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L. Scott Ramos will receive the 2021 Eastern Analytical Symposium (EAS) Award for Outstanding Achievements in Chemometrics at the EAS Symposium taking place November 15–17, 2021, in Plainsboro, New Jersey. The annual EAS awards honor analytical chemists who have distinguished career achievements and who have advanced their fields by superior work in developing theory, techniques, or instrumentation.

Ramos has worked for 37 years at Infometrix,Inc., in Seattle, Washington, where he is the chief technical officer. He received his BS in Chemistry from the Massachusetts Institute of Technology, his MS in Environmental Science from Washington State University, and his PhD in Chemistry from the University of Washington, where he worked with. Prof. Bruck Kowalski. Before joining Infometrix, he was a senior scientist at the Amazon National Research Institute.

While at Infometrix, Ramos has lectured on and led development of methodologies using chemometrics for several companies and organizations in the United States, the United Kingdom, Japan, and Brazil. He has been an invited speaker at the annual meetings of Pittcon, FACCS/SciX, EAS, the Association of Official Agricultural Chemists, and the Gordon Conferences, as well as at the Sociedade Brasileira de Química, Congresso Brasileira de Química, the Congresso da Alago Sobre Geoquímica Orgânica meetings in Brazil, the Congreso Mexicano del Petróleo in Mexico, and the IFPAC/PAT Summit in Puerto Rico. He also served as visiting professor at the Universidade Estadual de Campinas and the Universidade Federal do Pará.

Ramos has been involved with studies of both anthropogenic and natural trace constituents in the environment. His current interests include full automation of multivariate calibration of complex chemical systems, optimization of varietal blending for natural product simulation, and online quality control of multi-stream production of cannabinoid materials.

Articles

L. Scott Ramos will receive the 2021 Eastern Analytical Symposium (EAS) Award for Outstanding Achievements in Chemometrics at the EAS Symposium taking place November 15–17, 2021, in Plainsboro, New Jersey. 

L. Scott Ramos Wins 2021 EAS Award for Outstanding Achievements in Chemometrics

Tony Taylor is Group Technical Director of Crawford Scientific Group and CHROMacademy. His background is in pharmaceutical R&D and polymer chemistry, but he has spent the past 20 years in training and consulting, working with Crawford Scientific Group clients to ensure they attain the very best analytical science possible. He has trained and consulted with thousands of analytical chemists globally and is passionate about professional development in separation science, developing CHROMacademy as a means to provide high-quality online education to analytical chemists. His current research interests include HPLC column selectivity codification, advanced automated sample preparation, and LCâMS and GCâMS for materials characterization, especially in the field of extractables and leachables analysis.

No matter which ”version” of liquid–liquid extraction (LLE) method you wish to use, there are some fundamentals that need to be clearly understood, and perhaps principle among these is analyte physicochemical information.

To achieve the best selectivity and recovery, one needs to ensure that partitioning of target analytes from one phase into the other is as efficient as possible, and this is underpinned in most cases by some simple physicochemical information. The following information is recommended:

This information is available from a variety of sources, including:

Chemspider (Royal Society of Chemistry, UK) (http://www.chemspider.com)

Chemicalize (ChemAxon) (https://chemicalize.com)

Marvin Sketch (ChemAxon) (which also calculates values for analytes not in the previous databases) (https://chemaxon.com/products/marvin).

So how can we use this information to design and optimize our LLE experiment? The first decision in designing any liquid extraction is the choice of extracting solvent, and this is driven by the relative hydrophobicity of the analyte molecule, which is reflected by its 

 value indicates the partitioning value between aqueous and organic phases. The more highly positive the Log

 value, the greater will be the extent of partitioning into the organic (extracting) phase. Analytes with increasingly negative Log

 values will show much lower partitioning into the organic phase.

For ionogenic analytes, the optimum partitioning behavior from aqueous to organic will be achieved when the analyte in is the neutral form. For acids, the aqueous sample should be adjusted to pH units below the analyte p

Of course, the partitioning behavior of our target analytes is strongly influenced by the choice of organic extraction solvent, as the various solvents will also have a range of polarities that will influence their affinity for the analytes.

For more polar analytes—and here lower values of Log

) can also act as a good indicator of polarity—one may need to select an organic solvent that has a higher polarity index value, in order to optimize recovery of the analyte from the aqueous sample. The rules are fairly simple—try to match the polarity of your analyte with the polarity of the extraction solvent. If you cannot obtain a good polarity measure of the analyte, Log

) can be used as a surrogate, with lower Log

 values indicating the need for a more polar extraction solvent, and vice versa.

Adding high-enough concentrations of simple salts to saturate the aqueous sample solution can increase the partition coefficient of hydrophilic analytes, and improve recovery into organic extraction solvents. The analyte solubility in the aqueous sample can be reduced by adding, for example, 3–5 M sodium sulphate to the sample solution, driving the target analytes into the organic phase, and improving recovery.

Of course, it is also possible to use”mixed” organic systems as the extraction solvent to further fine-tune the selectivity or the recovery of the target analytes from the sample. Here, one needs to develop an in-house screening approach, using solvent mixtures that have proven to be successful in your application area.

When considering solvent selection, one must always be mindful of the specificity of the extraction. Some organic solvents are able to solubilize a reasonable amount of water, and therefore can be expected to be less specific than those solvents in which water has much lower solubility.

The selectivity of the extraction experiment involving pH manipulation can be further improved using a technique known as back extraction. Once the target analytes are initially extracted into the organic phase, they can be re-extracted into a fresh aqueous phase where the pH has been manipulated to ensure the analytes are in the charged form, and therefore most highly hydrophilic. In this way, specificity of the extraction can be improved, as all neutral extractants will be left behind in the organic solvent, reducing the instances of co-elution, and achieving lower matrix background (and therefore lower detection limits). To ensure the highest recovery possible, the ratio of organic extraction solvent to aqueous sample should be high (or perhaps higher than is generally accepted as reasonable), and a ratio of 7:1 is considered somewhat of a generic optimum value. Obviously, the actual optimum value will depend upon the partition coefficient of the target analytes between the two solvents, and extraction solvents with high partition coefficients may require lower ratios, and vice versa.

The extraction time and method (vigor) of shaking also need to be optimized; how- ever, these are both reasonably simple to achieve using empirical methods.

 is the Chief Science Officer of ArchSciences Group and the Technical Director of CHROMacademy. Direct correspondence to: 

To achieve the best selectivity and recovery in liquid–liquid extraction (LLE), a few fundamentals need to be clearly understood.

Strategies for Optimization of Liquid–Liquid Extraction (LLE) Protocols

Microplastics are now recognized as pervasive in the environment, as well as in stormwater, wastewater, ocean water, coastal sediments, drinking water, and even animal tissue. The mismanagement of plastic waste throughout the world is a matter of urgent concern that has led to product bans for small plastics, such as microbeads used in cosmetic products, to larger plastic item, such as bags and straws, that can degrade into microplastics, This concern has also resulted in new and planned requirements to monitor microplastics in the environment. The implementation of monitoring programs requires reliable standardized methods and best practice guidelines, yet environmental measurement and monitoring practitioners still do not have established practices and methods for collecting and analyzing plastic pollution. This deficiency has resulted in significant challenges in the interpretation of research findings for use in corporate or government policy. A major issue with measurement based upon sampling and analysis of microplastics is their unique behavior; environmental microplastics occur as non-homogeneously distributed particulates and fibers composed of various polymeric materials. This article provides an overview of the environmental and analytical challenges caused by microplastics; it proposes an analytical measurement approach that enables accurate qualitative and quantitative analysis of microplastics, and demonstrates some of the advantages of pyrolysis–gas chromatography–mass spectrometry (Py–GC–MS) for the analysis of environmental samples as compared to the other leading analytical methods.

Plastic waste has proven very difficult to measure. In 2019, 368 million tons of plastic were produced (1), an increase of more than 600% over the last four decades (2) (Figure 1). Global collection rates of plastic waste over the same timeframe remained stagnant, not surpassing 10% of production worldwide (3). Collection and sorting of plastic waste into recovered material is not a guarantee that it will be recycled. This low recycling rate has been exacerbated by implementation of China’s National Sword Policy on January 1, 2018, with its rejection of imported recovered plastic waste from Western countries.

FIGURE 1: Yearly global plastic production.

Examination of the sources of these problems can also be misleading. For example, a leading economic model uses surrogate factors for plastic waste mismanagement and depicts non-Western countries as being primarily responsible for the inputs of plastic pollution into the environment (4). These models miss the point that countries like the United States have high per capita plastic consumption with sporadic and inconsistent waste management infrastructure, often exporting large portions of recovered plastic waste to non-Western countries (5). Final disposition of plastic waste exports from Western countries is now a bigger unknown than it was prior to 2018, with likely more plastic ending up in landfills, burned, or accumulating in outdoor heaps and piles and migrating to watersheds, creeks, streams, rivers, and the ocean during wind and rain events. The problem of plastic pollution does not belong to “other” countries, but is very much one of every nation. As plastic debris enters roadways and watersheds, it threatens our natural environment (Figure 2), pollutes and clogs stormwater conveyances, and can pose significant sanitation and public health issues, such as an increase in standing water and mosquito vector growth. As debris enters streams and rivers, micro- and nanoparticles are generated at rates dependent on the forces acting upon them and their polymer type and plastic form. We anticipate that microplastic monitoring will become more prevalent as a means for measuring plastic pollution in the environment.

FIGURE 2: Plastics on Midway Island and stomach contents of an albatross in the NW Hawaiian Islands.

Plastic debris in the environment is not inert. Plastic debris ultimately fragments into smaller particles over time as a result of the effects of weathering, including UV radiation and wind and wave action. As debris breaks down into smaller and smaller particles, polymer surface areas increase relative to their volume, and these surfaces can host pathogens such as bacteria and viruses. These smaller plastic particles, with their relatively higher surface area, have a greater propensity to sorb persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs) and pesticides from surrounding air, dust, and water.

Additionally, the manufacturing process of plastic products typically adds chemical constituents, or plasticizers, to augment polymer characteristics and performance, and some of these additives are toxins or carcinogens. It is currently unknown to what extent plasticizers from within microplastics and any sorbed contaminants on the surface of particles are able to desorb, and, upon ingestion by an organism, be taken up by the organism’s tissue and organs (Figure 3). These contaminated microplastics form dust pools both indoors and outdoors. Plastic particles have been documented in every environmental media, and reports in 2019 and 2020 present evidence that plastic is even raining down on us. This pervasiveness and persistence of microplastics and nanoplastics cause some to speculate that scientists of the future will use these plastics as a tool for dating abandoned habitations and geologic strata.

FIGURE 3: Plastic (PE) extracted from a 9 cm lanternfish (myctophid) in the Northern Pacific Ocean.

The behavior of microplastics in environmental media is expected to be somewhat more predictive than that of larger debris, which is subject to chaotic ebbs and flows of wind and water. Although plastic fragments may initially float on the water’s surface, they soon become suspended in the water column as they become covered in biological growth; biofouling increases the specific gravity, which decreases buoyancy. It has been assumed that, ultimately, most waterborne microplastics are destined for the floor of an ocean, lake, or river, perhaps being ingested along the way or resuspended through various mechanisms on their way to, and once on, the bottom. However, colloidal suspension and electrostatic interaction between the smallest of these plastic particles and the resulting effect on transport and occurrence in the environment is an area of study that has yet to be explored.

Until recently, calculations of density and distribution of plastic in the environment focused almost exclusively on the visible size range above 300 μm, and employed visual techniques as a means of identification. In addition, these samples were collected on the ocean surface. The majority of microplastics in the environment, both water and air, are now believed to be smaller than 300 μm.

You Can’t Manage What You Can’t Measure

Rigorous and reproducible identification of plastic particles is critical to pollution monitoring. Considering the mantra “you can’t manage what you can’t measure,” effective upstream policy, including corporate and government action to reduce the occurrence of plastic particles, must rely on robust monitoring regimens. Efficacy of mitigation actions will not be measurable without such monitoring, and measurement standards therefore must be agreed on and widely adopted.

The risk of microplastics to our natural environment, and to public health, can be described as the likelihood of harm based on both toxicity and exposure, yet this understanding of risk has been stymied by the lack of rigor, standardization, and reproducibility of measurement practices and methods. While spectroscopy techniques measure plastic particle count, the properties of smaller plastic particles create a sort of fingerprint more easily traced with mass-based concentrations. Mass-based concentrations are much more conducive to benchmark comparisons such as ambient conditions or regulatory thresholds. Measuring concentrations of these tiny plastic particles and their effects on humans and animals necessitates the global standardization of collection, preparation, and identification procedures and methods.

Standardized methodologies for non-destructive collection, handling, separation, sample preparation, and positive identification of microplastics are in the early stages of development. ASTM International is leading the way with recently published standards for collection (D8332) and preparation (D8333) of microplastic particles. Some of the leading technologies currently available that yield scientifically defensible and reproducible results for positive microplastic identification include focal plane array detection, thermo-gravimetric analysis-pyrolysis-gas chromatography-mass spectrometry (Py–GC–MS), and infrared (IR) and Raman spectroscopy.

Presently, most researchers attempting to positively identify polymers are employing IR or Raman spectroscopy for analysis of particle type and count. Information on particle polymer type, count (per unit volume) and size are germane to specific research contexts, and some advanced spectroscopy technologies have reduced operational procedures from upwards of a week to only hours; in most instances, spectroscopy remains time consuming and costly, and requires highly trained and skilled technicians with sophisticated laboratory equipment. Although a high degree of skill is also needed for Py–GC–MS, this technique offers a solution to speed up sample preparation and analysis time, while yielding perhaps more practical information on polymer types and mass per volume.

For water quality monitoring activities such as discharge management, plastic particle identification by mass per unit volume may be more useful for both point source and non-point source measurement. We believe that the use of Py–GC–MS for polymer identification has broader applicability than IR or Raman spectroscopy, because it is less constrained by particle size (large or small), requires less preparation, has fewer interferences, has less potential for analytical error, enables straightforward calibration, provides consistent and highly reproducible results, much lower equipment entry costs, and readily available polymer identification libraries. It is also conducive to varied matrices such as water, soil, and biota, enabling more easily accomplished comparisons.

Here, we outline the development of the Py–GC–MS method, and explain its practical application to understanding the occurrence and prevalence of microplastics, and potentially even nanoplastics, in the environment.

Recent studies have discovered the unique capability of the microfurnace pyrolysis–GC–MS technique in the identification of polymer types and quantities in microplastic particles. This article describes the study of microplastics in water samples with high and low suspended solids using a microfurnace pyrolyzer directly interfaced with a benchtop gas chromatography–mass spectrometry (GC–MS) instrument.

The samples were collected from drinking water, wastewater influent and effluent, surface water, and marine water. The developed method using the microfurnace Py–GC–MS confirms that the analysis of microplastics in such water samples is independent of polymer type, and not limited to any particular water matrices.

The method proves higher analytical sensitivity for both qualitative and quantitative analyses, including better detection limits. The inorganic contaminants in the sample matrices do not impede the identification and specification of the polymers and associated additives. Detailed information about the chemical nature of the polymer and contained organic additives are also obtained simultaneously.

Most of the initial research on analyzing microplastics was developed using spectroscopy techniques such as Fourier transform infrared (FT-IR) and Raman spectroscopy. These traditional techniques are nondestructive, and commonly used in analytical laboratories. They provide information for functional group identification and library comparison. Detailed information about the contained organic additives is usually not obtained because contaminants (organic or inorganic) and additives can overlap polymer bands. In other words, FT-IR can suffer from peak masking if two or more compounds in a mixture with the same dipole moments absorb IR rays at the same or nearby wavelength. In this case, identifying the overlaid peaks will be extremely difficult. With Raman spectroscopy, measurement of samples that contain fluorescent dyes is very challenging.

The need for sample preparation, such as matrix isolation, solvent extraction, or pre- treatment, is another challenge with these traditional techniques. Typically, it could take several hours to perform the analysis, and quantitation is challenging. The limit on sample particle size with these techniques also must be considered. Micro-Raman spectroscopy and an optical microscope are used for particle size analysis that is larger than 1 μm, whereas FT-IR can be used for particle size larger than 10 μm.

In contrast, microfurnace Py–GC–MS can be used for both qualitative and quantitative analysis of low microplastics concentrations, because of its high sensitivity capability. Although there is no practical lower limit in particle size, total polymer mass in a sample constrains the lower limit of the measurement (see Figure 4). In addition to microplastics, GC–MS adds the benefit of detecting and quantifying both additives and contaminants in the sample using interpretation libraries. Sample preparation is easy and straightforward; micrograms of a sample (including solids taken “as is”) are placed into an inert pyrolysis cup, and the analysis can be performed. The recent developments in F-Search software for microplastics data processing, which is an automated analysis (quantitative and qualitative), has also significantly shortened the time needed for the analysis.

FIGURE 4: Calibration curve for polystyrene.

The microfurnace utilizes a low thermal mass ceramic heater to quickly heat and cool with thermal uniformity. This technology can be used for multiple analytical techniques in addition to flash pyrolysis, such as evolved gas analysis (EGA), thermal desorption (TD), double-shot (TD followed by flash pyrolysis), and heart-cutting. All the surfaces are quartz or temperature stable deactivated, and there is no cross-contamination.

The microfurnace performs continuous pyrolysis in a single step by directly depositing all the pyrolyzates on column as they are generated. The direct connection of the microfurnace to the GC injection port allows this single-step pyrolysis where reproducibility and temperature accuracy are obtained. The sample is held near ambient temperature and in an inert atmosphere prior to pyrolysis.

Therefore, there is no chance for evaporation or degradation of the sample before the analysis, maintaining sample integrity, and contributing to accuracy, precision, and reproducibility of the data set. The microfurnace is preheated to the desired temperature, and precisely measured with a thermal couple sensor. The sample cup then freefalls into the quartz pyrolysis tube, where the sample is rapidly and reproducibly pyrolyzed. The pyrolyzates are directly swept onto the GC column for separation and detection by MS. A critical factor with the microfurnace is its ability to control the actual sample temperature to within ±0.1 °C set point of the pyrolysis temperature. This feature guarantees reproducibility and precision, as well as true flash pyrolysis, where the set temperature in the furnace by the analyst reflects the pyrolysis temperature on the sample.

There are other pyrolysis technologies: filament, which uses platinum coil wire, and Curie-point, which utilizes a ferromagnetic foil. These technologies can suffer from poor temperature accuracy and poor reproducibility because of non-uniform heating. In both systems, to avoid condensation of the pyrolyzates, the sample must be inserted into a heated manifold before pyrolysis. This preheating step can cause degradation or thermosetting of the samples prior to analysis. With filament pyrolyzers, sample placement and heat-transfer variations also affect the temperature of the sample, depending on where the sample sits in relation to platinum wire coils and on sample contact. The filament temperature is estimated from the electrical properties of the wire, but the temperature of the sample is not actually measured as it changes over time. In other words, the platinum wire can only control the temperature of its heating element, not the actual sample pyrolysis temperature, requiring the user to program the system at a much higher temperature than the desired true pyrolysis temperature on the sample.

In a study of microplastics in surface water using a filament pyrolyzer, the pyrolysis temperature was set at 750 °C, with the sample placed in a narrow quartz tube inside the platinum wire (6). Based on actual bond energy strengths, polymers and other organic material will typically pyrolyze prior to a temperature of approximately 700 °C.

Therefore, the only reason to set an initial pyrolysis temperature in excess of 700 °C is that the actual sample temperature in the pyrolyzer is not accurate. Literature indicating pyrolysis at 1200 or 1300 °C is either unnecessary, over pyrolysis, or shows poor actual sample temperature control in the particular pyrolysis device.

It is critical to note that some filament pyrolyzers perform 2-step pyrolysis. First, some of the pyrolyzates are collected on a trap, and then a switching valve sends the pyrolyzates from the trap through a long transfer line into the GC column. In this configuration, the pyrolyzates are indirectly placed on column. During this two-step process, polar and heavy compounds may cause cross-contamination, or become irreversibly stuck in the trap or the long transfer line, and are not transferred for analysis because of the potential of cold spots in the system.

Figure 5 shows the system configuration of the microfurnace pyrolysis-GC–MS used for microplastic analysis in the water samples. Experiments are performed with a microfurnace pyrolyzer (EGA/PY-3030D, Frontier Laboratories Ltd.) with Auto-Shot Sampler (AS-1020E, Frontier Laboratories Ltd.) coupled to a GC–MS system (5977B MDS, Agilent Technologies, or GCMS QP-2020NX, Shimadzu Corporation). Chromatographic separation is achieved using a metal capillary column (UA-5, 0.5 μm film of 5% diphenyl-95% dimethylpolysiloxane, 30 m x 0.25 mm, Frontier Laboratories Ltd.) with helium (He) as the carrier gas (1 mL/min). Reduced analysis time can be obtained by utilizing a backflush system, especially when dealing with high- boiling point matrix interference or high molecular weight pyrolysis products. This system consists of a precolumn (UA-50, 1 μm film 50% diphenyl 50% dimethylpolysiloxane, 2 m x 0.25 mm, Frontier Laboratories, Ltd.) connecting the injector and the separation column with a three-way split (7).

FIGURE 5: Pyrolysis-GC–MS system configuration using a microfurnace pyrolyzer.

Using the flash pyrolysis mode of operation (the single-shot mode) of the microfurnace, the chemical composition analysis is performed on microplastic samples. The microfurnace is programmed at 600 °C to break down the organic bonds of the samples into smaller and more stable molecules, referred to as pyrolyzates. The pyrolyzates are directly swept from the pyrolysis tube into a GC column, via a needle fixed in a split injection port. The column is interfaced directly to an MS instrument. The chemical species are ionized and sorted, based on their mass-to-charge ratio. Compounds eluted from the GC column are identified by comparing the mass spectra at a predetermined retention time to reference spectra and retention index (RI) to those contained in a database.

Table I shows some of the polymers that can be identified using microfurnace Py–GC–MS. The validation is performed using a pyrolyzate primary quantitative ion and identification ion(s) representative of a specific polymer. It is important to note that the developed method is not limited to the polymers listed in the table below.

Figure 6 demonstrates a typical pyrogram of the 11 target pyrolyzates and their associated polymer (in parenthesis), using the developed method described above.

FIGURE 6: Typical pyrogram of 11 target polymers using microfurnace technique.

The concentration of each identified polymer is determined using either internal or external standard calibration. Using a multi-component standard (8), the calibration curves can be obtained. Table I lists the data precision of each polymer for 

As mentioned earlier, F-Search, an automated search program, has been augmented for the fast identification and quantitation (in the range of seconds) of microplastics. The search algorithm is based on the identification of the selected number of characteristic pyrolyzates of each polymer in the sample. Each selected pyrolyzate is only obtained from the pyrolysis of a polymer, and not from the pyrolysis of all other investigated polymers. For each characteristic pyrolyzate, a set of specific 

 signal intensities is also selected. A normalized summated mass spectra (SMS) is obtained from the selected 

 signal intensities so that the mass spectra are cleared from any interfering signals due to co-eluted species. The similarity of the obtained SMS with the one in the library is calculated and expressed as a percentage. A signal-to-noise ratio (S/N) is calculated for each polymer by extracting a characteristic 

 signal chosen from one of the main pyrolyzates. If the S/N is lower than a set threshold, the presence of the corresponding polymer is excluded, regardless of the similarity index obtained by the algorithm.

Figure 7 demonstrates an example of the identification process of polypropylene (PP) in the reference mixture using the automated F-Search program. The ions at 

 41, 43, 55, and 70 were selected as the representative four pyrolyzates of PP. The peaks of each characteristic pyrolyzate identified in the extracted ion chromatograms (EICs) are marked with dots. The SMS of these peaks was then compared with that obtained for reference PP. Because a similarity of 98.4% was obtained, the presence of PP in the reference mixture was confirmed. The results obtained with the algorithm for the two samples are summarized in Table II (5), along with the calculated S/N values and the limit of detection of the algorithm. Both a high match and S/N greater than the set threshold were obtained for all polymers in the reference mixture.

FIGURE 7: Identification process for PP in reference mixture using F-Search automated algorithm.

The microfurnace Py–GC–MS technique provides detailed information about polymers, additives, and even contaminants. The identification of polymers is rapid and automated, easy to use in different laboratory setups, and less costly compared to traditional spectroscopy techniques. Neither the presence of biofouling nor that of contaminants or additives impedes the identification and quantitation of polymer types.

The sample preparation is easy and straightforward; the sample is placed into an inert sample cup, and held in the ambient atmosphere before the analysis starts. Once the furnace achieves the desired temperature as programmed by the user, the sample cup freefalls into the microfurnace, where rapid and reproducible pyrolysis happens. All the generated pyrolyzates are directly placed on column in a single step, foregoing the need for a transfer line or chemical trap.

The F-Search automated program significantly increases analytical efficiency by providing polymer identification in a matter of seconds. Microplastics are often a mixture of polymers and additives, and in most cases, they are co-eluted, so identification using spectroscopy techniques is challenging. However, the co-elution of compounds and contamination in the microplastic mixture samples does not affect the ability of microfurnace Py–GC–MS to provide simultaneous identification and specification of polymers and additives.

Another advantage of this technique is the elimination of solvent extraction or sample pretreatment before analysis. The microfurnace can be used for thermal desorption analysis where the additives and light compounds are thermally extracted from a mixture. Laboratories save money over time by using fewer solvents, while also protecting analysts and the environment from solvent exposure and hazards.

The microfurnace produces reproducible data, and controls the actual sample temperature to within ±0.1 °C of the pyrolysis temperature. Other types of pyrolyzer devices can only control the temperature of the heating element, and not the actual sample temperature. Also, the ability of the microfurnace to protect the sample from any heat prior to the analysis and the absence of the transfer line makes it possible to analyze a wide range of compounds, including additives, volatiles, light, heavy, polymeric, and polar compounds.

The collection approach and sample number must always be considered in the analysis of microplastics. Often, water samples are filtered to collect the microplastic mixture. With Py–GC–MS, only a small amount of sample (microgram quantities) is needed, and multiple analyses can be performed with one-time sampling. This new Py–GC–MS method is currently under development as an official ASTM method by an ASTM International workgroup.

A concerted effort must be made to standardize and make widely available these types of sampling and analytical methodologies, as well as streamline methodologies and reduce their costs. As the demand for microplastics measurement and monitoring increases, practitioners must have access to reliable and repeatable methods. Ideally, these methods would provide both qualitative and quantitative information, and be comparable to other standardized and commonly used environmental measurement techniques.

The ability to adapt these technologies for field deployment is essential to gain a better understanding of the quantity, distribution, and behavior of plastic particles in all environmental matrices. Common reporting of microplastic concentration data will support both government and industry responses to the growing crisis of plastic pollution. As described, microplastic measurement provides advantages over traditional debris monitoring. Mass-based analysis is directly comparable to other environmental contaminant analysis methods, and has an advantage over spectroscopy techniques because it avoids complicated preparation and the need to count individual particles after identification.

New frontiers in microplastic measurement and monitoring require robust and repeatable analytical techniques. Analytical information should be the cornerstone of corporate and government pollution mitigation efforts, and Py–GC–MS will be critical in the evaluation of efforts to curb plastic pollution around the world.

The authors wish to thank the scientists at Frontier Laboratories whose work made this article possible: T. Ishimura, I. Iwai, M. Matsueda, A. Shiono, K. Matsui, N. Teramane, and C. Watanabe.

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

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 is the President of ATRq, in Orem, Utah. 

 is the Technical Director of Frontier Americas, in Houston, Texas. 

 is the founder and Principal Engineer at Ocean P3 Systems, in San Francisco, California, and a former engineer for the U.S. Environmental Protection Agency (USEPA), Region 9, in San Francisco, California. 

 is an environmental scientist with EPA Region 9 Superfund Division and is based in Los Angeles, California. 

 is Co-founder & CEO of Kamilo, Inc. in Oakland, California, and a former engineer for USEPA Region 9. 

 is the Vice President and Manager of Global Product Marketing at Frontier Laboratories, in Koriyama, Japan. Direct correspondence to: 

Pyrolysis–gas chromatography–mass spectrometry has advantages for the analysis of environmental microplastic samples compared to other leading analytical methods, including spectroscopic techniques.

Identification of Microplastics in Environmental Monitoring Using Pyrolysis–GC–MS Analysis

A fast-screening workflow for residual pesticides present on the surface of fruits was enabled using a gas chromatography (GC) oven with direct heating technology and mass spectrometry (MS) spectral deconvolution. Residual pesticides were rinsed from the tested commodity surface with acetone. The rinsate was collected and injected into the GC–MS system. The direct heating oven allowed for a very high temperature program rate (250 

C/min) to complete the GC–MS analysis in 3.4 min. Spectral deconvolution coupled with the library search algorithm and time-filtering using retention indices resulted in rapid and confident identification of residual pesticides present on the fruit. The NIST 17 spectral library, as well as other commercially available and user created libraries, can be used for compound identification. The entire analysis from sample collection to reporting took under 6 min. This approach is particularly useful for prioritizing samples in high-throughput facilities for more in-depth analysis.

Trace-level pesticides and environmental pollutants in the food supply continue to be a worldwide concern, and drive demand for more rapid and reliable methods of analysis. Part of the challenge is finding technologies that can search for hundreds of pesticides, polyaromatic hydrocarbons (PAHs), and other targets with simple sample preparation and a quick turnaround time.

The targeted analytical methods commonly used in food safety analysis focus on a finite list of compounds. This approach typically utilizes gas and liquid chromatography coupled with triple quadrupole mass spectrometry (GC–MS/MS and LC–MS/MS) (1,2). While these techniques provide outstanding sensitivity, they are limited by their restricted scope of targets, which is problematic if the method includes several hundreds of pesticides. High resolution mass spectrometry (HRMS) techniques coupled with GC and LC would enable comprehensive sample screening (3). However, this approach typically requires high-resolution databases to analyze data against them. Such databases often include only a limited number of compounds, and creating an expert-curated library is a labor-intensive process. In addition, HRMS requires rather sophisticated and expensive instrumentation. An alternative screening approach utilizes GC coupled with a single quadrupole mass spectrometer (GC–MS) operated in full scan mode (4). To increase the confidence of identification with GC–MS, mass spectral deconvolution and time-filtering can be utilized. Time-filtering can be performed based on the retention times (

) of the identified targets by comparing them to the library 

-filtered search is its dependence on the flow path, column flow, and oven ramp rate. The use of linear retention indices (RI) makes the screening strategy much less dependent on these chromatographic conditions. Additionally, com- mercial libraries commonly include RI values rather than 

 values. RI values for the identified compounds can be compared to the RI of the library entry. When the observed and the library RI values are close, the identification is confirmed with increased confidence. When the observed RI differs from the library entry value, the hit is removed from the list of identified compounds.

The screening approach described in this work utilizes GC–MS for a rapid (3.4-min) analysis of fruit rinsates, followed by compound identification, based on deconvoluted mass spectral search and time-filtering, using RIs for increased identification confidence. Acetone rinsates of fruit provided a qualitative estimate of residual foliar applied pesticides. The advantage of this sample collection technique is a high pesticide-to-matrix ratio compared to sample preparation techniques that include sample grinding and extraction. This approach can be successfully implemented for initial commodity triage. Any fruit for which the rinsate is found to contain compounds of concern can be further subjected to a comprehensive and quantitative sample preparation using the “quick, easy, cheap, effective, rugged, and safe” (QuEChERS) method, followed by targeted quantitative analysis performed with GC–MS operated in selected ion monitoring (SIM) mode or triple-quadruple GC–MS in multiple reaction monitoring (MRM) mode.

The entire triage workflow was performed in under 6 min, which included fruit rinsing, GC–MS analysis, data processing, and report generation. The unprecedented speed of GC–MS screening performed in 3.4 min was achieved with direct heating technology, which provides oven ramping at a rate of 250 °C/min.

As a demonstration of the utility of this approach, five types of fruits and berries (including strawberries, bananas, lemons, cherries, and peaches) were purchased in a local grocery store and a farm around Wilmington, Delaware, and subjected to analysis with the method.

The analysis was performed using an Agilent Intuvo 9000 GC instrument equipped with a 7650A Automatic Liquid Sampler that was coupled with an Agilent 5977B MSD (Agilent Technologies). The GC–MSD system was configured to enable the shortest cycle time, avoid carryover, and maximize throughput. Figure 1 shows the system configuration used in this work, and the instrument operating parameters are summarized in Table I. The optimized chromatographic method included an HP-5MS UI column (10 m × 180 μm × 0.18 μm)(part number 19091S-571UI-INT) and the Intuvo Restrictor Module (part number G39093-63008), with temperature programming of 250 °C/min that enabled a 3.4 min run time. Helium flow rate was 0.75 mL/min and 1.25 mL/min through the column and the restrictor, respectively. The injection volume was set to 1 μL. A multimode inlet was set to 280 °C during the injection, and the temperature was increased to 350° in post-run to minimize carryover caused by residual sample matrix accumulated in the inlet. The multimode guard chip (G4587-60665) prevented high boiling matrix compounds from contaminating the head of the column, and was operated in the track oven mode.

The GC system was configured for mid-column backflushing using the programmable makeup helium supply. A post-run backflush was initiated at 3.4 min after linear n-alkane C

 was eluted. There was no need to continue the chromatographic run past 3.4 min, because no pesticides were expected to be eluted beyond this time. However, heavier matrix components may still be on the GC column by the end of the analytical run. To eliminate carryover and the appearance of ghost peaks in subsequent runs, post-run backflush was initiated at 3.4 min by raising the pressure on the programmable makeup helium supply at the midpoint, thereby reversing the flow through the first column, and increasing the flow through the second column. The reversed flow carries any high boilers that were in the column and the guard chip at the end of data collection out into the split vent trap, thereby extending the life of the columns and the guard chip. For this configuration, the backflushing time was 1 min.

The bus temperature was set to 320 °C, and the MS EI source and MS quadrupole were heated to 300 °C and 150 °C, respectively. The mass spectrometer operated in full scan data acquisition mode (

MassHunter Quant 10.1 Unknowns Analysis (MH UA) software was used for data processing. The spectral deconvolution function provided an automated means of quickly identifying compounds, even in high matrix samples, in the presence of coeluted compounds using library match score (LMS). Time filtering using RI values increased compound identification accuracy. MH UA recalculated RI values observed for the component in the sample and RI values available in the library to 

 calibration. The difference between the calculated 

 values for the observed component and the library entry was listed in the report. Unlike 

, RI is independent of the flow path, and is applicable to a custom column configuration. Further details on the method setup can be found in the application note describing the screening workflow (5).

Five varieties of fruits and berries not labeled as organic, including strawberries, bananas, lemons, cherries, and peaches, were purchased at a local retail store and a farm in the Wilmington, Delaware, area. The tested fruit was placed into a glass funnel and rinsed with acetone (≥99.9% [dried basis], Honeywell Burdick & Jackson), using a 500 mL PTFE squeeze bottle. With bananas, lemons, and peaches, one piece of fruit was placed into the funnel. With strawberries and cherries, several berries were placed in the funnel to fill up 2/3 of the funnel volume. The rinsate was collected into a 4 mL amber vial until the vial was full. The funnel was rinsed on the inside with acetone between the samples.

 is a key parameter to aid analyte identification. However, 

 depends on the GC flow path, column stationary phase, and dimensions, as well chromatographic conditions. Alternatively, relative 

 can be used for analyte identification. Originally, relative 

s for isothermal GC separations known as Kovats Indices were used. Subsequently, the calculations of relative retention were adapted to apply to temperature-programmed GC separations and these values became known as linear retention indices (RI or LRI). RI works well for identifying compounds, as long as the oven temperature program is not complex, and the stationary phase type is the same as that used to collect the reference library RIs. Application of RI requires that an n-alkane mixture be run that covers the elution range of the analytes of interest (some practitioners use straight chain methyl- or ethyl-esters). In this work, the RI of the latest eluted pesticide in the pesticide library was under 3400, suggesting that RI calibration needed to be performed up to C

 linear n-alkane. A detailed workflow describing how to create RI calibration with MassHunter Quantitative Analysis is described in a technical brief (6). In this work, RI calibration was performed over the range of linear n-alkanes C

 with a custom n-alkane standard (Ultra Scientific, Agilent Technologies Inc.), containing every linear n-alkane between C

. The concentration of several alkanes, including C

, was twice that of other alkanes simplifying their identification.

Figure 2 shows the scan total ion chromatogram (TIC) of a strawberry acetone rinsate. Although sample preparation via rinsing the surface decreased the amount of matrix introduced to the system compared to the conventional sample preparation techniques that involve sample grinding and extraction, it still brought over some matrix compounds, as seen in the scan TIC on the top of Figure 2.

FIGURE 2: (a) Screening results for strawberry rinsate identified against (b) the pesticide and (c) the NIST spectral libraries, featuring (d) identification of fludioxonil.

The scan file for the strawberry rinsate was then analyzed with the deconvoluted components searched against a custom pesticide library that included 1081 entries (spectra and RI) for pesticides and common environmental contaminants. The minimum library match score (LMS) was set to 55 and time filtering was enabled with an 

 penalty-free range of ±12 sec. Ten compounds were identified against the library of pesticides and environmental contaminants in the strawberry rinsate that met the method criteria. The generated report summarizing the identified compounds is shown under the TIC in Figure 2. The peaks corresponding to the pesticides identified in the strawberry rinsate sample, and their retention times are labeled in red in the TIC on the top of Figure 2. These pesticides are highlighted in blue in the table underneath the TIC. Among the remaining identified compounds are environmental contaminants like dicyclohexyl phthalate, possibly from packaging, cis-1,2,3,6-tetrahydrophthalimide (a possible metabolite of captan), a fungicide, tolclofos-methyl, and a phenol derivative, 4-methylphenol of natural origin.

The pesticide library includes the information only on the compound RI, but not 

. RI calibration is used to calculate library 

 of the detected components with the library entries. The filtered hits are dis- played in the report. In Figure 2, only 

 values are shown. If desired, RI values also can be displayed in the report.

As an example, one of the reported pesticides in Figure 2, fludioxonil, had a low LMS of 64.2. However, confidence in the identification was improved because of the small delta 

 (-0.028 min), which is a difference between the observed 

 and that calculated based on the library RI.

Additionally, ions present in the deconvoluted spectrum for fludioxonil were relatively unique, and matched those from the inverted library reference spectrum (Figure 2, mirror plot on the bottom right). The spectral deconvolution process had removed the interfering ions that appeared in the spectrum before deconvolution, producing an LMS of 64.2 that would not have been achieved for the raw spectrum. The alignment of extracted ion chromatograms for characteristic ions is shown on the bottom left in Figure 2.

The inspection process of the compounds reported in the sample is repeated for all the found hits to generate a list of compounds included in the final report. The decision as to what compounds to add to the report depends on several factors, including (but not limited to) LMS, uniqueness of the ions, 

 match, and degree of concern for a specific compound. The component area item is also useful as an indication of the relative size of the response for the listed hit.

The custom mass spectral library with >1000 compounds that contains exclusively pesticide and environmental contaminants is convenient for screening, because the number of hits to be inspected is limited. However, there are cases when a much broader screen may be desired. The same approach can also be used to search the deconvoluted components against the NIST 17 library, which contains over 260,000 spectra. NIST 17 contains RIs experimentally determined on semi-standard non-polar columns of the type used here for many of the entries. The deconvoluted spectra of the components are searched through NIST 17 and the LMS for the identified components are shown in the report (the second table in Figure 2). To increase confidence of identification, time-filtering is performed using the algorithm described above. Briefly, the NIST RI values are automatically recalculated to 

s for the hits are compared to the calculated NIST 

 values to enable time filtering. Although this is a very powerful tool, it searches all matrix components and can produce a very large list of hits to be inspected. For example, the screen of the strawberry rinsate produced 135 hits with LMS values > 55 when time filtering was applied. Time-filtering limited the number of compounds included in the report from 300 to 135. Reviewing the search results from NIST 17 with over 100 hits is more time consuming than that from the custom library.

The second table in Figure 2 shows a portion of the screen results from NIST 17 for the strawberry rinsate. The Library 

 shown in the table is calculated based on the RI value from NIST 17 using RI calibration. The RI value from NIST 17 used for calculating the 

 is either the experimental RI for the semi-standard non-polar phase if available, or a theoretical value calculated from molecular parameters. Note that the latter is of limited value, as the errors in the predicted RI can be quite large. Fludioxonil, a fungicide identified in the strawberry rinsate when searching against the pesticide library, was also identified against NIST 17 with a comparable LMS of 61.3 and a small 

NIST search is useful for comprehensive screening to uncover unexpected compounds. It takes longer reviewing time, because of the large number of naturally occurring compounds from the matrix. Also, some compounds identified against the pesticide library may not be present in NIST 17 (for example, fluxapyroxad, which was found in the strawberry rinsate, is not included in NIST 17).

The NIST 17 screen can serve multiple purposes:

Confirming identifications of compounds found with the pesticide library screen.

Finding alternative identifications for the hits with questionable LMS values.

Identifying chemicals not in the pesticide library that may be of concern.

Other fruits and berries, including bananas, lemons, peaches, and cherries, were screened for pesticides and their rinsates were analyzed against the pesticide library. The results are shown in Figure 3, with the pesticides highlighted in the tables in blue. It is of note that various fruits resulted in different levels of matrix background in the scan TIC. Bananas and cherries showed lower backgrounds, whereas strawberries, lemons, and peaches produced a higher scan TIC. In some cases, base peaks in chromatograms were attributed to pesticides detected in acetone rinsates as a result of a favorable pesticides-to-matrix ratio, such as thiabendazole in bananas, fludioxonil in lemons, and captan in peaches.

FIGURE 3: Screening results for (a) banana, (b) lemon, (c) peach, and (d) cherry rinsates identified against the pesticide library.

With the hardware employed, the oven ramp rate can be lowered to yield a significant increase in a chromatographic resolution. For example, to more closely evaluate a screening result and increase confidence in compound identification, chromatographic and spectral interference can be reduced by using the slower oven ramp. Further details, and an example of the identity verification for compounds determined in peach rinsate, can be found in the application note describing the screening workflow (5). Additionally, the screening can be applicable to various contaminants monitored beyond the food and beverage industry. For example, the system configuration and work flow described in this work were used for rapid phthalate screening in plastics, as described in an application brief (7).

A rapid screening for pesticides and other contaminants found on the surface of fruits and berries was enabled using a GC oven with direct heating technology and MS spectral deconvolution, resulting in a 3.4-min analysis time with a total workflow time of under 6 min that includes sample preparation, analysis, data processing, and reporting. Rapid and reliable identification of pesticides was achieved by library searching of deconvoluted spectra coupled with time-filtering using RI values. Both user-created and the NIST 17 spectral libraries were used for com- pound identification. The screening workflow described herein should be useful for triaging samples in high-throughput facilities, such as receiving docks and distribution centers.

(1) M.C. da Silva, M.L.G. Oliveira, R. Augusti, and A.F. Faria, 

(2) Z. Zhang, M. Feng, K. Zhu, L. Han, Y. Sapozhnikova, and S.J. Lehotay. 

(3) Y. Pico, A.H. Alfarhan, and D. Barcelo. 

(4) A. Andrianova, B. Quimby, J. Westland. Agilent Technologies Application Note, publication number 5994 0915EN (2019).

(5) A. Andrianova and B. Quimby, Agilent Technologies Application Note, publication number 5994 2077EN (2020).

(6) C. Sandy, I. Butler, and R. Sullivan, Agilent Technologies Technical Brief, publication number 5991-8221EN (2017).

(7) A. Andrianova and B. Quimby. Agilent Technologies Application Brief, publication number 5994 2727EN (2020).

 is a GC/MS Applications Scientist at Agilent Technologies, in, Wilmington, Delaware. 

 is a Senior GC/MS Applications Scientist at Agilent Technologies, in Wilmington, Delaware. Direct correspondence to: 

Trace-level pesticides and environmental pollutants in the food supply continue to be a worldwide concern. Here we present a fast-screening workflow for residual pesticides present on the surface of fruits using a GC–MS system with direct heating technology and MS spectral deconvolution.

Rapid Rinse and Shoot: A Screening Workflow for Pesticides in Fruit by GC–MS in Under Six Minutes Using Library Searching of Deconvoluted Spectra

This installment of “Perspectives in Modern HPLC” describes newly introduced high-performance liquid chromatography (HPLC), mass spectrometry (MS), chromatography data systems (CDS), and related products at Pittcon 2021 and the prior year. It summarizes their significant features and user benefits.

The Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy (Pittcon) is one of the world’s largest laboratory science conferences. The 73rd Pittcon was supposed to be held at Ernest N. Morial Convention Center in New Orleans from March 8–12, 2021 but went virtual because of the Covid-19 pandemics. Nevertheless, the 2021 Pittcon technical program remained robust, with 2000+ presentations in plenary lectures, symposia (invited, award, or contributed), and poster–networking–affinity group or workshop sessions. In addition, 80+ short courses were held as webinars in a span of two weeks. The 3-day instrument exposition also went virtual, albeit with fewer than 200 exhibiting companies.

New HPLC, MS, and CDS Products in 2020–2021

In recent years, major manufacturers of HPLC instruments have turned their attention to HPLC system extensions, mass spectrometers (MS), and devices for sample preparations or injections. Table I lists new product introductions, ordered alphabetically by the vendor’s name, from 2020 to 2021, categorized as HPLC systems and modules, mass spectrometry (MS), chromatography data system (CDS), and other software. Key innovative features and benefits of each product are described.

HPLC and UHPLC Systems and Line Extensions

The introduction of new ultrahigh-pressure liquid chromatography (UHPLC) systems has slowed, as manufacturers focused on HPLC line extensions for higher productivity, ease of use, and specific applications such as those for biopharmaceutical quality control applications (1).

Agilent offers an extension to the 1290 Infinity II LC family, with a 1300-bar UHPLC system with a binary pump and a flow path constructed from biocompatible materials for high-salt or extreme pH mobile phases applications.

Agilent 850-DS Dissolution Sampling Station and NanoDis System

This sampling station simplifies pharmaceutical dissolution testing workflow using automated sampling, filtering, sample collection, and cleaning. The NanoDis is an adaptation for dissolution testing of nanoparticles.

An ultra-low-dispersion HPLC system with a pressure limit of 862 bars (12,500 psi) with a binary pump and photodiode array (PDA) or ultraviolet-visible (UV-vis) detector with instrument control and data handling by ClarityChrom CDS.

PerkinElmer LC 300 HPLC and UHPLC Systems

The LC 300 system is available as a 10k psi (690 bar) HPLC or 18k psi (1240 bar) UHPLC configuration. It features an autosampler, column oven, and a choice of five detectors (PDA, multiple-wavelength, ultraviolet–visible spectrometry [UV-vis], fluorescence, and refractive index). Both systems are paired with SimplicityChrom CDS.

Shine (Qingdao Shenghan Chromatograph Technology) CIC-D150 and D180 Ion Chromatographs

The CIC-D150 and D180 are compact, low-cost, metal-free (polyether ether ketone) ion chromatographs (IC) for the analysis of cations or anions with suppressed-conductivity detection. Data handling and system control are accomplished using Clarity CDS. The performance of the CIC-D180 system for the sensitive quantitation of anions is shown in Figure 1. The D180 system has the ability for automated reagent generation for the mobile phase.

FIGURE 1: Chromatogram illustrating the sensitivity performance of the CIC-D180 in anion analysis.

The LC-2030C NT is an integrated HPLC system catering to operators with little HPLC experience. It accommodates a slide-in cartridge or packed column. The system is intuitive to operate, and has a quaternary pump, autosampler, PDA, and built-in touch panel with many automated functions (shown in the workflow diagram of Figure 2). The pressure limits are 5000 psi or 1000 psi for the packed column or slide-in cartridge, respectively (monolith-based microfluidic system that can connect directly without fittings). It is compatible with LabSolutions version 5.92 or later.

FIGURE 2: Graphics illustrating the automated workflows of the Shimadzu LC-2030C NT.

Shimadzu i-Series LC-2050 and LC-2060 Models

These compact, integrated HPLC systems (7000 or 10,000 psi; 340 or 700 bar) have low carryover (<0.0025%), and are capable of high-speed sampling (14-second injection cycle with sample capacity of 1500 samples for quality control [QC] applications). They are equipped with a built-in controller keyboard and compatibility with LabSolutions CDS. They incorporate intelligent features, such as reagent monitoring, accessing from mobile devices for remote monitoring and reanalysis and maintenance resources, and an I-PDeA II deconvolution software add-on to LabSolutions for quantitative peak areas assessment for partially resolved analytes.

Highlights of the new high-performance liquid chromatography, mass spectrometry, and chromatography data systems introduced over the past year.

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