The U.S. Environmental Protection Agency, through its Office of Research and Development, funded and managed the research described here. This document has been reviewed by the U.S. Environmental Protection Agency, Office of Research and Development, and approved for publication. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. This research was supported in part by an appointment to the Postdoctoral Research Program at the National Exposure Research Laboratory administered by the Oak Ridge Institute for Science and Education through Interagency Agreement DW89992431601 between the U.S. Department of Energy and the U.S. Environmental Protection Agency.

1. Collection of water samples
<ol>
	<li>Preparation of PFAS Standard Stocks
	<ol>
		<li>Prepare a PFAS standard mixture in methanol containing any targeted compounds of interest (e.g., PFOA, PFOS, HFPO-DA) at 1 ng/&#181;L. This is the Native PFAS Mixture. Commercially prepared mixtures are also available (i.e., PFAC Mix A and Mix B).</li>
		<li>Prepare a standard mixture containing matched stable isotope labeled (SIL) PFAS compounds (e.g., 13C4-PFOA, 13C8-PFOS, 13C3-HFPO-DA) at 1 ng/&#181;L. This is the IS PFAS Mixture. Commercially prepared mixtures are also available (i.e., MPAFC Mix A and Mix B). 
		NOTE: If an SIL version of the targeted PFAS is unavailable, a surrogate with similar structure and chain length can be used (e.g., 13C2-PFHxA for HFPO-DA)</li>
	</ol>
	</li>
	<li>Preparation of Field Blank (FB), Spike Blank (SB) samples
	<ol>
		<li>Fill two, clean high-density polypropylene (HDPE) or polypropylene (PP) bottles with 1,000 mL of laboratory deionized (DI) water, known to be PFAS free. 
		CAUTION: PFAS materials frequently have undefined toxicity and/or carcinogenicity. Care should be taken to avoid oral or skin exposure to standards or stock solutions.</li>
		<li>Add a quantity of PFAS standard mixture to one of the bottles at a final concentration equivalent to the expected sample concentrations (e.g., 100 ng/L). This is the Spike Blank (SB).</li>
		<li>Add 5 mL of 35% nitric acid preservative to the Spike Blank.</li>
		<li>Carry both SB sample and the unspiked field blank to the sampling location as controls.</li>
	</ol>
	</li>
	<li>Field sampling 
	NOTE: Sample collector should wear nitrile gloves and sample from flowing systems where possible. Tap samples should be allowed to flow and equilibrate prior to sampling (2-3 min).
	<ol>
		<li>Collect 500-1,000 mL of water from the field location in a clean HDPE or PP bottle.</li>
		<li>Add 5 mL of 35% nitric acid preservative to sample bottles and field blank. 
		CAUTION: Nitric acid is corrosive and a strong oxidizer</li>
	</ol>
	</li>
</ol>
2. Sample extraction
NOTE: PFAS are ubiquitous and persistent. Ensure that all solvents are of the highest grade and have been analyzed for low level PFAS contamination. Thoroughly rinse all laboratory equipment used for preparing standards before preparing blanks and samples.
<ol>
	<li>Sample pretreatment
	<ol>
		<li>Pour each sample into a separate, pre-cleaned 1 L HDPE graduated cylinder and record the exact volume.</li>
		<li>Add 10 mL of methanol to the emptied sample bottle, cap it, and shake well to rinse adsorbed PFAS from the bottle interior.</li>
		<li>Return the measured water sample to the rinsed bottle with the methanolic rinse.</li>
	</ol>
	</li>
	<li>Standard curve for quantitation
	<ol>
		<li>Fill eight, 1 L HDPE/PP bottles with PFAS-free DI water.</li>
		<li>Select eight evenly spaced concentrations covering the desired quantitation range. For example: 10, 25, 50, 100, 250, 500, 750 and 1,000 ng/L for a range of 10-1,000 ng/L.</li>
		<li>Add a quantity of Native PFAS mix to each bottle to yield the final PFAS concentrations in 2.2.2 (e.g., 100 &#181;L PFAS Mix A to 1L of DI water = 100 ng/L).</li>
	</ol>
	</li>
	<li>Internal standard addition 
	NOTE: Addition of stable isotope labeled internal standard (IS) is necessary only if quantitative results are desired in addition to non-targeted analysis.
	<ol>
		<li>Add the IS PFAS mixture to each sample at a concentration approximating the midpoint of the calibration curve (e.g., 250 &#181;L of IS PFAS mix = 250 ng/L)</li>
	</ol>
	</li>
	<li>Filtration
	<ol>
		<li>Filter samples through GF/A glass fiber filters (47 mm, 1.6 &#181;m pore size) under gentle vacuum into a pre-cleaned 1 L HDPE vacuum flask.</li>
		<li>If particulate matter remains in the bottle, rinse with additional deionized water into the filter. Return the filtered water to the sample bottle or a new container for solid phase extraction.</li>
	</ol>
	</li>
	<li>Solid phase extraction (SPE) 
	NOTE: Cartridge concentration described here uses a constant flow piston pump. Alternative methods of concentration using a vacuum manifold20 or using an on-line SPE-LC-MS17 setup are possible but not discussed.
	<ol>
		<li>Condition a weak anion exchange (WAX) cartridge with 25 mL of methanol.</li>
		<li>Condition the WAX cartridge with an additional 25 mL of deionized water.</li>
		<li>Position pump draw tubing in filtered sample bottles and label SPE cartridges with corresponding sample names.</li>
		<li>Pump 500 mL of sample water through the cartridge at a steady flow rate of 10 mL/min (500 mL total), discarding flow-through liquid to waste. 
		NOTE: Larger or smaller volumes can be concentrated depending on expected sample concentrations.</li>
		<li>Remove the cartridge from piston pump for elution. 
		NOTE: If concentrating additional samples using the same pump, the piston pump should be flushed with 25 mL of methanol before installing the next cartridge for equilibration.</li>
		<li>Transfer SPE cartridge to a vacuum manifold and equip with external glass reservoir.</li>
		<li>Flush SPE cartridge with 4 mL of 25 mM, pH 4.0 sodium acetate buffer under gentle vacuum. Discard flow through. Wash SPE cartridge with 4 mL of neutral methanol. 
		NOTE: Neutral wash fraction can be collected if specific nonionic polar analytes are expected. Otherwise, discard to waste</li>
		<li>Place a 15 mL polypropylene centrifuge tube beneath each SPE cartridge to collect eluent. Elute sample with 4 mL of 0.1% ammonium hydroxide in methanol.</li>
		<li>Remove elution tube and reduce eluate volume to 500 - 1,000 &#181;L by evaporation under dry nitrogen stream in a water bath at slightly elevated temperature (40 &#176;C).</li>
		<li>Concentrated sample extracts can be stored prior to analysis at room temperature.</li>
	</ol>
	</li>
	<li>Targeted LC-MS/MS quantitation
	<ol>
		<li>Dilute 100 &#181;L of sample extract with 300 &#181;L of 2 mM ammonium acetate buffer into an HPLC sample vial.</li>
		<li>Calibrate and equilibrate an HPLC and MS systems according to manufacturer&#39;s instructions. 
		NOTE: Background PFAS are commonly detected due to the use of fluoropolymer components of most LC systems and in sample vial septa. Confirm that the detectable levels in blanks is negligible before use. Modification of the LC system to replace Teflon components is suggested where possible. The use of an analytical &#34;hold-up&#34; column adjacent to the LC mixing valve is also suggested29.</li>
		<li>Prepare an analytical worklist consisting of the standard curve, samples, and an additional replicate of the standard curve to assess instrumental drift across the run. An example worklist is shown in Table 1.</li>
		<li>Analyze the samples using LC and MS methods established for the targeted compound(s) of interest. The example LC gradient is shown in Table 2 and MS method parameters are shown in Table 3 and Table 4. Further detailed discussion can be found in McCord et al.21.</li>
		<li>Generate a standard curve from the standard samples using the peak area ratio of the analyte to the internal standard versus the concentration of analyte. Generate a quadratic regression formula with 1/x weighting for concentration prediction9.</li>
		<li>Quantitate targeted analytes in each sample using the prepared standard curves and area ratio (standard area/IS area) for each measurement.</li>
		<li>If the concentration exceeds the calibration range, dilute the original sample with DI water spiked with the appropriate IS concentration and re-extract to bring the concentration into the appropriate range.</li>
	</ol>
	</li>
	<li>Non-targeted LC-MS/MS data collection
	<ol>
		<li>Dilute 100 &#181;L of sample extract with 300 &#181;L of 2 mM ammonium acetate buffer into an HPLC sample vial.</li>
		<li>Calibrate and equilibrate an HPLC and high-resolution MS according to manufacturer&#39;s instructions.</li>
		<li>Prepare an analytical worklist as in 2.6.2.</li>
		<li>Using the instrument software, collect LC-MS data in with a wide scan MS1 in data-dependent mode to collect MS/MS. Example LC gradient in Table 5. Further discussion of instrument settings can be found in Strynar et al.30 and Newton et al.31. 
		NOTE: For improved MS/MS quality data-dependent analysis can be carried out with a preferred ion list of a subset of features remaining after data processing in 2.8.1-2.8.8.</li>
	</ol>
	</li>
	<li>Non-targeted data processing 
	NOTE: Data analysis can be performed with a wide range of software and these methods do not reflect the only, or best method for an arbitrary dataset. Where possible, steps provide a generic description that can be carried out in alternate software. Processing of the example data used in this manuscript was carried out using vendor specific software (Software 1 and Software 2) as detailed in Newton et al.31.
	<ol>
		<li>Perform molecular feature extraction of chemical features using one of several open source software packages32,33 or vendor software to identify monoisotopic masses, retention times, and integrated peak areas of chemical features.
		<ol>
			<li>In Software 1, select Add/Remove Sample Files &#62; Add Files and select the raw data from the non-targeted experiment, then hit OK.</li>
			<li>In Software 1 select Batch Recursive Feature Extraction &#62; Open Method&#8230; to load a preestablished method, or manually edit software settings. Profinder settings for feature extraction are found in Table 6.</li>
			<li>In Software 1, after feature extraction, select File &#62; Export as CSV&#8230;, File &#62; Export as CEF&#8230;, or File &#62; Export as PFA&#8230; for further processing. CEF files are assumed for the remainder of the description.</li>
			<li>In Software 2 (MPP) create a new experiment with Type Unidentified and Workflow type Data Import Wizard and click OK.</li>
			<li>In MPP Select Data Files and locate the exported Software 1 results (either CEF or PFA) to import; then click Next until Alignment Parameter options appear.</li>
			<li>In MPP, set the Compound Alignment values to 0.0 (alignment was already performed in the feature extraction of Software 1, step 2.8.1.2) then click Next through the steps until Finish is available.</li>
		</ol>
		</li>
		<li>Filter identifications based on analytical reproducibility. Where multiple replicate samples are available, features should be present in &#62;80% of individual replicates and have an analytical coefficient of variation (CV) of &#60; 30%
		<ol>
			<li>In MPP select Experimental Setup &#62; Experiment Grouping and assign each raw file a group corresponding to its origin sample (i.e., replicates from the same source should be in the same group). Multiple groups can be created corresponding to nested variables (e.g., instrumental vs. technical replicates).</li>
			<li>In MPP select Experimental Setup &#62; Create Interpretation then select the experiment parameter (i.e., group) and click Next until Finish is available. This will create a category that future filtering can operate on.</li>
			<li>In MPP select Quality Control &#62; Filter by Frequency. Set Entity List to All Entities and the Interpretation to the sample Group(non-averaged) created in 2.8.2.2, then hit Next.</li>
			<li>For Input parameters, set entity retention at 80% of sampled in at least one condition then click Next until Finish is available. Name the list Frequency Filtered Features</li>
			<li>In MPP select Quality Control &#62; Filter on Sample Variability. Set the Entity List to the Frequency Filtered Features from 2.8.2.4 and the interpretation to Group(non-averaged), then hit Next.</li>
			<li>Select the radio button for Raw Data and the Range of Interest to Coefficient of variation &#60; 30%. Click Next &#62; Finish and save the list as CV Filtered Features.</li>
		</ol>
		</li>
		<li>Remove features where no samples have significantly higher (&#62;3 fold) abundance than the Field Blank (FB) sample.
		<ol>
			<li>In MPP select Analysis &#62; Fold Change. Set Entity List to CV Filtered Features and the Interpretation to the sample Group then hit Next. Select the fold change option to All against single condition and select condition FB or whatever the group name for the blank processed sample was.</li>
			<li>On the following screen, set the Fold-Change cutoff to 3.0 and click through to the end of the prompts. Save the list as FC Filtered List.</li>
		</ol>
		</li>
		<li>Perform binary comparisons of individual samples of interest against an appropriate background sample (e.g., upstream vs. downstream of a point source) to determine fold-changes for individual chemical features.
		<ol>
			<li>In MPP select Analysis &#62; Filter on Volcano Plot. Set the entity list to FC Filtered List and the Interpretation to Group.</li>
			<li>For the fold-change condition pair choose two samples for comparison (e.g., a paired upstream and downstream sample) and select test Mann-Whitney Unpaired.</li>
			<li>For preliminary analysis, do not select a value for multiple test correction on the following screen, click through to the result plot.</li>
			<li>On the results screen select a fold-change cutoff of 3.0 and a p-value cutoff to 0.1. Then Finish and export the list as Prelim Results.</li>
		</ol>
		</li>
		<li>For each feature remaining after filtering, generate predicted chemical formula(s) from the exact mass and composite mass spectrum.
		<ol>
			<li>In MPP, select Results Interpretation &#62; IDBrowser Identification and the Prelim Results entity list.</li>
			<li>In the IDBrowser select Identify all compounds using molecular formula generator (MFG) as the identification method.</li>
			<li>In the Generate Formula options add F to the Elements column and set the Maximum to 50, then select Finish. Following formula generation select Save and Return to return to MPP.</li>
			<li>In MPP, right click the filtered and MFG matched Entity List and select Export List. Save the results.</li>
		</ol>
		</li>
		<li>Examine the monoisotopic mass of species in the reduced significant chemical feature list for those containing mass defects indicative of fluorination; see Kind and Fiehn34.</li>
		<li>Note chemical series containing common polyfluorination motifs (CF2 (m/z 49.9968), CF2O (m/z 65.9917), CH2CF2O (m/z 80.0074), etc.) using a mass defect plot or software algorithm; see discussion section, Liu et al.17, Loos et al.35 , and Dimzon et al.36.</li>
		<li>Search predicted chemical formulas or neutral masses against the EPA Chemistry Dashboard database and/or other databases to return potential chemical structures.
		<ol>
			<li>Open the EPA Comptox Chemicals Dashboard Batch Search tool (https://comptox.epa.gov/dashboard/dsstoxdb/batch_search) and paste the list of identifiers (either formulas or masses) into the identifier box, after selecting the identifier type (i.e., MS-ready Formula or Monoisotopic Mass).</li>
			<li>Select Download Chemical Data&#8230; and also select any physical/chemical/toxicology data desired for potential matches from the dropdown.</li>
		</ol>
		</li>
		<li>Using chemical intuition and available reference data, remove unlikely matches from the potential chemical structure list for each formula based on feasibility due to chemical stability, physical properties such as ionizability or hydrophobicity, the presence of manufacturing chemicals from nearby sources, etc. In the absence of additional data, spectral feasibility can be ranked purely on the basis of literature prevalence; see McEachran et al.37.</li>
		<li>Confirm structures using available standards and/or targeted high-resolution MS/MS matching of fragments against spectra from databases, in silico theoretical spectra, or manual curation.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

Sample Handling and Preparation 
The inclusion of reference/spike standards are of paramount importance to any targeted analysis, as they provide a backstop for checking analytical validity. Lack of QC samples prevents any assessment of the accuracy of the results; the ubiquitous nature of fluorochemicals means that chance contamination of field samples, processing materials, or LC-MS system is not uncommon and must be accounted for. Further, it allows for the validation of the protocol regardless of variation in the day-to-day sample processing, as many of the steps can be highly variable, particularly the SPE and sample concentration steps. The extraction of both legacy and novel perfluorinated chemicals can be heavily influenced by the choice of stationary phase for concentration, and components of the source samples, such as pH and salinity46. The influence of sample conditions should be considered if particular classes of pefluorinated chemicals are of interest. Alternative sample preparation schemes for water extracts can be used if the laboratory setup is available and the downstream data analysis remains similar.
Targeted Data Analysis 
For compounds with available standards and matched, stable isotope labeled internal standards, the primary concerns for data analysis are instrumental and determination of method detection limits and suitable reporting ranges can be determined on a laboratory-by-laboratory basis using standard approaches, such as signal-to-noise ratio from low-level standard spikes47. In the absence of matched internal standards errors from mismatched matrix effects can occur, and accurate back-prediction of spiked samples can be used to estimate the accuracy of the measurements. When lacking standards to prepare a curve, a quantitative estimate of an unknown can be made by treating it identically to a closely matched standard compound, but errors in the estimate are on the order of 10+ fold with limited ability to quantify the uncertainty, see McCord, Newton, and Strynar21. In these cases, trend data can still be collected, but concentration estimates are inherently unreliable.
Non-targeted Data Analysis 
Peak picking settings have a substantial impact on the number of chemical features identified, but the quality of feature selection is also heavily impacted. The decisions of interest in peak picking are 1) intensity of individual masses to be included in spectra, the ion abundance threshold 2) the intensity of extracted chromatogram peaks to be considered features, the feature abundance threshold 3) feature detection frequency, the replicate threshold, and 4) analytical variation, the CV threshold (Figure 10).
Setting unrealistically low thresholds for peak picking results in an exponential increase in sample time to resolve additional features of increasingly low abundance (Table 7). The ion-abundance threshold filters mass spectral features where enough of the individual isotope abundances do not pass the threshold. This ideally selects only for features with quality MS spectra, ensuring they are real chemical features rather than instrumental noise, and allowing for formula prediction in downstream processing. An appropriate threshold is based on instrumental noise, ideally at least 3x the noise threshold for MS1 scans. Feature abundance threshold filters chemical features based on the intensity or area of the chromatographic feature extracted. This step enables rejection of low abundance peaks, which are typically of poor chromatographic quality, have high variances, or are the result of other poor software extraction. An appropriate threshold must be determined per experiment, and per matrix based on an acceptable level of poor feature generation (e.g., features below the threshold exhibit unacceptably poor chromatography). Further analytical QC can be used to reject features at the chromatographic level based on inconsistent identification in analytical and/or preparatory replicates (replicate threshold) or based on poor reproducibility across replicates (CV threshold). Appropriate levels depend on the quality of the peak integration software used and the chemical entities under investigation. For water soluble perfluorinated compounds and lightly optimized integration protocols, features should be identified in 80+% of analytical replicates and CVs are expected to fall below 30%, as detailed in the methods section.
The peaks detected from non-targeted analysis do not yield quantitative estimates of the concentrations of the materials detected. Further, the identity of true unknowns can be difficult to confirm because novel compounds are absent from publicly available databases. Novel structural determination requires extensive analysis with multiple methods and requires expertise in both mass spectrometry and chemistry. However, normalizing the peak areas of chemical features can provide semi-quantitative estimates of concentrations of unknowns from known species21. If consistent sampling and preparation steps are employed, time trend information for individual species can be generated to monitor the persistence of a chemical into the future as the response for an individual species should be consistent barring large variations in the matrix21.
The primary benefit of this method is the extensibility of the sample treatment to allow both targeted and nontargeted analysis. While targeted analysis provides equivalent or superior quantitative information, it greatly lacks breadth of analysis desired when dealing with new and emerging materials, as well as their relationship to matrix materials. Applying a targeted methodology, or even a suspect screening method based only on known materials and limited databases is completely blind to previously unobserved species, even if they may have significant health effects. As software improves and databases become more robust, the accuracy of unknown identification will continue to increase, with a concomitant decrease in the time investment and level of expertise necessary to analyze the multidimensional data generated by this approach. Nevertheless, data generated presently is of significant future value because data banking allows for post-hoc analysis with newly developed software and enables comparison across time even if the identity of a detected compound is currently unknown.

The class of per- and polyfluoroalkyl substances (PFASs) are persistent organic pollutants with significant public health concern. The specific compounds perfluorooctanoic acid (PFOA) and perfluorooctanesulfonate (PFOS) have drinking water health advisory levels set by the EPA1,2 and their major US production ceased in the 2000s3,4. To gain a significant understanding for the properties of PFAS materials in the textile and consumer product manufacturing spheres, hundreds, if not thousands, of alternate PFAS chemistries have been developed to fill product niches, including replacements for the deprecated compounds5,6,7,8. There is an ongoing need to monitor the environmental levels of straight chain perfluorinated carboxylic acids and sulfonates such PFOS, PFOA, and their related homologous series, but emerging chemical compounds are not covered by established methods such as EPA Method 5379 and frequently lack analytical standards for traditional targeted analysis. The intention of this protocol is thus two-fold. It provides a pathway for the targeted LC-MS/MS analysis of fluorochemical species in water where analytical standards are available and details the seamless integration of a non-targeted, high-resolution mass spectrometry-based approach for data analysis that enables the detection of unknown or unexpected compounds in the same samples.
Solid-phase extraction (SPE) is an established technique for the sample cleanup and concentration with applications to many analytes and sample matrices10,11. For PFAS analysis, multiple solid retentive phases including non-polar, functionalized polar, and ion exchange columns have been used to varying extents for subclasses of fluorinated species in a wide variety of matrices9,12,13,14,15,16. Advances in SPE sample analysis using on-line setups greatly increase the throughput of the approach and improve the reproducibility of sample handling, but the fundamental process remains consistent17. Some efforts to remove the offline concentration of SPE using large volume injections have also been undertaken, but these require modifications to the chromatography that place them outside the realm of casual analysis18,19. Our sample analysis uses a polymeric weak anion exchange (WAX) retentive phase to thoroughly separate acidic PFAS materials from the traditional organic contaminants while achieving substantial sample concentration factors. This WAX phase is important to capture the short chain perfluorinated acids such as perfluorobutane sulfonate (PFBS) or perfluorinated ethers such as hexafluoropropylene oxide dimer acid (HFPO-DA) which are more polar than the longer chain legacy perfluorinated species20,21. As there has been a significant shift towards shorter fluorinated chains and ether inclusion in recent PFAS chemistry5, this phase selection enables more thorough recovery of novel compounds for MS analysis.
Targeted LC-MS/MS quantitation using authenticated standards and stable isotope labeled internal standards provides an unparalleled level of specificity and sensitivity for the quantitative analysis. While this approach is desirable in many situations, it is impractical for all-too-common situations in analysis. Targeted approaches work only for species that are expected in the sample, and for which methods have previously been established. For new and emerging compounds, this approach is incapable of even detecting species that may be of interest, regardless of their chemistry or concentration, and low-resolution mass spectrometers are nearly incapable of providing enough information to make unequivocal chemical assignments of unknown compounds. Consequently, the field of non-targeted analysis has arisen, leveraging the power of high-resolution modern mass spectrometers to analyze samples without a presupposed hypothesis and retroactively assign chemicals to detectable features in the sample. This approach has been used extensively in the fields of biology22,23,24 and environmental science25,26,27 on numerous classes of chemicals. Perfluorinated chemicals are particularly straightforward to identify in this method due to their unique mass spectral patterns, and hundreds of compounds have been described in just the past few years5,28.
The protocol discussed here is intended to align targeted LC-MS/MS PFAS quantitation with the need to identify and semi-quantitatively monitor emerging compounds of interest. The SPE phase selection and sample preparation techniques are intended to ensure capture of more hydrophilic emerging PFAS acids from water and may be less suited for longer chain polymeric species and non-ionic species. Further, the data generated by non-targeted analysis is dense and of high dimensionality, which necessitates the use of data analysis software. Such software packages are frequently vendor specific and require modification to operate between instrument platforms. Where possible, the analysis process has been described in a generic fashion and open source/freeware alternatives are referenced, but the efficiency and accuracy of any software approach must be assessed on an individual basis.

<table border="0" cellpadding="0" cellspacing="0" style="width:1567px;" width="1568">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:391px;">Acqity ultra-high performance liquid chromatography system&#160;</td>
			<td style="width:131px;">Waters Corporation</td>
			<td style="width:119px;"></td>
			<td style="width:927px;">Modified with PFCs analysis kit (176001744); equivalent UPLC system is acceptible if PFAS background is checked and confirmed to be low</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Ammonium acetate</td>
			<td style="width:131px;">Fluka</td>
			<td>17836</td>
			<td>Mass spectrometry grade &#62;99% pure</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Ammonium Hydroxide</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td>338818</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Balance</td>
			<td>Mettler</td>
			<td>AB204S</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:391px;">BEH C18 reverse phase UPLC column, 2.1&#215;50&#160;mm, 1.7&#160;&#956;m&#160;</td>
			<td style="width:131px;">Waters Corporation</td>
			<td>186002350</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Dual piston syringe pump&#160;</td>
			<td>Waters Corporation</td>
			<td>SPC10-C</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Glacial Acetic Acid</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td>ARK2183&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Glass Microfiber Filters</td>
			<td>Whatman</td>
			<td>1820-070</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">High density polyethelye sample bottle&#160;</td>
			<td style="width:131px;">Nalgene</td>
			<td>2189-0032&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">High Resolution Mass Spectrometer</td>
			<td style="width:131px;">Various</td>
			<td style="width:119px;"></td>
			<td>Mass Spectrometer should be capable of providing accurate mass to &#60;10ppm and collecting MS/MS data.&#160; Agilent 6530 qTOF and Thermo Fisher Orbitrap Fusion were used in this work</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Methanol</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:391px;">Nitric Acid (35% w/w)</td>
			<td style="width:131px;">Thermo Fisher Scientific</td>
			<td>SVCN-5-1</td>
			<td>Can be prepared in house using concentrated nitric acid and reagent water</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Polypropylene Buchner funnel</td>
			<td>ACE Glass</td>
			<td>12557-09&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Polypropylene cenitrfuge tube and cap</td>
			<td style="width:131px;">BD Falcon</td>
			<td>352096</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Polypropylene Vacuum Flask (1 L)</td>
			<td style="width:131px;">Nalgene</td>
			<td>DS4101-1000</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:391px;">Quattro Premier XE triple quadrupole mass spectrometer&#160;</td>
			<td style="width:131px;">Waters Corporation</td>
			<td></td>
			<td>Equivalent triple-quadrupole or better system can be used instead, should provide high sensitivity and stability for targeted analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Reagent Water</td>
			<td style="width:131px;"></td>
			<td></td>
			<td>Any source determined to be PFAS free</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Sodium Acetate</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td>W302406</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">TurboVap nitrogen evaporator&#160;</td>
			<td>Caliper Life Sciences</td>
			<td>103198</td>
			<td>Equivalent systems or rotary vacuum evaporator may be used instead</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:391px;">Weak anion exchange SPE cartridge (Oasis WAX Plus)</td>
			<td style="width:131px;">Waters Corporation</td>
			<td>186003519</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Standard Solutions</td>
			<td style="width:131px;"></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:391px;">2,3,3,3-Tetrafluoro-2-(1,1,2,2,3,3,3-heptafluoropropoxy)propanoic acid (HFPO-DA)</td>
			<td style="width:131px;">Wellington</td>
			<td>HFPO-DA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Additional targeted compound standards of interest</td>
			<td style="width:131px;"></td>
			<td></td>
			<td>to be determined based on preliminary analysis and standard availability</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Mass labeled HFPO-DA</td>
			<td style="width:131px;">Wellington</td>
			<td>M2HFPO-DA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Native PFCA/PFAS Mixture (2 ug/mL)</td>
			<td style="width:131px;">Wellington</td>
			<td>PFAC-MXA</td>
			<td>or PFAC-MXB; or individually prepared mixture containing compounds of interest</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Stable Isotope Labeled PFCA/PFAS Mixture (2 ug/mL)</td>
			<td style="width:131px;">Wellington</td>
			<td>MPFAC-MXA</td>
			<td>or MPFAC-MXB; or individually prepared mixture containing compounds of interest as appropriate for Native PFASs</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Software</td>
			<td style="width:131px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Mass Profiler Professional</td>
			<td style="width:131px;">Agilent</td>
			<td style="width:119px;"></td>
			<td>Or open source software packages</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:391px;">Profinder</td>
			<td style="width:131px;">Agilent</td>
			<td style="width:119px;"></td>
			<td>Or open source software packages</td>
		</tr>
	</tbody>
</table>

identifying per- and polyfluorinated chemical species with a combined targeted and non-targeted-screening high-resolution mass spectrometry workflow

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.

Watch this Scientific Journal Video about Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow at JoVE.c

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Here, we present a protocol for the sequential targeted quantification and non-targeted analysis of fluorinated compounds in water by mass spectrometry. This methodology provides quantitative levels of known fluorochemical compounds and identifies unknown chemicals in related samples with semi-quantitative estimates of their abundance.

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the ...

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Research

JoVE Journal

Environment

12.2K Views.  U.S. Environmental Protection Agency. Here, we present a protocol for the sequential targeted quantification and non-targeted analysis of fluorinated compounds in water by mass spectrometry. This methodology provides quantitative levels of known fluorochemical compounds and identifies unknown chemicals in related samples with semi-quantitative estimates of their abundance.

Video: Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Physical, Chemical and Biological Characterization of Six Biochars Produced for the Remediation of Contaminated Sites

The physical and chemical properties of biochar vary based on feedstock sources and production conditions, making it possible to engineer biochars with specific functions (e.g. carbon sequestration, soil quality improvements, or contaminant sorption). In 2013, the International Biochar Initiative (IBI) made publically available their Standardized Product Definition and Product Testing Guidelines (Version 1.1) which set standards for physical and chemical characteristics for biochar. Six biochars made from three different feedstocks and at two temperatures were analyzed for characteristics related to their use as a soil amendment. The protocol describes analyses of the feedstocks and biochars and includes: cation exchange capacity (CEC), specific surface area (SSA), organic carbon (OC) and moisture percentage, pH, particle size distribution, and proximate and ultimate analysis. Also described in the protocol are the analyses of the feedstocks and biochars for contaminants including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), metals and mercury as well as nutrients (phosphorous, nitrite and nitrate and ammonium as nitrogen). The protocol also includes the biological testing procedures, earthworm avoidance and germination assays. Based on the quality assurance / quality control (QA/QC) results of blanks, duplicates, standards and reference materials, all methods were determined adequate for use with biochar and feedstock materials. All biochars and feedstocks were well within the criterion set by the IBI and there were little differences among biochars, except in the case of the biochar produced from construction waste materials. This biochar (referred to as Old biochar) was determined to have elevated levels of arsenic, chromium, copper, and lead, and failed the earthworm avoidance and germination assays. Based on these results, Old biochar would not be appropriate for use as a soil amendment for carbon sequestration, substrate quality improvements or remediation.

Biochar is a carbon-rich material used as a soil amendment with the ability to sustainably sequester carbon, improve substrate quality and sorb contaminants. This protocol describes the 17 analytical methods used for the characterization of biochar, which is required prior to large scale implementation of these amendments in the environment.

The physical and chemical properties of biochar vary based on feedstock sources and production conditions, making it possible to engineer biochars with ...

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and metabolism. Although powerful, metagenomics alone can only elucidate functional potential. On the other hand, metaproteomics enables the description of the expressed in situ metabolism and function of a community. Here we describe a protocol for cell lysis, protein and DNA isolation, as well as peptide digestion and extraction from marine microbial cells collected on a cartridge filter unit (such as the Sterivex filter unit) and preserved in an RNA stabilization solution (like RNAlater). In mass spectrometry-based proteomics studies, the identification of peptides and proteins is performed by comparing peptide tandem mass spectra to a database of translated nucleotide sequences. Including the metagenome of a sample in the search database increases the number of peptides and proteins that can be identified from the mass spectra. Hence, in this protocol DNA is isolated from the same filter, which can be used subsequently for metagenomic analysis.

This protocol is for the extraction and concentration of protein and DNA from microbial biomass collected from seawater, followed by the generation of tryptic peptides suitable for tandem mass spectrometry-based proteomic analysis.

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and ...

Spotting Cheetahs: Identifying Individuals by Their Footprints

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges widely over sub-Saharan Africa and in parts of the Middle East. Cheetah conservationists face two major challenges, conflict with landowners over the killing of domestic livestock, and concern over range contraction. Understanding of the latter remains particularly poor2. Namibia is believed to support the largest number of cheetahs of any range country, around 30%, but estimates range from 2,9053 to 13,5204. The disparity is likely a result of the different techniques used in monitoring.
Current techniques, including invasive tagging with VHF or satellite/GPS collars, can be costly and unreliable. The footprint identification technique5 is a new tool accessible to both field scientists and also citizens with smartphones, who could potentially augment data collection. The footprint identification technique analyzes digital images of footprints captured according to a standardized protocol. Images are optimized and measured in data visualization software. Measurements of distances, angles, and areas of the footprint images are analyzed using a robust cross-validated pairwise discriminant analysis based on a customized model. The final output is in the form of a Ward's cluster dendrogram. A user-friendly graphic user interface (GUI) allows the user immediate access and clear interpretation of classification results.
The footprint identification technique algorithms are species specific because each species has a unique anatomy. The technique runs in a data visualization software, using its own scripting language (jsl) that can be customized for the footprint anatomy of any species. An initial classification algorithm is built from a training database of footprints from that species, collected from individuals of known identity. An algorithm derived from a cheetah of known identity is then able to classify free-ranging cheetahs of unknown identity. The footprint identification technique predicts individual cheetah identity with an accuracy of &gt;90%.

The cheetah (Acinonyx jubatus) is an iconic, endangered species, but conservation efforts are challenged by habitat shrinkage and conflict with commercial farmers. The footprint identification technique, a robust, accurate and cost-effective image classification system, is a new approach to monitoring cheetahs.

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges ...

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence the oxidizing capacity of the atmosphere through interactions with ozone and hydroxyl radicals. The main sink of NOx is the formation and deposition of nitric acid, a component of acid rain and a bioavailable nutrient. NOx is emitted from a mixture of natural and anthropogenic sources, which vary in space and time. The collocation of multiple sources and the short lifetime of NOx make it challenging to quantitatively constrain the influence of different emission sources and their impacts on the environment. Nitrogen isotopes of NOx have been suggested to vary amongst different sources, representing a potentially powerful tool to understand the sources and transport of NOx. However, previous methods of collecting atmospheric NOx integrate over long (week to month) time spans and are not validated for the efficient collection of NOx in relevant, diverse field conditions. We report on a new, highly efficient field-based system that collects atmospheric NOx for isotope analysis at a time resolution between 30 min and 2 hr. This method collects gaseous NOx in solution as nitrate with 100% efficiency under a variety of conditions. Protocols are presented for collecting air in urban settings under both stationary and mobile conditions. We detail the advantages and limitations of the method and demonstrate its application in the field. Data from several deployments are shown to 1) evaluate field-based collection efficiency by comparisons with in situ NOx concentration measurements, 2) test the stability of stored solutions before processing, 3) quantify in situ reproducibility in a variety of urban settings, and 4) demonstrate the range of N isotopes of NOx detected in ambient urban air and on heavily traveled roadways.

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Previous work suggests that the nitrogen isotopic composition of atmospheric nitrogen oxides might distinguish the influence of different sources in the environment. We report on an automated, mobile, field-based method for the high collection efficiency of atmospheric NOx for N isotopic analysis at an hourly time resolution.

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence ...

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

A rheometer system to measure the rheology of crude oil in equilibrium with carbon dioxide (CO2) at high temperatures and pressures is described. The system comprises a high-pressure rheometer which is connected to a circulation loop. The rheometer has a rotational flow-through measurement cell with two alternative geometries: coaxial cylinder and double gap. The circulation loop contains a mixer, to bring the crude oil sample into equilibrium with CO2, and a gear pump that transports the mixture from the mixer to the rheometer and recycles it back to the mixer. The CO2 and crude oil are brought to equilibrium by stirring and circulation and the rheology of the saturated mixture is measured by the rheometer. The system is used to measure the rheological properties of Zuata crude oil (and its toluene dilution) in equilibrium with CO2 at elevated pressures up to 220 bar and a temperature of 50 &#176;C. The results show that CO2 addition changes the oil rheology significantly, initially reducing the viscosity as the CO2 pressure is increased and then increasing the viscosity above a threshold pressure. The non-Newtonian response of the crude is also seen to change with the addition of CO2.

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

A method for measuring the rheology of crude oil in equilibrium with carbon dioxide at reservoir conditions is presented.

A rheometer system to measure the rheology of crude oil in equilibrium with carbon dioxide (CO2) at high temperatures and pressures is described. The ...

The Barnacle Balanus improvisus as a Marine Model - Culturing and Gene Expression

Barnacles are marine crustaceans with a sessile adult and free-swimming, planktonic larvae. The barnacle Balanus (Amphibalanus) improvisus is particularly relevant as a model for the studies of osmoregulatory mechanisms because of its extreme tolerance to low salinity. It is also widely used as a model of settling biology, in particular in relation to antifouling research. However, natural seasonal spawning yields an unpredictable supply of cyprid larvae for studies. A protocol for the all-year-round culturing of B. improvisus has been developed and a detailed description of all steps in the production line is outlined (i.e., the establishment of adult cultures on panels, the collection and rearing of barnacle larvae, and the administration of feed for adults and larvae). The description also provides guidance on troubleshooting and discusses critical parameters (e.g., the removal of contamination, the production of high-quality feed, the manpower needed, and the importance of high-quality seawater). Each batch from the culturing system maximally yields roughly 12,000 nauplii and can deliver four batches in a week, so up to almost 50,000 larvae per week can be produced. The method used to culture B. improvisus is, probably, to a large extent also applicable to other marine invertebrates with free-swimminglarvae. Protocols are presented for the dissection of various tissues from adults as well as the production of high-quality RNA for studies on gene expression. It is also described how cultured adults and reared cyprids can be utilized in a wide array of experimental designs for examining gene expression in relation to external factors. The use of cultured barnacles in gene expression is illustrated with studies of possible osmoregulatory roles of Na+/K+ ATPase and aquaporins.

The Barnacle Balanus improvisus as a Marine Model - Culturing and Gene Expression

The barnacle Balanus (Amphibalanus) improvisus is a model for studying osmoregulation and antifouling. However, natural seasonal spawning yields an unpredictable supply of cyprid larvae. Here, a protocol for the all-year-round culturing of B. improvisus is described, including the production of larvae. The use of cultured barnacles in gene expression studies is illustrated.

Barnacles are marine crustaceans with a sessile adult and free-swimming, planktonic larvae. The barnacle Balanus (Amphibalanus) improvisus is particularly ...

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 ...

Removal of Arsenic Using a Cationic Polymer Gel Impregnated with Iron Hydroxide

In this work, we prepared an adsorbent composed of a cationic polymer gel containing iron hydroxide in its structure designed to adsorb arsenic from groundwater. The gel we selected was the N,N-dimethylamino propylacrylamide methyl chloride quaternary (DMAPAAQ) gel. The objective of our preparation method was to ensure the maximum content of iron hydroxide in the structure of the gel. This design approach enabled simultaneous adsorption by both the polymer structure of the gel and the iron hydroxide component, thus, enhancing the adsorption capacity of the material. To examine the performance of the gel, we measured reaction kinetics, carried out pH sensitivity and selectivity analyses, monitored arsenic adsorption performance, and conducted regeneration experiments. We determined that the gel undergoes a chemisorption process and reaches equilibrium at 10 h. Moreover, the gel adsorbed arsenic effectively at neutral pH levels and selectively in complex ion environments, achieving a maximum adsorption volume of 1.63 mM/g. The gel could be regenerated with 87.6% efficiency and NaCl could be used for desorption instead of harmful NaOH. Taken together, the presented gel-based design method is an effective approach for constructing high-performance arsenic adsorbents.

In this work, we prepared an adsorbent composed of the cationic N,N-dimethylamino propylacrylamide methyl chloride quaternary (DMAPAAQ) polymer gel and iron hydroxide for adsorbing arsenic from groundwater. The gel was prepared via a novel method designed to ensure the maximum content of iron particles in its structure.

In this work, we prepared an adsorbent composed of a cationic polymer gel containing iron hydroxide in its structure designed to adsorb arsenic from ...

Sampling for Estimating Frankliniella Species Flower Thrips and Orius Species Predators in Field Experiments

The western flower thrips, Frankliniella occidentalis (Pergande), is a polyphagous pest that has been spread worldwide. The extensive use of insecticides in attempts to control its populations eliminates natural enemies and competitor flower thrips species, thereby increasing its populations. An unsustainable situation develops with concomitant resistant pest populations, secondary pest outbreaks, and environmental degradation. Integrated pest management utilizes knowledge of pest and natural enemy relationships to implement tactics that are environmentally friendly and sustainable. Minute pirate bugs are the most important worldwide predators of thrips. They can suppress and ultimately control Frankliniella species flower thrips. Flower samples taken at least weekly are needed to understand predator-prey dynamics. Shown here is the sampling of the flowers of fruiting vegetables and companion plants to estimate the densities of individual thrips and minute pirate bug species. Representative data illustrates how the protocol is used to determine the efficacy of management tactics over time and how to evaluate the benefits of predation by minute pirate bugs. The sampling protocol is similarly adaptable to sampling thrips and minute pirate bugs in other plant species hosts.

Sampling for Estimating Frankliniella Species Flower Thrips and Orius Species Predators in Field Experiments

Presented here is a protocol to determine the number of thrips and minute pirate bug predators in crops over multiple dates in field experiments. Also illustrated is how to determine the efficacy of management tactics against thrips and evaluate the benefits of predation by minute pirate bugs.

The western flower thrips, Frankliniella occidentalis (Pergande), is a polyphagous pest that has been spread worldwide. The extensive use of insecticides ...

Investigation of Xenobiotics Metabolism In Salix alba Leaves via Mass Spectrometry Imaging

The method presented uses mass spectrometry imaging (MSI) to establish the metabolic profile of S. alba leaves when exposed to xenobiotics. Using a non-targeted approach, plant metabolites and xenobiotics of interest are identified and localized in plant tissues to uncover&#160;specific distribution patterns. Then, in silico prediction of potential metabolites (i.e., catabolites and conjugates) from the identified xenobiotics is performed. When a xenobiotic metabolite is located in the tissue, the type of enzyme involved in its alteration by the plant is recorded. These results were used to describe different types of biological reactions occurring in S. alba leaves in response to xenobiotic accumulation in the leaves. The metabolites were predicted in two generations, allowing the documentation of successive biological reactions to transform xenobiotics in the leaf tissues.

Investigation of Xenobiotics Metabolism In Salix alba Leaves via Mass Spectrometry Imaging

This method uses mass spectrometry imaging (MSI) to understand metabolic processes in S. alba leaves when exposed to xenobiotics. The method allows the spatial localization of compounds of interest and their predicted metabolites within specific, intact tissues.

The method presented uses mass spectrometry imaging (MSI) to establish the metabolic profile of S. alba leaves when exposed to xenobiotics. Using a ...