We appreciate the guidance from Dr. Randy Southard on NaOCl oxidation and various discussions of spectral subtractions with Dr. Fungai F.N.D. Mukome.

1. Prepare Soil for Non-treated DRIFT Spectroscopy and SOM Removal
<ol>
	<li>Sieve the soil to &#60; 2 mm using a stainless-steel mesh (the &#39;fine-earth fraction&#39;). 
	NOTE: This demonstration employs two soils of similar texture but a nearly 3-fold difference in total SOM content (Table 1).</li>
</ol>
2. SOM Removal by Chemical Oxidation: Example of NaOCl
<ol>
	<li>Adjust the pH of 6% w/v NaOCl to pH 9.5 by adding 1 M HCl dropwise to the solution while mixing and measuring with a pH meter. 
	NOTE: Most commercial bleaches (e.g., Clorox) are suitable in quality and concentration (typically 3-7% NaOCl v/v) but will have pH &#62; 12. As NaOCl oxidation of organic matter is pH-dependent, and pH 9.5 is recommended for its use with soil samples19,23, it is necessary to adjust the pH of most commercially available bleaches.</li>
	<li>Add 25 mL NaOCl (6% w/v, pH 9.5) to 4 g soil (sieved, air dried) in a 50-mL conical tube and mix by sonication (600 s, output frequency 20 kHz, power 200 W).</li>
	<li>Incubate the mixture in a hot-water bath (15 min, 80 &#176;C) to increase oxidation rate.</li>
	<li>Centrifuge to obtain a clear supernatant (e.g., 15 min at 4,000 &#215; g for coarser textured soils; room temperature). Manually decant the supernatant into a waste container. 
	NOTE: The concentration of NaOCl in the supernatant (conservatively assuming no oxidation and thus no consumption of NaOCl) is the same as commercially available bleach for household use. Finer textured soils may require longer centrifugation time (e.g., up to an additional 15-30 min) at a given centrifuge speed (e.g., 4,000 &#215; g) to obtain a clear supernatant.</li>
	<li>Repeat steps 2.3 and 2.4 twice for a total of three oxidation steps.</li>
	<li>After the last oxidation step, add 20 mL deionized H2O (dH2O) to the soil and mix for 5 min using a horizontal shaker (120 rpm). Centrifuge for 15 min at 4,000 &#215; g and room temperature. Repeat for a total of three treatments.</li>
	<li>Using a spatula and dH2O from a squirt bottle as needed, extract and wash out the soil pellet from the bottom of the centrifuge tube into a plastic weigh boat (or another container with high surface area). Oven-dry (60 &#176;C maximum, 48 h) to an air-dried state.</li>
	<li>Once the soil sample is dried, quantify total organic carbon content by combustion-gas chromatography using a C/N analyzer24. Calculate SOM removal as the difference in organic carbon concentration before and after oxidation treatment. 
	NOTE: Due to loss of organic matter and soil structure, soil will be prone to crusting, in particular for soils with low sand content. It may be necessary to apply gentle pressure and/or hand grinding to re-homogenize the crusted soil. Soils with inorganic carbon (i.e., carbonates) require additional steps for quantifying organic carbon by combustion-gas chromatography25,26.</li>
</ol>
3. SOM Removal by High-Temperature Combustion
<ol>
	<li>Measure ~1-2 g of soil (sieved, air dried) into a porcelain crucible using a spatula.</li>
	<li>Heat at 550 &#176;C for 3 h using a muffle furnace. 
	NOTE: This is an example method of SOM removal using combustion at a relatively high temperature. Refer to Discussion on alternative procedures (e.g., temperature).</li>
</ol>
4. DRIFT Spectroscopy
NOTE: For this example, the FTIR spectrometer software listed in the Table of Materials will be used.
<ol>
	<li>Acquire spectra of untreated soil and mineral enriched reference sample (treated to remove SOM).
	<ol>
		<li>Prepare the soil samples.
		<ol>
			<li>Dilute the samples (optional).
			<ol>
				<li>Use analytical grade KBr (or other halide salt) dried at 105 &#176;C and stored in a desiccator to remove residual moisture. For soil samples, effective KBr dilutions can be achieved at a range of 1-33%, in contrast to &#60; 1% for pure compounds.</li>
				<li>Mix soil and KBr for a final sample size of 100-400 mg. For example, for a 3% dilution, gently grind 12 mg of dry sample with 60 mg of KBr for 60 s with an agate mortar and pestle. Then, &#39;fold in&#39; 328 mg of KBr to fully homogenize the sample.</li>
				<li>Use serial dilutions with KBr to obtain a high final dilution rate ( &#60; 1%). Perform replicate dilutions to ensure reproducibility, especially since diluted samples use 101-102 less soil than neat samples.</li>
			</ol>
			</li>
			<li>Grind untreated and treated soil samples to similar consistency by hand grinding and sieving (e.g., 250 &#181;m using a 60# sieve). 
			NOTE: Compared to hand grinding, greater consistency is facilitated by automation, in particular by ball milling. However, the relatively small amount of soil used in SOM removal (e.g., 1-3 g for ashing due to crucible volume) means that hand grinding may be more practical.</li>
		</ol>
		</li>
		<li>Collect the background spectrum.
		<ol>
			<li>Load a sample of KBr (ground in the same manner as soil samples (see 4.1.1.2) to mimic soil matrix effects) into a sample cup or plate well. 
			NOTE: The &#34;background spectrum&#34; is different from the mineral-enriched reference spectrum (see 4.1.3) used for performing subtractions. The background spectrum will be used by the software to remove atmospheric and other ambient absorbances during collection of spectra on soil samples. All software descriptions are specific to the chosen software and will need to be adapted to other software.</li>
			<li>Purge the spectrometer chamber with CO2- and H2O-scrubbed air (via a purge gas generator) or with N2 gas for greater consistency in collection conditions. For example, collection of spectra under ambient atmosphere may entail small fluctuations in humidity and CO2 that can cause changes in absorbance spectra. 
			NOTE: Newer spectrometers may have mirrors (e.g., gold, SiC) that can potentially reduce humidity effects.</li>
			<li>Collect a background spectrum using the same detector and acquisition parameter settings, including scan number, wavenumber range, and resolution, that will be used to collect spectra of samples.
			<ol>
				<li>Open the drop-down menu for Experiment and select the desired experimental collection method (e.g., acquisition mode). 
				NOTE: In this example using chosen spectrometer (see the Table of Materials), the selected method is iS50 Main Compartment.</li>
				<li>Click the Experimental Setup icon to select spectral acquisition parameters.</li>
				<li>Under the Collect tab, check that the number of scans and resolution are appropriate for experimental objectives; for example, a common setting for DRIFT spectra of neat soils is 128 scans at 4 cm-1 resolution. Click Ok to save changes.</li>
				<li>Click the Collect Background icon to collect a background spectrum. Save the background spectrum for use in the collection of spectra of soils (treated and untreated).</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Acquire spectra of soil samples. 
		NOTE: Use the same acquisition parameters to collect background and sample (untreated soil, mineral-enriched soil) spectra. Differences among detectors in acquisition time and resolution pose trade-offs that impact collection time and spectral quality. Typical scan numbers for soil spectra range from 128-512 scans. Scan number can be decreased and replicates averaged to obtain a total target scan number. For example, two analytical replicates - the same sample loaded in two separate wells - can be collected using 64 scans each and averaged for a total of 128 scans.
		<ol>
			<li>Load the soil sample. To ensure consistent loading and minimize surface roughness, pour samples into the sample cup (or well) to the point of slightly overfilling above the lip or edge of the cup. Then, surface-smooth the soil in the cup using a flat edge (e.g., razor) such that the height of soil sample in the cup is flush with the lip of the cup. 
			NOTE: Due to the interaction of infrared light with a matrix such as soil in diffuse reflectance mode, sample loading can influence DRIFT spectra. Samples should not be tamped or subjected to pressure because packing density can affect absorbance. Finer particle size of samples ensures greater ease of surface smoothing (see 4.1.2.1). Depending on the spectrometer model and the sample density, the mass of sample needed to fill a sample cup will range from 300 to 600 mg. In the case of plate wells, this also depends on the well size. Plates with a greater number of wells will have smaller wells and will therefore require less sample. For example, 96-well plates commonly have a well volume of 360 &#181;L whereas 24-well plates have a well volume of 3.4 mL.</li>
			<li>Collect spectra of untreated and treated soil samples. First check that the background spectrum collected previously (see 4.1.2.3.4) is used. Click Experimental Setup. Under the Collect tab, select Use specified background file and load the background spectrum file. Click Ok to save changes. To commence spectral collection on the soil, click Collect Sample. 
			NOTE: Re-load the same sample in a different well or sample cup to collect the replicate spectra to account for scattering artifacts produced by surface roughness and by variability in matrix density.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Perform spectral subtractions. 
	NOTE: The subtraction factor (SF) weighs the degree to which absorbances in the mineral reference spectrum are subtracted from absorbances at the corresponding wavenumber in the spectrum of the untreated soil. For subtractions focused on improving resolution of organic absorbances to characterize SOM, it is recommended to utilize the entirety of the MIR afforded by most spectrometers (e.g., 4,000 to 650 or 400 cm-1, depending on the detector). The next steps describe an empirical method for determining the SF. All software descriptions are specific to the chosen software and will need to be adapted to other software.
	<ol>
		<li>Zero out peaks by using the subtraction option of the software program to change the subtraction factor (SF) to minimize or reduce a target mineral peaks and/or mineral peaks, and/or to maximize a linear baseline14.</li>
		<li>Simultaneously select the untreated and treated soil spectra and click the Subtract icon (top center of screen);the first spectrum selected (untreated soil) will be the spectrum from which the second spectrum (treated soil) will be subtracted.</li>
		<li>Use the vertical toggle bar or arrows to increase or decrease the SF (left-hand of screen). Observe the changes in the previewed subtraction spectrum.
		<ol>
			<li>Use this iterative feature to determine an appropriate SF as described in Representative Results. The numerical SF value appears in the middle of the toggle bar. To adjust the range of SF values, use the Finer and Coarser buttons.</li>
		</ol>
		</li>
		<li>Click Add (upper right-hand of screen) to load the calculated subtraction spectrum into a window. 
		NOTE: Since the majority of mineral absorbances are not linear with concentration in most (if not all) soil samples, it is usually not feasible to remove all mineral peaks. It is recommended that mineral peaks considered less prone to inversion (e.g., quartz-like Si-O at 2,100-1,780 cm-1)14 be used as the target peak to zero-out by adjusting the SF.</li>
		<li>Record and report methodological details on how the subtraction was performed with sufficient detail to allow independent calculation of the same subtraction spectrum from the untreated soil spectrum, including: (1) the wavenumber region used for subtraction, (2) the SF or range of SFs used, and (3) the (mineral) peak or region targeted for zero-ing out. 
		NOTE: A good test of the reliability of a subtraction is to have it performed anew by the same user and/or independently by a different user using the reported subtraction parameters.</li>
	</ol>
	</li>
	<li>Interpret the spectra.
	<ol>
		<li>Perform spectral interpretation using various resources available to analyze and interpret the resulting subtraction spectra, in particular assignments of absorbances to organic functional groups4. 
		NOTE: Other uses of subtraction spectra include multivariate analysis (e.g., principal component analysis), chemometric prediction of soil analytes27, and even forensic fingerprinting9.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The method of removing SOM carries two considerations: 1) the amount of SOM removed, and 2) absorbance artifacts in the resulting mineral reference spectrum. It is fortunately possible&#8212; and arguably necessary&#8212; to identify and quantity these issues in order to avoid biased interpretations of SOM composition from the resulting subtraction spectrum. Ideally, spectral subtractions would employ a mineral-only reference spectrum to yield a spectrum of &#39;pure&#39; SOM. In reality, the resulting subtraction spectrum exhibits absorbances corresponding to SOM that are enhanced relative to the original (untreated) soil spectrum. This is because non-linear absorbance of mineral components in soil samples prevents complete subtraction of all mineral absorbances. Artifactual mineral absorbances and/or incomplete removal of SOM limit interpretation of specific absorbance features or interpretation of the total SOM pool, respectively.
As evidenced by the examples shown here, the two common methods for removing SOM to obtain mineral reference spectra (thermal combustion or ashing, and chemical oxidation) tend to engender a trade-off between the degree of SOM removal and spectral artifacts. These have distinct consequences for the interpretation of subtraction spectra. For the same reason that it is used in the loss on ignition (LOI) method to estimate SOM content, high-temperature ashing (&#8805; 350 &#176;C) removes nearly all SOM but is likely to alter crystalline mineral structures and thus produce changes in mineral absorbance bands. As illustrated here, these will manifest as artifacts in the subtraction spectrum if spectral contributions from minerals before and after ashing differ. The thermal sensitivity of minerals, which can be mineral-specific, forces a compromise on the utility of spectral subtractions calculated with reference spectra obtained by ashing, especially at high temperatures (e.g., 550 &#176;C). Mineral alterations specific to mineral type and ashing temperature include dehydroxylation, interlayer collapse and dehydration41,42,43, and have been documented in spectra of soils of diverse mineralogy10,28,44,45.
Information on the mineralogy of a soil sample can be used beforehand to determine an ashing temperature that avoids or minimizes mineral alteration (e.g., interlayer collapse of kaolinite at or above 350 &#176;C). Mineral artifacts can be identified and anticipated by subjecting a soil sample to ashing at varying temperatures and comparing the resulting spectra with the spectra of untreated soil and pure mineral standards10,46. One may also constrain sample set mineralogy in order to maintain subtraction artifacts that result from ashing constant across soil samples. Finally, lower ashing temperatures (e.g., 350 &#176;C) can be used to enhance some organic bands10,47. Similar to chemical oxidations, the use of lower temperatures generally comes at a cost of lower28 (Table 2) and potentially selective10 SOM removal.
Chemical oxidations are in general less likely to cause mineral alteration, in particular for phyllosilicates, but may impact other mineral fractions such as iron (hydr)oxides. If known, the mineralogy of soil samples should be considered when selecting an oxidant. For example, sodium hypochlorite can remove relatively high amounts of SOM with minimal or no dissolution of mineral oxides, in contrast to other oxidizing agents (e.g., hydrogen peroxide)48,49,50,51. These effects are expected to depend on mineralogy and/or SOM (e.g., a high proportion of mineral-bound organic matter may be more resistant to some but not other oxidations).
The reason why zero-ing out target mineral bands is a better strategy than relying on software-calculated or a priori SFs is that these may need to be adjusted if target mineral bands are not fully removed. As this is common for mineral bands due to high phyllosilicate concentrations in soil samples and thus non-linear absorbance in spectra (e.g., Si-O stretch at 1,100-950 cm-1)22, especially for the widespread practice of analyzing neat samples (no halide salt dilution), the adjustment of the SF should be explored to minimize mineral bands as per experimental objectives.
Spectral subtractions offer a marked improvement on the ability of soil spectra to be used to interpret organic functional groups that compose SOM by improving the interpretability of organic absorbances in regions with overlapping mineral absorbances. Even organic bands though to be relatively mineral-free can benefit from spectral subtractions. For example, the aliphatic C-H stretch at 3,000-2,800 cm-1 is considered to be the least mineral-confounded organic band in soil spectra22, but its resolution can still be markedly improved with spectral subtractions52. Challenges to interpretation of FTIR spectra of a chemically complex sample such as SOM should not be conflated with the limitation of the method or its modifications (e.g., subtractions) because such complexity is an inherent attribute of SOM, which also challenges other spectroscopic approaches (e.g., nuclear magnetic resonance spectroscopy)1,5.
Spectral subtractions offer a unique strategy to address constraints inherent to spectroscopic analysis of SOM arising from mineral dominance of soil samples, and with strong complementarity to other methods with their own distinct limitations5,14. For example, a separate approach to improve FTIR spectroscopy analysis of SOM is the pre-treatment of soils with hydrofluoric acid (HF) to remove a majority of the mineral component. Since demineralization of soil samples is routine for NMR spectroscopic analysis, the same HF-treated soils are often also analyzed by FTIR spectroscopy. However, demineralization with HF can engender significant losses of SOM that compromise its utility as a stand-alone approach53,54. For example, nearly one-third of SOM of soil A (Tables 1 and 2) was removed during HF treatment5.
It is important to note that the utility of subtractions for analysis of soils is to enhance potential organic bands of interest at a particular wavenumber(s) rather than yielding a complete subtraction of all interfering mineral absorbances55,56,57,58. For this reason, subtractions may be more or less suited for particular spectral regions. Nguyen et al.4 recognized that subtractions were likely to be most useful to enhance organic bands at 1,800-1,600 cm-1, which Reeves III22 diagnosed as a spectral region for which accurate subtractions were possible even with artifacts from reference spectra obtained by high-temperature ashing. Mineral Si-O absorbances in this region subtract accurately, in contrast to other regions (e.g., 1,000-400 cm-1) due to non-linear absorbance and inversions of mineral bands such as Si-O at 1,050-980 cm-1 37,59.
A potentially fruitful area of future work is to systematically address soil properties expected to entail sample-specific artifacts introduced by the method of SOM removal. The main challenge to spectral subtractions is the difficulty of obtaining a mineral reference spectrum that has near or full SOM removal yet minimal or no artifacts from SOM removal. Current methods of high-temperature ashing and chemical oxidation engender a trade-off between these two objectives. Identifying mineralogy-specific artifacts detectable in FTIR spectra across a range of SOM removal conditions and soil types, as well as pure mineral standards, would be a first step toward establishing soil-specific recommendations22. Alternative SOM removal methods have yet to be systematically investigated. Promising removal methods include low-temperature ashing (often performed under low pressure) and for chemical oxidations, hydrogen peroxide and sodium peroxodisulphate14.
Spectra of soils demineralized using HF can be used to compare subtraction spectra obtained by various methods. HF can be used to dissolve and remove a majority of the mineral component of soil samples, yielding an SOM-enriched sample. Since the spectrum of an HF-treated soil sample should in theory be similar to a subtraction spectrum of the same soil using a mineral reference spectrum obtained by full removal of SOM, comparing the spectra of SOM obtained by HF demineralization versus subtraction could be used to cross-validate absorbance bands, identify artifacts and increase confidence in spectral assignments5, in particular in the regions prone to subtraction artifacts via inversion (&#60; 1,200 cm-1). However, incomplete and/or selective removal of mineral and organic components during HF treatment means that spectra of HF-treated soils may not offer an &#39;objective&#39; spectrum of SOM, only an operational comparison which, not unlike spectral subtractions, must be carefully considered in light of potential artifacts.

Soil organic matter (SOM) is a minor constituent by mass in most soil samples but is implicated in multiple properties and processes underlying soil functions, such as nutrient cycling and carbon sequestration1. Characterizing the composition of SOM is one of several approaches to link SOM formation and turnover with its role(s) in soil functions2,3. One method of characterizing SOM composition is Fourier transform infrared (FTIR) spectroscopy, which offers detection of functional groups that constitute organic matter in soils and other environmental samples (e.g., carboxyl C-O, aliphatic C-H)4. However, the utility of FTIR spectroscopy for revealing SOM functional group composition is challenged by the dominant mineral component for the majority of soils (typically &#62; 95% mass) due to strong inorganic absorbances that challenge or severely limit detection and interpretation of organic absorbances.
Spectral subtractions offer a way to improve FTIR spectroscopic characterization of organic matter in soil samples. Subtracting mineral absorbances from the soil spectrum can be used to enhance absorbances of organic functional groups of interest in the analysis of SOM composition
(Figure 1).
Advantages of spectral subtractions over standard FTIR spectroscopy (i.e., soil spectra) include:
(i) Improved resolution and interpretation of organic absorbance bands compared to normal soil spectra. Though interpretation of organic bands in soil spectra can be performed by assuming that the relative differences in absorbance are due to differences in organic functional groups, this limits comparisons to samples with the same mineralogy and relatively high SOM content, and may be less sensitive to changes in organic bands, even those considered to be relatively mineral-free (e.g. aliphatic C-H stretch)5
(ii) Analysis of soils beyond high SOM samples or organic matter-enriched extracts or fractions
(iii) Highlighting changes induced by experimental treatments from mesocosm to field scales6
Additional applications of spectral subtractions in FTIR analysis of SOM include complementing structural and molecular characterizations (e.g., NMR spectroscopy, mass spectrometry)5,7, identifying the composition of SOM removed by an extraction or destructive fractionation8, and fingerprinting SOM composition for forensic purposes9. This method is applicable to a wide variety of mineral-organic mixtures beyond soils, including sediment10, peat11, and coal12,13.
The potential of spectral subtractions to improve FTIR spectroscopic characterization of SOM is demonstrated using examples of organic matter removal to obtain mineral reference spectra, and then, using these mineral reference spectra, performing and evaluating ideal and non-ideal spectral subtractions. This demonstration focuses on diffuse reflectance infrared Fourier transform (DRIFT) spectra collected in the mid-infrared region (MIR, 4,000 - 400 cm-1), as this is a widespread approach for the analysis of soil samples4.
The two example methods of SOM removal for obtaining a mineral-enriched reference spectrum are (i) high-temperature combustion (&#39;ashing&#39;) and (ii) chemical oxidation, using dilute sodium hypochlorite (NaOCl). It should be noted that these are examples of commonly employed SOM removal methods, rather than prescriptive recommendations. Other methods of SOM removal may offer reduced mineral artifacts and/or enhanced removal rates (e.g., low-temperature ashing)14. High-temperature ashing was one of the first methods used to obtain mineral-enriched reference spectra for performing subtractions, initially for OM-enriched samples derived from soils (e.g., dissolved organic matter, litter)15,16 followed by its application to bulk soil samples17,18. The example chemical oxidation used to remove SOM is based on the method of NaOCl oxidation described by Anderson19. This was originally developed as a pretreatment for removing organic matter in soil samples prior to X-ray diffraction (XRD) analysis, and has been investigated as a potential chemical fractionation sensitive to SOM stabilization20,21. Both high-temperature removal and chemical oxidation using NaOCl can entail soil-specific artifacts and have limitations on spectral interpretation that should be considered when selecting a method of SOM removal14,22.

<table><tbody><tr><td>Nicolet iS50 spectrometer</td><td>Thermo Fisher Scientific</td><td>912A0760</td><td>infrared spectrometer used to collect spectra</td></tr><tr><td>EasiDiff</td><td>Pike Technologies</td><td>042-1040</td><td>high throughput sample holder</td></tr><tr><td>OMNIC</td><td>Thermo Fisher Scientific</td><td>INQSOF018</td><td>software used to perform subtractions</td></tr><tr><td>6% v/v sodium hypochlorite</td><td>Clorox</td><td>n/a</td><td>generic store-bought bleach for oxidative removal of soil organic matter</td></tr><tr><td>Type 47900 Furnace</td><td>VWR International</td><td>30609-748</td><td>muffle furnace for ashing soils to removal soil organic matter</td></tr><tr><td>VWR Gooch Crucibles, Porcelain&amp;nbsp;</td><td>VWR International</td><td>89038-038</td><td>crucibles for ashing</td></tr><tr><td>VWR Tube 50 mL Sterile CS500&amp;nbsp;</td><td>VWR International</td><td>89004-364</td><td>for sodium hypochlorite</td></tr><tr><td>Forced air oven</td><td>VWR International</td><td>89511-414</td><td>for drying soils after oxidation and water washes</td></tr><tr><td>VersaStar pH meter</td><td>Fisher Scientific</td><td>13 645 573</td><td>for measuring pH of oxidation solution</td></tr></tbody></table>

improving infrared spectroscopy characterization of soil organic matter with spectral subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic bonds that constitute the organic component of soils. However, the relatively low organic matter content of soils (commonly &#60; 5% by mass) and absorbance overlap of mineral and organic functional groups in the mid-infrared (MIR) region (4,000-400 cm-1) engenders substantial interference by dominant mineral absorbances, challenging or even preventing interpretation of spectra for SOM characterization. Spectral subtractions, a post-hoc mathematical treatment of spectra, can reduce mineral interference and enhance resolution of spectral regions corresponding to organic functional groups by mathematically removing mineral absorbances. This requires a mineral-enriched reference spectrum, which can be empirically obtained for a given soil sample by removing SOM. The mineral-enriched reference spectrum is subtracted from the original (untreated) spectrum of the soil sample to produce a spectrum representing SOM absorbances. Common SOM removal methods include high-temperature combustion (&#39;ashing&#39;) and chemical oxidation. Selection of the SOM removal method carries two considerations: (1) the amount of SOM removed, and (2) absorbance artifacts in the mineral reference spectrum and thus the resulting subtraction spectrum. These potential issues can, and should, be identified and quantified in order to avoid fallacious or biased interpretations of spectra for organic functional group composition of SOM. Following SOM removal, the resulting mineral-enriched sample is used to collect a mineral reference spectrum. Several strategies exist to perform subtractions depending on the experimental goals and sample characteristics, most notably the determination of the subtraction factor. The resulting subtraction spectrum requires careful interpretation based on the aforementioned methodology. For many soil and other environmental samples containing substantial mineral components, subtractions have strong potential to improve FTIR spectroscopic characterization of organic matter composition.

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Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

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12.4K Views.  University of Illinois Urbana-Champaign. SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

Video: Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF relies on sequential density separation and sedimentation steps to isolate (1) the free light fraction (uncomplexed organic matter), (2) the occluded light fraction (uncomplexed organic matter trapped in soil aggregates) and (3) a variable number of heavy fractions (soil minerals and their associated organic matter) differing in composition. Provided that the parameters of the CSDF (dispersion energy, density cut-offs, sedimentation time) are properly selected, the method yields heavy fractions of relatively homogeneous mineral composition. Each of these fractions is expected to have a different complexing ability towards organic matter, rendering this a useful method to isolate and study the nature of organo-mineral interactions. Combining density and particle size separation brings an improved resolution compared to simple size or density fractionation methods, allowing the separation of heavy components according to both mineralogy and size (related to surface area) criteria. As is the case for all physical fractionation methods, it may be considered as less disruptive or aggressive than chemically-based extraction methods. However, CSDF is a time-consuming method and furthermore, the quantity of material obtained in some fractions can be limiting for subsequent analysis. Following CSDF, the fractions may be analyzed for mineralogical composition, soil organic carbon concentration and organic matter chemistry. The method provides quantitative information about organic carbon distribution within a soil sample and brings light to the sorptive capacity of the different, naturally-occurring mineral phases, thus providing mechanistic information about the preferential nature of organo-mineral interactions in soils (i.e., which minerals, what type of organic matter).

Combined size and density fractionation (CSDF) is a method to physically separate soil into fractions differing in texture (particle size) and mineralogy (density). The purpose is to isolate fractions with different reactivities towards soil organic matter (SOM), in order to better understand organo-mineral interactions and SOM dynamics.

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Raman and IR Spectroelectrochemical Methods as Tools to Analyze Conjugated Organic Compounds

In the presented work, two spectroelectrochemical techniques are discussed as tools for the analysis of the structural changes occurring in the molecule on the vibrational level of energy. Raman and IR spectroelectrochemistry can be used for advanced characterization of the structural changes in the organic electroactive compounds. Here, the step-by-step analysis by means of Raman and IR spectroelectrochemistry is shown. Raman and IR spectroelectrochemical techniques provide complementary information about structural changes occurring during an electrochemical process, i.e. allows for the investigation of redox processes and their products. The examples of IR and Raman spectroelectrochemical analysis are presented, in which the products of the redox reactions, both in solution and solid state, are identified.

A protocol of step-by-step Raman and IR spectroelectrochemical analysis is presented.

In the presented work, two spectroelectrochemical techniques are discussed as tools for the analysis of the structural changes occurring in the molecule ...

Chemistry

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the prediction of the components in industrial processes. The ability to record spectra for solid and liquid samples without any pretreatment is advantageous and the method is widely used. However, the disadvantages of analyzing high-dimensional near-infrared spectral data include information redundancy and multicollinearity of the spectral data. Thus, we propose to use partial least squares regression method, which has traditionally been used to reduce the data dimensionality and eliminate the collinearity between the original features. We implement the method for predicting the o-cresol concentration during the production of polyphenylene ether. The proposed approach offers the following advantages over component regression prediction methods: 1) partial least squares regression solves the multicollinearity problem of the independent variables and effectively avoids overfitting, which occurs in a regression analysis due to the high correlation between the independent variables; 2) the use of the near-infrared spectra results in high accuracy because it is a non-destructive and non-polluting method to obtain information at microscopic and molecular scales.

The protocol describes a method of predicting o-cresol concentration during the production of polyphenylene ether using near-infrared spectroscopy and partial least squares regression. To describe the process more clearly and completely, an example of predicting the o-cresol concentration during the production of polyphenylene is used to clarify the steps.

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the ...

Engineering

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated biochemical information. FT-IR imaging of tissue is based on the principle that different regions of the mid-infrared are absorbed by different chemical bonds (e.g., C=O, C-H, N-H) within cells or tissue that can then be related to the presence and composition of biomolecules (e.g., lipids, DNA, glycogen, protein, collagen). In an FT-IR image, every pixel within the image comprises an entire Infrared (IR) spectrum that can give information on the biochemical status of the cells that can then be exploited for cell-type or disease-type classification. In this paper, we show: how to obtain IR images from human tissues using an FT-IR system, how to modify existing instrumentation to allow for high-definition imaging capabilities, and how to visualize FT-IR images. We then present some applications of FT-IR for pathology using the liver and kidney as examples. FT-IR imaging holds exciting applications in providing a novel route to obtain biochemical information from cells and tissue in an entirely label-free non-perturbing route towards giving new insight into biomolecular changes as part of disease processes. Additionally, this biochemical information can potentially allow for objective and automated analysis of certain aspects of disease diagnosis.

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Fourier Transform Infrared (FT-IR) spectroscopic imaging is a fast and label-free approach to obtain biochemical data sets of cells and tissues. Here, we demonstrate how to obtain high-definition FT-IR images of tissue sections towards improving disease diagnosis.

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated ...

Medicine

Optimizing VIS-SWIR Spectroscopy for Biological Molecules

Characterization of Biological Absorption Spectra Spanning the Visible to the Short-Wave Infrared

For noninvasive light-based physiological monitoring, optimal wavelengths of individual tissue components can be identified using absorption spectroscopy. However, because of the lack of sensitivity of hardware at longer wavelengths, absorption spectroscopy has typically been applied for wavelengths in the visible (VIS) and near-infrared (NIR) range from 400 to 1,000 nm. Hardware advancements in the short-wave infrared (SWIR) range have enabled investigators to explore wavelengths in the ~1,000 nm to 3,000 nm range in which fall characteristic absorption peaks for lipid, protein, and water. These molecules are difficult to visualize in the VIS-NIR and can provide label-free sources of biological contrast. Furthermore, lower SWIR absorption has been observed for melanin, the primary chromophore responsible for skin pigmentation. In vivo&#160;optical devices like clinically standard pulse oximeters have been found to have reduced accuracy in people with darkly pigmented skin, possibly because of the stronger melanin absorption in the VIS range. Thus, error associated with skin pigmentation could be reduced by using devices operating in the SWIR. Optical instrument design is facilitated by the understanding of the absorption properties of core tissue components from the VIS to the SWIR range. This article describes protocols and instrumentation for obtaining VIS-SWIR absorption spectra of common tissue absorbers: oxygenated hemoglobin, deoxygenated hemoglobin, melanin, water, and lipid.

Biological samples have distinct optical characteristics in the short-wave infrared (SWIR) compared to the visible (VIS) and near-infrared (NIR) wavelength ranges. However, recording pure SWIR absorption spectra is challenging and rarely conducted for biological molecules. This article presents a method to characterize the VIS-SWIR absorption spectra of biological absorbers.

For noninvasive light-based physiological monitoring, optimal wavelengths of individual tissue components can be identified using absorption spectroscopy. ...

Bioengineering

Lead Analysis of Soil Using Atomic Absorption Spectroscopy

Source: Laboratories of Margaret Workman and Kimberly Frye - Depaul University
Lead occurs naturally in soil, in levels ranging from 10-50 ppm. However, with the widespread use of lead in paint and gasoline in addition to contamination by industry, urban soils often have concentrations of lead significantly greater than background levels &#8211; up to 10,000 ppm in some places. Ongoing problems arise from the fact that lead does not biodegrade, and instead remains in the soil.
Serious health risks are associated with lead poisoning, where children are particularly at risk. Millions of children in the U.S. are exposed to soil containing lead. This exposure can cause developmental and behavioral problems in children. These problems include learning disabilities, inattention, delayed growth, and brain damage. The Environmental Protection Agency has set a standard for lead in soil at 400 ppm for play areas and 1,200 ppm for non-play areas.
Lead is also of concern in soil, when it&#8217;s used for gardening. Plants take up lead from the soil. Therefore, vegetables or herbs grown in contaminated soil can lead to lead poisoning. In addition, contaminated soil particles can be breathed in while gardening or brought into the house on clothing and footwear. It is recommended that soils with lead levels greater than 400 ppm should not be used for gardening. It is further recommended that soil with lead levels between 100 and 400 ppm not be used for leafy vegetables or herbs, because lead can be stored in the leaves. On a similar note, root vegetables should not be grown in this soil, because lead can also accumulate in plant roots.

Source: Laboratories of Margaret Workman and Kimberly Frye - Depaul University
Lead occurs naturally in soil, in levels ranging from 10-50 ppm. However, ...

Education

JoVE Science Education

Environmental Sciences

Environmental Science

Infrared Spectroscopy

Source: Vy M. Dong and Zhiwei Chen, Department of Chemistry, University of California, Irvine, CA
This experiment will demonstrate the use of infrared (IR) spectroscopy (also known as vibrational spectroscopy) to elucidate the identity of an unknown compound by identifying the functional group(s) present. IR spectra will be obtained on an IR spectrometer using the attenuated total reflection (ATR) sampling technique with a neat sample of the unknown.

Infrared Spectroscopy: Characterization of Functional Groups

Source: Vy M. Dong and Zhiwei Chen, Department of Chemistry, University of California, Irvine, CA
This experiment will demonstrate the use of infrared ...

Organic Chemistry II

Infrared (IR) Spectroscopy: Overview

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their functional groups, which allows IR spectroscopy to be used to determine the present functional groups. Vibrational IR spectroscopy is performed in the 2.5 to 25 &#181;m wavelength range. A&#160; molecule is considered IR active if it exhibits a change in dipole moment during vibration when absorbing IR radiation. For a molecule to absorb IR radiation effectively, its vibrations must fluctuate its dipole moments, allowing it to interact with the electromagnetic field of the IR light.
Since different functional groups absorb IR radiation at varying frequencies, the IR spectrum is similar to the &#34;fingerprint&#34;&#160;for each molecule. IR spectroscopy is primarily employed in qualitative analysis to identify the functional groups in organic and inorganic compounds by comparing their vibrational frequencies with known compounds. Additionally, It can determine the concentration of a substance by applying the Beer-Lambert law, where absorbance is proportional to concentration.

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation ...