times, this limitation presents no problem. However,
Wavelet analysis was developed to overcome the limitations of the Fourier transform with non-stationary signals and the resolution problems of the STFT. The procedure is similar to Fourier analysis where the signal is multiplied and integrated by a function. However, rather than using sine and cosine functions, wavelet analysis uses scaled and shifted versions of a base function called a mother wavelet. Unlike sine and cosine functions these mother wavelets are local and finite, making them ideal for approximating signals with sharp peaks and discontinuities. Selecting a mother wavelet that represents the general shape of the signal is important. The continuous wavelet transform Tcw is defined as Z t t 1 T cw ðt; sÞ ¼ pffiffiffiffiffi xðtÞc dt (1) s jsj
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where: x(t) is the time representation of the signal; t is time; and stands for the complex conjugate (Haykin & Van Veen, 2003). The continuous wavelet transform of the signal is a function of two factors, s and t. The factor s is the scale factor and causes the mother wavelet to either stretch (s41) or dilate (so1). The translation factor t is related to the location of the window as it is shifted along the signal. The term c represents the mother wavelet. The wavelet procedure used for the soil spectral analyses involved deconvoluting with a Coiflet-three (Daubechies, 1992) mother wavelet as the basis function. Coiflet wavelets have the highest number of vanishing moments for a given support width. The number of vanishing moments determines the order of the polynomial used to approximate the signal (Tan, 2005). Therefore, a Coiflet wavelet approximates a specific type of signal more precisely than many other wavelets. A Coiflet wavelet is also compactly supported which allows for distinguishing discontinuities or singularities in a signal. It is commonly used for spectral signals of soils and other biological materials. A MATLAB (2002) program was written that utilised the wavelet toolbox to perform a continuous wavelet decomposition analysis of the spectral signals. A three-dimensional plot was created to display the peaks in each of the signals (Fig. 1). The nitrate peak was identified in each plot (in the 1370 to 1390 cm1 range) and the volume of this peak was estimated.
The interaction of the nitrate ion with other metals can cause the nitrate peak to shift from 1390 cm1 to as far as 1360 cm1, but the scale coordinate remained constant (scale 3) for all the soil samples. Therefore, another MATLAB program was written to detect the wavenumber coordinate of the nitrate peak using a Savitsky and Golay routine (Savitsky & Golay, 1964). Once the peak coordinates were identified, the volume of the peak was estimated by integrating over a wavenumber range of 10 cm1 on both sides of the peak and scale range from 2 to 4 using MATLAB. The range was experimentally determined to adequately represent the nitrate peak volume with minimal influence from adjacent peaks and this same range was used for all the samples.
5. Results and discussion 5.1. Wavelet analysis of the samples Wavelet analysis was applied to the eight soils used by Linker et al. (2004). These soils included sandy loam, peat and clay soils, with varying levels of carbonate and organic matter. The texture-and carbonate-related information for these soils can be found in Table 2. Since Linker et al. (2004) has pointed out to the interfering effect of carbonate, the analysis was first
0.06 Magnitude
0.05 0.04 0.03 0.02 0.01 16 15 14 12
NO3− 11 10 9 Sc 8 ale 7 6
1800 1700 5
4
3
2
1400 1
1600 −1 1500 ber, cm m u n Wave
Fig. 1. Three-dimensional wavelet deconvoluted graph of a soil paste contained 59 ppm NO3-N showing peak due to nitrate (circled), organic matter, water, and other components
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subsequently overcome by seeking a second wavenumber to account for carbonate content of soil as described later. 1200
NO3-N, p.p.m.
1000 800 600 400 200 0
0
1
2
3 4 5 6 Volume of nitrate peak
7
8
9
Fig. 2. Plot of nitrate concentration versus volume of nitrate peak for the five non-calcareous soils from Linker et al. (2004); the standard error was 244 ppm NO3-N; coefficient of determination (R2), was 099
1200 1000 NO3-N, p.p.m.
performed separately on calcareous and non-calcareous soils. Strong linear correlations between wavelet deconvoluted nitrate peak volumes and nitrate concentrations were found. The values for the coefficient of determination R2 were 086 and higher, with standard errors less than 112 ppm NO3-N (nitrate concentrations range in samples: 0–1000 ppm NO3-N). Figure 2 is plot of nitrate content versus nitrate peak volume for the five noncalcareous soils pooled together while Fig. 3 is for the three calcareous soils pooled together. The standard error for the five non-calcareous soils was 244 ppm NO3-N while that for the three calcareous soils was 1120 ppm NO3-N. The reason for the larger standard error in the calcareous soils can be explained as follows. The nitrate peak volumes for the calcareous soils were nearly an order of magnitude less than those for the noncalcareous soils. This is because the large carbonate peak found at approximately 1450 cm1 tended to overshadow with the smaller nitrate peak located around 1370 cm1. Rather than having a clearly defined nitrate peak in the absorbance spectra, the response due to nitrate now showed up as a shoulder on the much larger carbonate peak, as shown in Fig. 4. This, in turn, caused the nitrate peak to both be shifted to a lower wavenumber and to become smaller in magnitude. Separating these peaks in the third dimension (scale) using wavelet analysis still resulted in peak overlap issues that caused problems when estimating peak volumes, as shown by the relatively poor correlation coefficient for the three calcareous soils (R2 ¼ 086) compared to the correlation coefficient for the five non-calcareous soils (R2 ¼ 099). Pooling the calcareous and non-calcareous data together resulted in poor correlation, due to the significantly different slopes in the data from Figs 2 and 3. Therefore it was concluded that separate calibration equations depending on the soil carbonate content are required for correlating wavelet decomposed peak volumes to nitrate concentrations. This unfortunate situation was
800 600 400 200 0
0
0.2
0.4 0.6 0.8 1 Volume of nitrate peak
1.2
1.4
Fig. 3. Plot of nitrate concentration versus volume of nitrate peak for the three calcareous soils from Linker et al. (2004); the standard error was 1120 ppm NO3-N; coefficient of determination (R2), was 085
Table 2 Soil texture and CaCO3 concentrations for soils from Israel Soil name
Soil type
Clay, %
Silt, %
Sand, %
CaCO3 concentration, %
Beit Shean Bsor Shaalabim H1 H2 Germany Columbia Tourba
Calcareous clay Loam Clay Sandy loam Sandy loam Loam Clay Peat
55 15 54 1 1 24 N/A N/A
22 9 19 6 5 23 N/A N/A
23 76 27 93 94 53 N/A N/A
47 13 9 0 0 0 1 0
N/A, not available.
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0.12
ATR absorbance
0.1
CO32−
0.08 0.06
NO3−
0.04 0.02 0
1600 1550 1500 1450 1400 1350 1300 1250 1200 Wavenumber, cm−1
slightly larger range of nitrate concentrations and nonlinear trend for the clay plots provide likely explanations for the slightly higher standard error obtained for these plots. Even though the prediction capabilities looked promising, the slopes of the correlation lines were significantly different. Pooling the data from Figs 5 and 6 together and fitting a linear trendline resulted in a value for R2 of 078 and standard error of 154 ppm NO3-N. Therefore, although a fertiliser specific calibration is not needed, soil-specific calibration is required for accurate nitrate prediction using wavelet decomposed volumes.
Fig. 4. Baseline- and water-corrected attenuated total reflection (ATR) absorbance spectra of calcareous soil with 872 ppm 1 NO3-N showing large CO2 and shoulder due 3 peak at 1450 cm 1 to NO 3 at approximately 1350 cm
NO3-N, p.p.m.
The split plot experiment was analysed using Statistical Analysis Software (SAS, 2001) revealed a significant effect (probability a ¼ 005) due to soil type and none due to nitrate source (fertiliser). The homogeneity of variance assumption was verified by Hartley’s Test (Milliken & Johnson, 1992). Note that the error structure of this model required that the main factor —soil type, be tested against the main plot error and the subfactor—fertilizer type, be tested against the experimental error. Compared to the laboratory experiments conducted, the range of nitrate concentrations for the field plots (0–140 ppm NO3-N) was significantly less. As with the previous experiments, the wavelet decomposed nitrate peak volume for each sample was correlated to nitrate concentration. Ten subsamples were used as a compromise between reduced measurement error and workload required to deal with many subsamples. The volume was determined for each subsample and then all ten values were averaged to represent that sample. A correlation graph for all six clay plots pooled together for both fertilisers is shown in Fig. 5, and a graph for all loam plots pooled together for both fertilisers is shown in Fig. 6. Figure 5 indicates a possible non-linear relationship between nitrate peak volume and nitrate concentration. Fitting a quadratic to the data resulted in a value for R2 of 097 and standard error of 56 ppm NO3-N. A linear regression line was plotted, however, to compare regression equations among the two soil types. In both cases, relatively high correlation values (R2 ¼ 093 for clay and 096 for loam) and low standard errors (95 and 58 ppm NO3-N, respectively) were obtained. The
140 120 100 80 60 40 20 0
0
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Volume of nitrate peak
2
Fig. 5. Plot of nitrate concentration versus volume of nitrate peak for Capay clay soil for two fertilisers pooled together; each point on the graph is an average of 10 subsamples; the standard error was 95 ppm NO3-N; coefficient of determination (R2), was 093
160 140 120 NO3-N, p.p.m.
5.2. Wavelet analysis of the Californian field spectra
160
100 80 60 40 20 0 0
0.2
0.4 0.6 0.8 1 1.2 1.4 Volume of nitrate peak
1.6 1.8
2
Fig. 6. Plot of nitrate concentration versus volume of nitrate peak for Yolo loam soil for two fertilisers pooled together; each point on the graph is an average of 10 subsamples. the standard error was 58 ppm NO3-N; coefficient of determination (R2), was 096
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5.3. Analysis using limited number of wavenumbers Several wavenumbers in the 1300 to 1400 cm1 range were investigated for correlating the absorbance values at these locations to nitrate contents. Referring to Figs 7 and 1, one can see that there is an interference problem with basically one peak around 1400 cm1. The wavelet decomposed plot in Fig. 8 shows that the nitrate peak and interfering peak (most likely due to carbonate) are separated in the scale (frequency) dimension with the nitrate peak occurring at scale 3 and the interfering peak at scale 2. Figure 8 shows a two-dimensional representation of Fig. 1 for scales 2 and 3 including two different concentrations of nitrate in soil pastes. The curve due to carbonate (scale 2) crosses the horizontal axis at approximately 1350 cm1 for all samples. Therefore at this location, the influence from the interfering peak at 1400 cm1 is virtually non-existent and represents the most promising location for predicting nitrate content with minimal interference. A similar technique was performed by Weyer (1986) where a derivative approach was used to locate zero crossover points where interfering components have no absorption. She analysed NIR reflectance spectra involving mixtures of hydrocarbon polymers and was able to avoid most of the interfering effects by using absorbance values and corresponding derivatives at these zero crossover points. Plots of nitrate concentration versus absorbance at 1350 cm1 for Capay clay and Yolo loam soils are shown in Figs 9 and 10. The Capay clay soils gave a value for R2 of 097 and standard error of 63 ppm NO3N. For the Yolo loam soil spectra, the value for R2 was
098 and standard error was 36 ppm NO3-N. Perhaps, what is more important for these two plots is that the correlation lines have similar slope and intercept values. Pooling the data from these two plots together gave a value for R2 of 095 and standard error of 74 ppm NO3-N. Out of the 47 spectra available (note that each point on the plots is an average of 10 spectra), 24 were randomly chosen to develop a calibration line and the remaining 23 spectra were used as validation. Standard errors of 82 ppm NO3-N and 62 ppm NO3-N were obtained for the calibration and validation sets, respectively. Similar procedures were used for the soils of Linker et al. (2004) and it was found that the calibration equations of nitrate concentration versus absorbance at 1350 cm1 for the five non-calcareous soils were both similar to each other as well as to the equations developed from the field spectra. In addition, the slopes of the calibration equations for the three calcareous soils were similar to the slopes for the non-calcareous and field soils. However, the intercept values for the noncalcareous soils were found to differ both from each other and from the calcareous and field soils. There are several reasons for the different intercept values among the three groups of spectra: (1) The large peak at approximately 1450 cm1 (Fig. 4) tended to deform the much smaller nitrate peak located at approximately 1370 cm1; (2) difference in spectrometers used in the two studies, in particular, with Linker et al. (2004) using a deuterated triglycine sulphate (DTGS) detector while a MCT detector was used in this study; and (3) differences in moisture content of soil pastes [1:1 weight basis pastes in this study and Linker et al. (2004) using variable moisture contents].
0.014
ATR absorbance
0.012 0.01 0.008 0.006 0.004 0.002 0 1600
1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 Wavenumber, cm−1
Fig. 7. Two-dimensional attenuated total reflection (ATR) absorbance spectra for soil paste showing nitrate and carbonate peaks merged together
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Wavelet decomposed value
0.025 0.02 0.015 0.01 0.005 0 1300 1320 1340 1360 1380 1400 1420 1440 1460 1480 1500 . − 0 005 − 0.01 − 0.015 − 0.02 − 0.025
Wavenumber, cm−1
Fig. 8. Wavelet decomposed values at scale 2 possibly due to carbonate and at scale 3 due to nitrate; the ppm values are in terms of NO3-N: , 24 ppm NO3-N; , 120 ppm NO3-N; , carbonate
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0.005 0.006
0.007 0.008 0.009
Absorbance at 1350 cm−1 Fig. 9. Plot of nitrate concentration versus absorbance at 1350 cm1 for Capay clay field experiments; the standard error was 63 ppm NO3-N; each point on the graph is an average of 10 subsamples; coefficient of determination (R2), was 097
160 140 NO3-N, p.p.m.
120 100 80 60 40 20 0
0
0.001 0.002
0.003 0.004
0.005
0.006 0.007
0.008 0.009
Absorbance at 1350 cm−1 Fig. 10. Plot of nitrate concentration versus absorbance at 1350 cm1 for Yolo loam field experiments; the standard error was 36 ppm NO3-N; each point on the graph is an average of 10 subsamples; coefficient of determination (R2), was 098
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Due to these intercept differences, a method based on indicator variables was used to pool all the data into one calibration plot. There were four distinct sets of soil spectra: one set for each of the three calcareous soils from Linker et al. (2004) and one set for both the non-calcareous soils from Linker et al. (2004) and for the field soils. The four distinct calibration lines were combined into one equation by using indicator variables for each of the three sets of soil spectra. Figure 11 shows a calibration plot of nitrate concentration versus absorbance at 1350 cm1 for the ten soils pooled together. The resulting value for R2 was 098 and the standard error was 40 ppm NO3-N. It was found that the intercept value for each of the three sets of spectra correlated to the absorbance at 1500 cm1, which is possibly related to the organic and/ or carbonate amount in the soil. Therefore, a multiple linear regression procedure was used to develop a calibration equation based on the absorbance values at 1350 and 1500 cm1. This calibration equation resulted in a value for R2 of 098 and standard error of 45 ppm NO3-N. Wavelet analysis was essentially the tool used to locate the wavenumber where nitrate could be predicted with minimal interference from other components. Even though wavelet analysis was able to predict nitrate contents reasonably well, using a single absorbance value is much more practical for developing a real-time soil nitrate sensor. Wavelet analysis uses a continuum of absorbance values which requires a spectrometer to obtain. With a limited number of wavebands, a tunable diode laser or IR light source with filters is sufficient to develop a nitrate sensor.
6. Conclusions Wavelet analysis was applied to soil spectra collected by Linker et al. (2004) and soil nitrate calibration equations were developed, resulting in values for the coefficient of determination R2 as high as 099. Due to a large interfering absorbance peak near the nitrate peak, soil-specific calibration equations were needed depending on the carbonate contents of the soils to allow for accurate prediction. A similar wavelet analysis technique was applied to field soil samples obtained near the UC Davis campus. Correlating nitrate concentrations with wavelet decomposed nitrate peak volumes resulted in values for R2 as high as 096 and relatively low standard errors (as low as 58 ppm NO3-N). However, the calibration equations obtained for non-calcareous and calcareous soils contained significantly different slopes indicating that soil-specific calibrations were needed. Further exploration of the data revealed that the interference from carbonate content of soils can be minimised if absorbance values at 1350 cm1 wavenumber were used to determine soil nitrate content rather than wavenumber corresponding to the peak absorbance of nitrate ion (i.e. 1370 cm1). Although the slope of the calibration was insensitive to the carbonate content of the soil even when it was very high such as in calcareous soils, the intercept of the calibration equation was sensitive to high carbonate content of calcareous soils. However, this intercept was found to be related to absorbance values corresponding to a wavenumber of 1500 cm1. It was possible to develop a single calibration equation valid for 10 different soils using absorbance
1200
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0
200
400 600 800 Predicted NO3-N, p.p.m.
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1200
Fig. 11. Calibration plot for 10 soils pooled together using indicator variables method; the standard error was 45 ppm NO3-N; coefficient of determination (R2), was 098
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values corresponding to 1350 and 1500 cm1 wavenumbers. Standard errors as low as 36 ppm NO3-N were obtained, which would suffice for precision farming applications. This result is an encouraging step towards the development of a real-time, in situ soil nitrate sensor.
7. Future Work The next step is to apply this technique of using a limited number of wavebands to predict nitrate concentration from spectra collected with a rugged, filterbased spectrometer that was recently purchased. This spectrometer has a sapphire-coated ATR crystal and no moving parts (Wilks Model VFA spectrometer). These characteristics make this spectrometer suitable for field use, allowing for the possibility of interfacing with a soil sampler. This combination of a spectrometer with a soil sampling device will allow for in-situ nitrate determination. Alternately, this type of sensor can be used in a ‘mobile laboratory’ that can analyse soil samples at the test site rapidly. Moreover, such a sensor can be employed for environment monitoring purposes such as determining nitrate content in agricultural drainage, streams, or rivers.
Acknowledgements This project is supported by a US–Israel Binational Research and Development Fund (BARD Project US3293-20c). References Adsett J F; Thottan J A; Sibley K J (1999). Development of an automated on-the-go soil nitrate monitoring system. Applied Engineering in Agriculture, 15(4), 351–356 Adsett J F; Zoerb G C (1991). Automated field monitoring of soil nitrate levels. Proceedings of the 1991 Symposium on Automated Agriculture for the 21st Century, Chicago, Il., pp 326–335 ALBA (2005). Nitrate leaching and groundwater contamination. Agricultural and Land-Based Training Association, Salinas, Cal., Available at: http://www.albafarmers.org/ Homepage.html. Accessed 10 August 2005 Birrell S J; Hummel J W (1993). Multi-ISFET sensors for soil nitrate analysis. In: Soil Specific Crop Management (Robert P C, ed), p 349. ASA Miscellaneous Publication (ASACSSA-SSSA), Madison, WI
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