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Single-Cell Analysis by Inductively Coupled Plasma–Time-of-Flight Mass Spectrometry 

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Bridging the Gap Between GC–MS and LC–MS

Gas Chromatography–Mass Spectrometry (GC–MS) with Cold Electron Ionization (EI)

The New York Microscopical Society 2020 Ernst Abbe Award was presented to Brian J. Ford on Tuesday, November 17. He delivered a talk titled “The Lion, the Witch, and the Microscope.”

Ford is one of the world’s well-known microscopists. He has published 40 books, 20 of them on microscopy, and his books have appeared in 150 editions worldwide. He has also written hundreds of papers and has made important discoveries in fields ranging from hematology and cell intelligence to protozoology and plant physiology.

Ford is best known for his discovery of the original specimens sent to London by the “Father of Microbiology,” Antony van Leeuwenhoek, which had been lost for more than 300 years. He also recently identified two additional Leeuwenhoek microscopes that were not known to exist. He is a Fellow of Cardiff University, a former Fellow at The Open University, and was appointed as a visiting professor at Leicester University. Ford is also president emeritus of the Cambridge Society for the Application of Research.

Ford was awarded the inaugural August Kölher medal of the State Microscopical Society of Illinois, and recently the Royal Microscopical Society elected him an Honorary Fellow, the society’s highest honor. He has also served as a consultant for Guinness World Records and 

 and he has hosted many programs for the BBC. He presented his own weekly BBC program, 

, and has also contributed to many foreign television stations.

Ford is currently a contributor to publications such as 

. His also runs a regular column called “Critical Focus,” which appears regularly in 

Articles

The New York Microscopical Society 2020 Ernst Abbe Award was presented to Brian J. Ford on November 17.

Brian J. Ford Receives the New York Microscopical Society Ernst Abbe Award

Gas chromatography–mass spectrometry (GC–MS) is a central analytical technique for a broad range of applications and disciplines. A major strength of GC–MS with its standard electron ionization (EI) ion sources is its ability to provide easy sample identification with names and structures at the isomer level through the use of 70 eV EI libraries.

However, GC–MS suffers from two major limitations: First, only a relatively small range of volatile, thermally stable compounds are amenable for analysis, and second, EI mass spectra suffer from a frequent absence or weakness of the molecular ions. Absent or weak molecular ions result in a reduced confidence level in the sample identification via the library and make it impossible to identify the majority of compounds that are not in the library. As a result of these two limitations, the use of liquid chromatography–mass spectrometry (LC–MS) is growing.

Several other softer ion sources were developed for GC–MS, including field ionization (FI), photoionization (PI), chemical ionization (CI), and atmospheric pressure chemical ionization (APCI). Chemical ionization can serve for the provision of protonated molecular ions, and CI ion sources sometimes supplement EI ion sources in GC–MS. However, CI usually requires venting the GC–MS system and the manual replacement of the ion source; therefore, given it is less sensitive than EI, CI is incompatible with library-based sample identification and ineffective with several important classes of compounds with low or negative proton affinity. In addition, its closed ion source design further restricts the volatility range of compounds accessible for analysis and promotes increased peak tailing and sample degradation. CI also adds cost to a GC–MS system. Thus, CI, like the other “soft” ion sources, is rarely used. Another claim for a softer ion source is to use low electron energies, but the use of low eV EI does not provide molecular ions for compounds in which it is absent in 70 eV EI (1).

In the last thirty years, we developed and explored the performance capabilities of a new type of GC–MS based on the use of supersonic molecular beams (SMB) for interfacing the GC and MS instruments and as a medium for electron ionization of vibrationally cold sample compounds in the SMB (cold EI), in a contact-free fly-through ion source (2–15).

A supersonic molecular beam is formed by the expansion of gas through a small pinhole or shaped nozzle into a vacuum chamber. In this adiabatic expansion, the carrier gas (typically helium) and sample molecules obtain the same final velocity so that the sample compounds are accelerated to the carrier gas velocity. The uniform velocity ensures slow intrabeam collisions, resulting in internal vibrational–rotational cooling of the sample compounds. SMBs are characterized by a few features including: a) vibrational and rotational cooling of the seeded sample molecules; b) unidirectional molecular motion in space with heavy species concentration along the beam axis (jet separation); c) controlled amount of sample species kinetic energy up to 20 eV; and d) high carrier gas flow rate compatibility up to 100 mL/min.

GC–MS with cold EI improves all the central performance aspects of GC–MS. This includes improving sample identification via the provision of enhanced molecular ions combined with improved mass spectral isomer and structural information (2–5). It also makes a significantly extended range of compounds (and applications) amenable for analysis (6) and the GC–MS process effectively fast (5,7). There is improved sensitivity, particularly for sample compounds that are difficult to analyze (8,9). Lastly, very good response uniformity is provided for improved quantitation and for the measurement of chemical reaction yields (10).

Cold EI began in 1990 with the discovery of the large enhancement of the molecular ion of cholesterol and bromopentane upon its cooling in SMB (11–13). In 1994, GC–MS with cold EI was born (14) via the use of ultrafast isothermal GC and, shortly after that, the first paper on GC–MS with cold EI analysis of hydrocarbons using a standard GC instrument was published (2). In 2010, Aviv Analytical commercialized GC–MS with cold EI based on the conversion of an Agilent 5975 GC–MS instrument into GC–MS with cold EI (15). Mass spectrometry of vibrationally cold molecules in free jet or SMB was also explored by several other groups (16–26).

Experimental: The GC–MS with Cold EI Systems

Figure 1 shows a schematic of the Aviv Analytical 5977-SMB GC–MS with cold EI system. The GC column output is mixed with helium make-up gas (typically 60 mL/min total for cold EI or ~5 mL/min total for classical EI with SMB), at typically 830 mBar pressure in front of a supersonic nozzle. The supersonic nozzle itself is based on a shaped pinhole with a 100 μm diameter made from ruby ceramic, located at the end of a heated, temperature-controlled transfer line. The helium make-up flow can be mixed (via the opening of a respective solenoid valve) with perfluorotributylamine (PFTBA) vapor for periodic system tuning and calibration. Sample compounds seeded in helium expand from the supersonic nozzle into a nozzle vacuum chamber that is differentially pumped by an Agilent TwisTorr 304 turbo molecular pump with 250 L/s pumping speed. The expanded sample compounds form a supersonic molecular beam with vibrationally cold molecules. These vibrationally cold sample compounds are skimmed by a 0.8 mm skimmer and are collimated in a second differentially pumped vacuum chamber. A Pfeiffer 250 L/s turbomolecular pump is used as provided by Agilent for its 5977 MS for the pumping of the ion source and MS vacuum chamber. The molecular beam with cold sample compounds fly through a dual cage EI ion source (27) where these beam species are ionized by 70 eV electrons with 5–10 mA emission current. The ions are focused by an ion lens system, deflected 90o by an ion mirror and introduced into the original Agilent 5977 mass analyzer of the GC–MS system. The ion lens system also serves for vacuum background filtration through low positive voltage biasing of one lens element that repels low kinetic energy vacuum background ions while transmitting the energetic SMB ionized species. The 90

 ion mirror is separately heated and serves to keep the mass analyzer clean and to suppress mass-independent neutral noise. The ions that exit the Agilent MS are detected by the Agilent triple-axis ion detector and the data is processed by the software.

Enhanced Molecular Ions for Improved Sample Identification

Cold EI is the name that we gave to electron ionization of vibrationally cold molecules in SMB. With SMB the sample compounds expand from the supersonic nozzle while they are at the nozzle temperature (typically 250–300 

C). Soon after the expansion, the sample compounds are vibrationally and rotationally cooled while reaching the helium (being the majority) velocity (3–5,13). The sample molecules fly through the dual cage ion source without scattering from its walls and, as a result, are ionized while being vibrationally cold.

Figure 2 demonstrates the effect of vibrational cooling on EI mass spectra via the comparison of 18 eV cold EI mass spectrum of n-C

 (upper left trace) with its 70 eV cold EI mass spectrum (middle left trace) and its NIST library standard EI mass spectrum (lower left trace). Note that the relative abundance of the molecular ion is increased by over a factor of 100 in cold EI. As also shown, at 18 eV the molecular ion is further enhanced via the suppression of fragment ions but such a low eV cold EI mass spectrum suffers from incompatibility with the NIST library identification and the total ion count signal is weaker thus we consider the 70 eV cold EI MS as the most useful MS ionization technique. On the right side of Figure 2, the effect of vibrational cooling on EI mass spectra is demonstrated for squalane which is a highly branched isoprenoid C

 hydrocarbon. The 18 eV cold EI mass spectrum of squalane (upper right trace) is compared with its 70 eV cold EI mass spectrum (middle right trace) and its NIST library standard EI mass spectrum (lower right trace). As for n-C

, we observe a major SMB cooling effect on the squalane mass spectra. Figure 2 also shows that cold EI significantly amplifies isomeric, structurally important fragment ions and thus cold EI enables much easier conversion of its mass spectrum into the squalane structure than standard EI.

The availability in cold EI of molecular ions for hydrocarbons and their branched isomers uniquely enables isomer distribution analysis, which is a very important application for the optimization of fuels and oils quality (28).

Sample Identification via Cold EI Mass Spectra and the NIST Library

One of the primary goals and benefits of GC–MS is sample compound identification by the available large MS libraries such as the NIST library, particularly with its excellent sample identification algorithm (29–31). Thus, a common question is asked: “How does the enhanced molecular ion abundance affect the results of National Institute of Standards (NIST) library identification?”

Figure 3 shows the mass spectra of dimethoate, measured with GC–MS with a standard EI system (lower trace) and with GC–MS with a cold EI system (upper trace). The molecular ion’s relative abundance is enriched in cold EI by about a factor of 20. However, the library identification probability is higher for the cold EI mass spectrum (97.9%) than for the standard EI (97.3%) even though cold EI exhibits a lower match factor. In reference 32, we showed a computer simulation of the identification probability versus the molecular ion enhancement factors, and as described, there is a large range of molecular ion enhancement factors that actually improve the NIST library identification. Furthermore, NIST library mass spectra typically show higher abundance molecular ions than in current GC–MS systems. For example, dimethoate has about 8% molecular ion in the NIST library (up to 14% in one replica MS) while our data with an Agilent 5977 with its inert ion source at 250 

C shows only 4.3% relative abundance for standard EI.

We studied 46 compounds and found that cold EI provides on the average higher identification probabilities than standard EI (1).

Accordingly, cold EI is the only soft ionization method that is fully compatible with NIST library–based sample identification.

While the enhancement of the molecular ion in cold EI results in lower matching factors but usually with superior identification probabilities (32,33), for certain compounds and particularly for the identification of certain isomers that exhibit weak isomer mass spectral effects, the availability of “classical EI” mass spectra could be desired. Thus, there is some limited need to supplement and complement cold EI with classical EI in the same GC–MS system ion source.

Thus, GC–MS with cold EI provides a “smart EI” ion source with three modes of operation of cold EI, low eV soft cold EI, and classical EI-SMB, while the change from cold EI to classical EI-SMB mode is achieved via a simple method change in a matter of seconds up to a minute via the reduction of the helium make-up cooling gas flow rate. Figure 4 demonstrates classical EI-SMB and cold EI mode changing in 10 s during a single analysis run. A test mixture was analyzed with 1 ng each hexadecane, methyl stearate, cholesterol and dotriacontane (n-C32H66) with mode changing as indicated in the middle of the run at 8.5 min (experimental conditions are in the Figure 4 caption). Accordingly, we conclude that with cold EI there is no need to have an additional standard EI ion source and if there is a desire to have classical EI mass spectra, the same ion source can be used with a method-based fast mode change and provide classical EI mass spectra with high NIST library matching factors.

Thus, with cold EI, there is no need to have any additional standard EI ion source.

Isotope Abundance Analysis (IAA) for the Provision of Elemental Formula from Cold EI Mass Spectra

Clearly, it is highly desirable to be able to obtain elemental formula from low-cost unit resolution quadrupole based GC– MS systems particularly for the many compounds that are not in the EI–MS libraries. We developed the Tal-Aviv Molecule Identifier (TAMI) software (34–36) that works well with standard EI MS and excels with cold EI MS by providing of abundant molecular ions. TAMI automatically converts the molecular ion group of isotopologues and the measured molecular ion mass (which it can improve its mass accuracy) into elemental formulas, and automatically (zero clicks) confirms or rejects the identification results of the NIST library. As a result, cold EI with TAMI provides an ideal sample identification technology for low cost unit resolution quadrupole MS that is superior to any GC–MS with expensive high-resolution MS (35). We found that isotope abundance analysis alone with 0.14 amu mass accuracy of quadrupole MS in centroid mode is equivalent in its ability to provide the correct elemental formula to 5 ppm accurate mass while if it is used in profile mode with a mass tune function, TAMI can provide elemental formula equivalent to high resolution mass spectrometers with 1 ppm mass accuracy that do not consider the isotope abundance (such as orbital trap instruments) (36).

Extending the Range of Compounds Amenable for Analysis: Bridging the Gap Between GC–MS and LC–MS

The recognized and well-known Achilles heel of GC–MS is its limited range of volatile and thermally stable compounds that are amenable for analysis. This limitation emerges from sample degradation induced by the GC injector, column, or ion source, or a lack of sufficient vaporization.

GC–MS, with cold EI, enables a significant extension of the range of compounds amenable for analysis via the use of short columns with high column flow rates in combination with full elimination of intra-ion source degradation (5,6). GC–MS with cold EI provides the ultimate range of compounds amenable for GC–MS analysis and thus effectively bridges the gap between standard GC–MS and LC–MS. The key parameter for this capability is the use of very high column flow rates, which in combination with the use of shorter columns leads to significantly lower elution temperatures (up to 200 °C lower than in standard GC–MS), while the provision of enhanced molecular ions compensates for the traded GC separation. High column f low rates further reduce intra-injector liner degradation (lower GC injector temperatures and reduced liner residence time) and intra-ion source sample dissociation is inherently avoided due to the cold EI fly-through ion source geometry (5,6,27).

The expanded GC–MS range of compounds amenable for analysis can be separated into three groups: an extended range of thermally labile compounds, an extended range of low volatility compounds, and an extended range of polar compounds.

Figure 5 demonstrates the analysis of the polar compound reserpine (C

, MW = 608) by the 5975-SMB GC–MS with cold EI. Although reserpine is the LC–MS industry standard specification compound, GC–MS with cold EI is the only GC–MS that can analyze this large polar compound and it provides a dominant molecular ion. Thus, reserpine serves as an example of the extended range of polar compounds amenable for GC–MS analysis by cold EI.

Figure 6 shows an illustration that demonstrates our perception of the average total ion count (TIC) and molecular ion signal dependence on the sample compound mass obtained with cold EI and with standard EI. From this figure, one can calculate the average sensitivity gain of cold EI with the 5977-SMB GC–MS with cold EI versus sample mass, which at some point is transformed into extended range of compounds amenable for analysis.

Detectability is considered to be one of the most important GC–MS parameters used to characterize the quality and value of an instrument. However, detectability is often misrepresented because all the major vendors use octafluoronaphthalene (OFN) for their specifications in selected-ion monitoring (SIM) or full scan reconstructed SIM (RSIM) modes and instrument detection limit (IDL). OFN specifications are misleading for real samples since OFN is one of the easiest compounds to detect. GC–MS with cold EI can obtain for one pg OFN S/N > 10+6 because it has practically no noise (37). However, GC–MS detectability should be evaluated by its performance using a mixture of several selected “hard to analyze” compounds, along with OFN (8).

Thus, Figure 7 shows that cold EI exhibits much better S/N than standard EI for compounds such as cholesterol and n-C

. As demonstrated, cold EI provides for 10 pg cholesterol in SIM on its molecular ion (

 = 386.4) over 600 times better S/N while for n-C

 the cold EI S/N is over 2000 times better. The 600 times superior cold EI S/N for cholesterol emerges from three main reasons: a) The molecular ion is enhanced in its absolute abundance by about 30 times; b) the total ion chromatogram (TIC) signal of cholesterol is reduced due to ion source peak tailing over 10 times compared with those of early eluters such as OFN; and c) the noise on the column bleed plateau where cholesterol elutes in standard EI is increased compared with that on OFN molecular ion while it is very low at the cholesterol elution time in cold EI. With n-C

 the S/N gain of cold EI is higher also due to greater enhancement of the molecular ion. We note that the obtained S/N for cholesterol is further reduced with the Agilent high-efficiency source (HES) despite its high ionization yield due to its lower molecular ion abundance and greater ion source peak tailing. For these same reasons, n-C

 was not detected by the HES ion source in SIM at 10 pg on column amount.

Selected Applications of GC–MS with Cold EI

Since the development of GC–MS with cold EI hundreds of applications have been explored with it as listed in part in reference 38. However, here we briefly describe three applications that we consider interesting and demonstrating “bridging the gap between GC–MS with standard EI and LC–MS.”

The analysis of cannabis-based drugs is currently a highly popular subject. GC–MS with standard EI serves mostly for terpenes in cannabis analysis as well as for pesticides analysis while LC–UV or LC–MS serve for target cannabinoids analysis. However, universal untargeted cannabinoids analysis is not performed due to unavailability of appropriate tools for such analysis given that compounds with OH groups tend to tail and decompose on the metallic surface of standard EI ion sources. Furthermore, diglycerides and triglycerides are currently ignored. The most widely known and potent compounds in cannabis are delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) that were initially explored and the benefits of which promoted in Israel by Mechoulam and his group (39). Cannabis-related compounds are described by Brenneisen (40) who lists and describes 58 cannabinoids. Accordingly, an important yet unmet challenge is analyzing untargeted cannabinoid compounds in various brands of cannabis and correlating the cannabis brands’ medical effectiveness with their cannabinoid compound content (this is known as the 

Figure 8 shows the analysis of Cannabis EP-1, which is a drug for children with autism spectrum disorder. A GC–MS with cold EI mass chromatogram zoomed around the elution time of cannabinoids is shown in the upper trace while cold EI mass spectrum of cannabidivarol is shown at the bottom trace. As shown, cold EI analyzes the full cannabinoid content and provides indications to what is unique in it that makes it an effective drug for children with autism spectrum disorder. We measured that the THC/CBD ratio is 5.0%, in agreement with analysis by LC with diode array and calibration measurement (note that GC–MS with cold EI uniquely provided this ratio measurement without calibration) and found that the cannabidivarol (CBDV) abundance is relatively high compared with other cannabis extracts. In addition, we also found vitamin E in the form of alpha tocopherol, as indicated in Figure 8. The road is now open to use cold EI to explore the entourage effect to optimize the efficacy of cannabis-based drugs.

One of the most widely used GC–MS applications is service analysis in universities and research institutes. There are about 28,000 universities worldwide and most have chemistry or life science departments with at least one (or few) service GC–MS systems. In universities, an important goal is the measurement and optimization of organic chemical reactions. Although GC–MS is being used in universities, its performance is weak and LC–MS is preferred. GC–MS with cold EI uniquely provides several very important benefits for service GC–MS (10): First, it provides an extended range of compounds amenable for analysis, including much larger and more labile synthetic compounds. Second, trustworthy molecular ions are present for large organic compounds, which are then converted into elemental formulae with our TAMI software (34–36). Third, GC–MS with cold EI also allows for much faster analysis (8 min) for semi online reaction monitoring feedback. Fourth, uniform compound-independent responses are provided as needed for the provision of chemical reaction yields (10). And lastly, compound analysis that cannot be ionized in LC–MS by its various ion sources can be ionized here.

Figure 9 shows an example of GC–MS with cold EI analysis of an organometallic compound with a weak coordinative bond. This analysis is impossible with GC–MS with standard EI and failed with LC–MS. We used an 8 mL/min column flow rate and a 200 

C injector temperature to obtain the upper mass chromatogram in which the sample compound partially degraded at the injector and at the column (hump prior to elution). Upon a column flow rate increase to 24 mL/min and an injector temperature reduction to 150 

C as shown in the middle mass chromatogram, a practically clean peak was obtained with cold EI MS showing the correct molecular ion (bottom mass spectrum).

This example demonstrates the type of compounds that can be analyzed only by GC–MS with cold EI and no other MS instrumentation and thus as bridging the gap between GC–MS with standard EI and LC–MS (10).

Lipids in Human Blood Analysis for Medical Diagnostics

The analysis of cholesterol and triglycerides in human blood is one of the most important and widely used medical diagnostics tests. These tests are vital for the control and monitoring of various heart diseases and other medical issues. However, currently these tests provide limited information on protein-bound cholesterol (LDL and HDL) and not on the blood majority free cholesterol and various cholesteryl esters plus on the total glycerides and not on their type and their fatty acid chemical compositions.

We are currently engaged in the development of a new type of “Blood-Probe” device for the sampling of raw blood and its insertion for analysis with GC–MS with cold EI (41). BloodProbe is a ChromatoProbe (42) vial holder that was modified to hold a thin glass rod (the same in our Open Probe Fast GC– MS (43,44) (the Agilent name “Quick-Probe” [45]) for its insertion into a ChromatoProbe device followed by GC–MS with cold EI analysis. As shown in Figure 10, we can obtain information on the full range of nonpolar lipids and with no sample preparation and a simple diabetes lancet being used to (minimally invasive) draw 2.0 μL blood that was taken with a micro pipette and deposited on the BloodProbe thin glass rod for its introduction as a dried raw blood spot. Another benefit is that only 2.0 μL blood was used, thus avoiding the use of a syringe in hand veins that collects several mL (10– 30) of blood. Lastly, the instrument and method used provide far greater information about lipids in blood information at the molecular level in comparison with currently used methods.

GC–MS with cold EI has over 60 benefits and advantages over GC–MS with standard EI (46,47) without any downside and thus it is leading the way to the future of GC–MS. GC–MS with cold EI is bridging the gap with LC–MS and serves for the analysis of an extended range of compounds and applications that are either difficult by standard GC–MS or unamenable for analysis by GC–MS with standard EI or even LC–MS. The advantages of GC–MS with cold EI are based mostly on the ionization of vibrationally cold molecules during their axial flight path in a fly-through ion source and due to the ability to analyze samples with high column flow rates up to 100 mL/min without affecting sensitivity. We have explored several hundred applications with GC–MS with cold EI. Many of them are described in our publications while in our Advanced GC–MS Blog Journal (38) we describe 57 application notes.

GC–MS with cold EI is characterized by the following main 20 improvements and benefits:

1. Significantly extended range of com- pounds amenable for analysis (6).

2. Enhanced molecular ions and structurally-informative fragment ions (2–6).

3. The only soft ionization method that is fully compatible with NIST library identification (32).

4. Uniform response for improved quantitation and measurement of chemical reaction yields (10).

5. The same ion source provides cold EI, classical EI and low eV soft cold EI mass spectra with method-based mode changing (33).

6. Best provision of elemental formula with the TAMI software by providing enhanced molecular ions for greater range of compounds (34–36).

8. Column flow programming is provided without affecting sensitivity (7).

9. Improved sensitivity particularly for compounds that are difficult to analyze (8,9).

10. Lowest MS baseline noise by vacuum background filtration (elimination) and highest (by far) ratio of TIC peaks to column bleed (5).

11. Inherently inert ion source even for low pg range polar compounds (48).

12. No ion source related peak tailing is observed in cold EI for better signal as well as improved chromatography and separation (5).

13. Isomer distribution analysis is uniquely enabled (28).

14. Doubly charged molecular ions are provided for doubled mass spectral range (49).

15. Fastest (by far) ion source response time (5).

16. Improved selectivity and thus reduced matrix interference on the molecular ions.

17. Better linearity and reproducibility at low levels (48).

18. Best compatibility with pulsed flow modulation GCxGC–MS and a range of additional GC–MS enhancement technologies (50,51).

19. GC–MS with cold EI and LC–MS with cold EI can be combined in one MS system with method-based mode changing (52).

20. Robust ion source that rarely needs cleaning.

Naturally, some improvements are more important than others, and we consider the extension of the range of compounds amenable for analysis as the most important advantage of GC–MS with cold EI because it bridges the gap between GC–MS and LC–MS and opens up new areas of analysis and applications.

The combination of so many improvements creates a new and qualitatively superior technology that improves every type of analysis. While GC–MS with cold EI enables new types of analyses and significantly improves challenging analyses, it does not impede on any simple method of analysis (compared with standard EI) and its added cost could be negligible or none in a fully integrated GC–MS with cold EI. Consequently, we believe that GC–MS with cold EI is destined to lead the way for the future of GC–MS.

This research was supported by the Israel Science Foundation (grant No 356/15).

(1) K.J. Margolin Eren, O. Elkabets and A. Amirav, “A Comparison of Electron Ionization Mass Spectra Obtained at 70 eV, Low Electron Energies and with Cold EI and Their NIST Library Identification Probabilities” 

(2) S. Dagan and A. Amirav, “Electron Impact Mass Spectrometry of Alkanes in Supersonic Molecular Beams” 

(3) A. Amirav, A. Gordin and N. Tzanani, 

(4) A. B. Fialkov, U. Steiner, L. Jones and A. Amirav, 

(5) A. Amirav, A. Gordin, M. Poliak, and A. B. Fialkov, 

(6) A.B. Fialkov, A. Gordin and A. Amirav, 

(8) A.B. Fialkov, U. Steiner, S.J. Lehotay and A. Amirav, 

(9) A. Amirav, “Achieving the Lowest Limits of Identification – GC-MS with Cold EI versus Standard EI with High Efficiency Source” (accessed December 17, 2018). http://blog.avivanalytical.com/2018/12/ achieving-lowest-limits-of.html

(10) A. Amirav, A. Gordin, Y. Hagooly, S. Rozen, B. Belgorodsky, B. Seemann, H. Marom, M. Gozin and A. B. Fialkov, 

(15) Aviv Analytical. GC-MS with Cold EI - The Ultimate Performance GC-MS. http:// www.avivanalytical.com/Superson- ic-GC-MS.aspx

(16) H. Kishi, A. Tahara and T. Fujii, Int. J. Mass Spectrom. 209, 133–140 (2001).

Gas chromatography–mass spectrometry (GC–MS) with cold electron ionization (EI) is based on interfacing the GC and MS instruments with supersonic molecular beams (SMB) along with electron ionization of vibrationally cold sample compounds in SMB in a fly-through ion source (hence the name cold EI). GC–MS with cold EI improves all the central performance aspects of GC–MS. These aspects include enhanced molecular ions, improved sample identification, an extended range of compounds amenable for analysis, uniform response to all analytes, faster analysis, greater selectivity, and lower detection limits. In GC–MS with cold EI, the GC elution temperatures can be significantly lowered by reducing the column length and increasing the carrier gas flow rate. Furthermore, the injector temperature can be reduced using a high column flow rate, and sample degradation at the cold EI fly-through ion source is eliminated. Thus, a greater range of thermally labile and low volatility compounds can be analyzed. The extension of the range of compounds and applications amenable for analysis is the most important benefit of cold EI that bridges the gap with LC–MS. Several examples of GC–MS with cold EI applications are discussed including cannabinoids analysis, synthetic organic compounds analysis, and lipids in blood analysis for medical diagnostics.

Gas Chromatography–Mass Spectrometry (GC–MS) with Cold Electron Ionization (EI): Bridging the Gap Between GC–MS and LC–MS

Per- and poly-fluoroalkyl substances (PFAS) hold the potential to cause multiple health problems in humans (1). One possible source of these pollutants is drinking water. Currently, there are no maximum contaminant levels (MCLs) established for PFAS chemicals in drinking water, but the United States Environmental Protection Agency (EPA) has taken steps to evaluate the need (2). It has also issued a health advisory for two key compounds, perfluorooctanoic acid (PFOA) and perfluorooctyl sulfonic acid (PFOS) (3), and in February 2019 published its PFAS action plan (4). In June of this year, the EPA officially established reporting thresholds for 172 PFAS and added these to the list of chemicals that must be reported to the Toxics Release Inventory, which tracks the management of toxic chemicals that may pose a threat to human health and the environment under the National Defense Authorization Act (5).

The nature of PFAS, and the large number of possible compounds that can be synthesized, mean that although many are known, a notable proportion of PFAS variants are being detected for which there are no existing analytical standards. There are more than 5000 viable PFAS variants (6), including precursors and telomers. PFAS have been manufactured and used extensively since the 1940s; they are found in many consumer products, including cookware, food containers, stain repellents, nonstick products, polishes, waxes, and cleaning materials. Manufacturing facilities using PFAS to make these and other products may contribute to their release into the environment. The aqueous film-forming foams (AFFF) used in firefighting at sites such as military bases and airports are considered to be a large source of PFAS groundwater contamination.

Having been in use for the longest time, PFOA and PFOS are the best documented PFAS in terms of chemical properties and toxicological effects. They are found to accumulate in the body, and the EPA has established health advisory levels at 70 ppt. Consequently, industries in the United States have phased out the production of PFOA and PFOS, but both persist in the environment and their manufacture continues in other parts of the world.

While much is known about managing the risks of PFOA and PFOS, insights are scarcer for newer PFAS such as chemicals from the Gen-X process used to make high-performance fluoropolymers without PFOA and perfluorobutane sulfonic acid (PFBS), a replacement for PFOS. In response to the growing understanding of PFAS, the EPA has issued draft toxicity assessments for these compounds (7).

Environmental monitoring of PFAS is challenging because of the extensive range of novel compounds within this class that can be found in products or in the environment. In 2009, the EPA Office of Ground Water and Drinking Water developed a method specifically for the analysis of PFAS in drinking water based on solid-phase extraction (SPE), followed by liquid chromatography-tandem mass spectrometry (LC–MS/MS). In 2018, the method was updated and validated for additional PFAS compounds, including Gen-X (8). Given the health concerns surrounding this entire group of chemicals, the effective and reliable detection and quantitation of both known and novel compounds remains critical.

The sensitivity of LC–MS/MS instrumentation has continued to improve, and its usability for detecting polar compounds has made it the technique of choice for analyzing compounds of emerging concern such as PFAS in water. However, scientists undertaking targeted analysis of environmental contaminants have been limited to examining compounds for which analytical standards are available, and to the analytical methods necessary to identify those specific chemicals.

Advances in mass spectrometry now enable scientists to monitor established contaminants while detecting new analytes not on targeted screening lists. Recent developments in high-resolution, accurate-mass (HRAM)-MS offer high sensitivity along with a mass resolution that provides both accurate quantitation and screening capabilities for novel and nontargeted analytes. Advanced HRAM techniques and integrated data analysis strategies allow semi-quantitative analysis when standards are not available.

Both LC–MS/MS and HRAM have been used in work conducted by scientists at the Department of Civil and Environmental Engineering at Duke University to study PFAS in water. This group is part of the North Carolina Per and Polyfluoroalkyl Substances Testing (PFAST) Network, which is an academic consortium within the State of North Carolina and aims to assess the presence and distribution of PFAS compounds in the state’s public drinking water sources. The network takes both a targeted and nontargeted analytical approach to environmental analysis and testing, with nontargeted screening focused on identifying previously unknown and potentially toxic compounds.

North Carolina has many potential sources of PFAS contamination, including chemical manufacturing plants, textile production sites, military bases and fire departments. In recent years, compounds, such as Gen-X, have been detected in drinking water samples by PFAST network laboratories. In the work reported here, the goals were to: a) assess the sensitivity and quantitative performance of LC-HRAM for 48 targeted PFAS compounds in raw and finished drinking water matrices; b) perform non-targeted analysis of AFFF formulations for PFAS discovery; and c) analyze water extracts from a focused area of the Haw River in North Carolina for PFAS and emerging pollutants.

The Duke University laboratories implemented advanced analytical methods and instrumentation to achieve each of these three objectives.

The specifications of the Thermo Scientific Orbitrap Exploris 240 mass spectrometer used in this study are shown in Table I.

Targeted Analysis of Legacy and Emerging (GenX) PFAS

The analysis aimed to assess the sensitivity of the LC-HRAM for both legacy (PFOA) and emerging (GenX) PFAS, and verify its accuracy using a National Institute of Standards and Technology (NIST) standard reference material (SRM) for poly-fluorinated carboxylic acids (PFCAs). Direct injection (50 μL) of raw and finished drinking water matrices was performed by using the LC–HRAM-MS instrument, targeting 48 PFAS compounds. The process required minimal sample preparation with samples centrifuged at 11,000 x g (times gravity) for 5 min to remove particles, and the addition of 2% methanol containing 20 stable isotope-labeled PFAS surrogates at a final concentration of 50 ng/L per compound. Full scan mode was used for quantitation, with no SPE step. Two separate ion transfer tube temperatures were used for emerging (such as ether acid) and legacy PFAS compounds, due to many emerging PFAS’ susceptibility to thermal degradation, and performed two separate injections: The first at 150 °C for ether acids and at 325 °C for legacy PFAS compounds. Differing electrospray ionization (ESI) spray voltages (3500 V vs. 1500 V) and sheath gas flows (40 vs. 30 units) were also used for legacy compounds and ether acids, respectively. The full scan was performed at a resolution setting of 120,000.

For analysis of suspected PFAS in AFFF, analysis was conducted by the Thermo Scientific AcquireX data acquisition workflow to maximize MS/MS coverage for potentially low-abundance but interesting PFAS compounds. This approach was applied to nontargeted discovery analysis of PFAS in six AFFF formulations. These formulations were first diluted 10,000 times, followed by five sequential deep-scan injections for data-dependent acquisition (DDA) with both positive and negative ESI polarities.

The workflow enabled a sequential inclusion–exclusion approach, in which matrix, system, and background precursor features deemed irrelevant were mapped, imported into the DDA method, and excluded from MS data acquisition of the samples of interest. This list was then fed into the first sample analysis (full- scan MS detection) to create an “inclusion list” composed of compounds of interest. Subsequently, an iterative DDA process (with both exclusion and inclusion lists) was performed, with multiple injections after each run, and the lists were refined and updated throughout according to the MS/MS results.

Targeted and Nontargeted Analysis of PFAS in North Carolina’s Haw River

The workflow was used to analyze 13 water samples via three sequential DDA MS/MS injections of two composite water mixtures, to assess levels of PFAS and other emerging contaminants. The two mixtures were created—one combining six of the 13 samples and one combining seven—and subjected to a full DDA MS/MS deep-scan workflow. Both direct injection and SPE were used in sample preparation. Data was subsequently analyzed using the Compound Discoverer 3.1 software (Thermo Scientific) to annotate nontargeted compounds with molecular and structural formulae, where possible.

We first assessed the quantitative accuracy of our MS approach by testing for PFCAs in NIST SRM 8446 (which contains certified levels of PFCAs). The analysis saw generally good agreement in two different dilutions for PFCAs with perfluoroalkyl chains up to C10 (such as poly-fluorinated decanoic acid; see Figure 1), although compounds with higher molecular weights may have been lost due to the specifics of the highly aqueous solution used in the analysis (which may have caused adsorption onto vials or sample collection containers).

The analysis showed excellent sensitivity for targeted compounds, specifically GenX. For some, especially intermediate alky-chain length, compounds, we achieved detection limit sensitivities of between 1 and 5 ng/L; for some longer-chain compounds and also PFBA, sensitivities ranged up to 50 ng/L. For GenX, the estimated detection limit was 2 ng/L and overall detection limits achieved good sensitivity in the range of low-to-mid parts per trillion (as shown in Table II). Overall, calibration curves for legacy PFAS (PFOA) and emerging PFAS (GenX) were linear over a wide concentration range, as shown in Figure 2.

For nontargeted analysis of PFAS in AFFF, 307 total compounds were detected from six AFFF formulations in ESI(-) and 449 in ESI(+). The DDA analysis achieved 100% DDA MS/MS acquisition coverage for both polarities (over all five injections). From these compounds, 140 putative PFAS compounds were annotated in ESI(-) and 116 PFAS in ESI(+), which is seen in Figure 3. However, some redundant detections occurred due to branched isomers (where substances are similar in structure, but different in retention time).

In the Haw River water samples, the analysis detected 6767 total com- pounds in 13 water samples in ESI(-), with >90% DDA MS/MS acquisition coverage over three sequential injections. Using a harmonized set of spectral libraries (Thermo Scientific mzCloud mass spectral library, NIST MS/MS, and MassBank of North America), 228 “unique” library matches were obtained with MS/MS match scores greater than 80%. These were further analyzed using a hierarchical cluster approach to match samples to sites and were also used to check instrument mass accuracy with no mass accuracy errors exceeding 0.5 ppm. Such high mass accuracy is needed to perform trace level nontargeted screening work with confidence.

Additionally, using our LC–HRAM-MS/MS workflow and subsequent spectral analysis methodology, we were able to annotate some novel PFAS compounds in AFFF very easily using the Compound Discoverer software with spectra contained in the mzVault library. An example of a compound detected with a formula of C8HClF16O3S is shown in Figure 4 with annotation and library spectral matching as a mirror plot on the right.

Due to its inclusion-exclusion functionality, the DDA methodology with the workflow eliminated the need for data-independent acquisition (DIA) to ensure MS/MS coverage. As well as utilizing this approach for emerging and legacy PFAS analysis, we tested it on EPA’s non-targeted analysis collaborative trial (ENTACT) samples. The DDA strategy (three sequential injections, updated exclusion/inclusion lists) resulted in 85–100% MS2 coverage, compared to <60% with standard DDA (three replicate injections, intensity-dependent precursor selection). This demonstrates a high-coverage method without reliance on DIA strategies.

In the studies reported here for analysis of waters in North Carolina, highly sensitive targeted PFAS analysis by direct-injection LC–MS/MS was used to understand the occurrence of PFAS in drinking water sources, and an intelligent data acquisition strategy was employed to mediate the characterization of PFAS found in water samples from the Haw River and six AFFF formulations commonly used at firefighting sites.

The techniques enabled both quantitative and semiquantitative, targeted and nontargeted analysis of legacy and emerging PFAS com- pounds. The workflow provides high sensitivity for quantitative analysis of PFAS in drinking water sources through direct-injection LC–HRAM-MS, and the combination of MS and the DDA strategy offers more complete MS/MS coverage for analysis of PFAS in firefighting foam. The workflow’s excellent mass accuracy (<0.5 ppm achieved in practice), coupled with expanded HRAM-MS/MS spectral libraries, allows high-confidence compound annotation in nontargeted analysis of water samples, which in turn al- lows the accurate identification and characterization of novel, and potentially harmful, PFAS in drinking water sources.

(1) United States Environmental Protection Agency. PFOA, PFOS and Other PFAS. https://www.epa.gov/ pfas/basic-information-pfas

(2) United States Environmental Protection Agency. EPA’s Third Un- regulated Contaminant Monitoring Rule: https://www.epa.gov/dwucmr/third-unregulated-contaminant-monitoring-rule

(3) United States Environmental Protection Agency. EPA’s Drinking Water Health Advisories for PFOA and PFOS. https://www.epa.gov/ ground-water-and-drinking-water/drinking-water-health-advisories-pfoa-and-pfos

(4) United States Environmental Protection Agency. EPA’s PFAS Action Plan. https://www.epa.gov/pfas/epas-pfas-action-plan

(5) United States Environmental Protection Agency. Addition of Certain PFAS to the TRI by the National Defense Authorization Act. https://www.epa. gov/toxics-release-inventory-tri-program/addition-certain-pfas-tri-national-defense-authorization-act

(6) United States Environmental Protection Agency. EPA’s PFAS Master List of PFAS Substances. https://comptox. epa.gov/dashboard/chemical_lists/ PFASMASTER

(7) United States Environmental Protection Agency. EPA’s Fact Sheet: Draft Toxicity Assessments for GenX Chemicals and PFBS. https://www.epa. gov/sites/production/files/2018-11/ documents/factsheet_pfbs-genx-tox- icity_values_11.14.2018.pdf

(8) United States Environmental Protection Agency. Method 537.1: Determination of selected per- and polyfluorinated alkyl substances in drinking water by solid phase extraction and liquid chromatography/ tandem mass spectrometry (LC/ MS/MS). https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=343042&Lab=NERL

 is an Associate Professor of Civil and Environmental Engineering at Duke University in Durham, North Carolina. 

 is a Product Manager at Thermo Fisher Scientific in Wroclaw, Poland, and 

 is a Product Specialist at Thermo Fisher Scientific in Bremen, Germany. Direct correspondence to: maciej.bromirski@thermofisher.com

Per- and poly-fluoroalkyl substances (PFAS) are a family of potentially thousands of synthetic compounds that have long been used in the manufacture of a variety of common products with stain-repellent and nonstick properties. Their signature strong fluorine and carbon bonds make them difficult to break down and, as a result, they are among the most persistent of today’s environmental pollutants. Alarmingly, PFAS can be found in drinking water and have been shown to accumulate in the body with the potential to cause multiple health problems, such as hormone disruption and cancer. Advances in mass spectrometry have facilitated the detection of known PFAS contaminants as well as the identification of poorly studied and novel compounds in watersheds. This article explores the detection of known and novel PFAS contaminants in aqueous film-forming foams and raw drinking water sources in North Carolina, using new advances in mass spectrometry and data acquisition to improve identification and quantitation.

Monitoring for Per- and Poly-Fluoroalkyl (PFAS) with Advanced Mass Spectrometry– Based Methods

Contamination of food and agricultural products by several types of toxigenic molds (fungi) is a severe and often overlooked problem. Regardless of years of study, scientists still have not discovered an effective way to resolve this challenging issue (1). Some of the fungal species like penicillium, alternaria, aspergillus, and fusarium can grow quickly on food products and food ingredients under favorable conditions and produce mycotoxins, which are harmful for humans and animals (2). The word 

 is resulting from two words: “mukes,” denoting to “fungi” (Greek), and “toxicum,” denoted to “poison” (Latin). Mycotoxins are generally larger molecules that are significantly volatile (3).

Significant losses of harvested crops are due to chemical factors (residues of fungicides, pesticides, and herbicides), microbial factors (fungi and bacteria), and biological factors (different storage pests). These three factors are also responsible for discoloration, off-flavors, and declined nutritional value (4). The Food and Agricultural Organization of the United Nations stated that currently more than 25% of food production all over the world is affected by the fungal problem. Besides damaging the food products by discoloration, fungi can also release toxins and change the flavor of food, resulting in the food products acquiring a bad taste and odor (5).

Fungi are single-celled organisms that are heterotrophic. Nearly 7,000 fungal species are formally classified, but it is estimated that about 150,000 species exist (6,7). Fungi mostly colonize and produce mycotoxins during the 

 phases (during transport, storage, and further handling), which cause health risks for humans and animals (8,9). Even though these mycotoxins are present in very low quantities as parts per million (ppm) or parts per billion (ppb), severe or long contact with mycotoxins has been associated with liver cancer (10), immunosuppression in children (11,12) and, in some cases, chromic illness and death (13). If timely detection is possible and we are able to remove fungal-contaminated cereal or grains, we can ensure long-lasting storage, food safety, and seed quality (14). Normally, filamentous fungi (fungi imperfecti) that are modified to the terrestrial environment are generally known as 

. These fungi colonize and utilize solid substrates by penetrating deeply into their matrices with the help of enzymes to break down complex matrices into simple ones. In the maximum number of cases, the colonizing fungi create low molecular-weight compounds (with set poisonous properties), usually stated as “mycotoxins or secondary metabolites,” which are unsafe and have adverse health effects on human and animal growth and survival (15).

Human exposure to mycotoxin usually occurs by inhalation, ingestion, and dermal contact, and is mostly occurring unintentionally. In most cases, the problem starts in animals and human beings by eating infected food. Human interaction can be through cereals, fruits, and vegetables and indirectly through different animal produce such as meat, eggs, milk, and butter (16). Maximum numbers of mycotoxins are comparatively heat stable during the conventional food preparation temperature range (80–121 °C); therefore, very little (or none) degradation of mycotoxins occurs in the usual cooking circumstances, such as frying, heating, boiling, or pasteurizing (16). The contamination due to the presence of aflatoxin in food products is a serious problem all over the world. Many studies focused on aflatoxin contamination in food products have reported alarming statistics in different countries, particularly in tropical and subtropical regions like Asia and Africa (16).

Nondestructive analytical methods for the detection of fungal infections and mycotoxin contamination have become more popular and used regularly (17). The modern imaging acquisition and processing techniques have potential for the evaluation of agricultural and food products by nondestructive techniques (18). Such techniques include thermal imaging (19), X-ray microcomputed tomography (20), and the electronic nose (21), Fourier transform infrared photoacoustic spectroscopy (FT-IR PAS) (22), color imaging (23), hyper-spectral imaging (24), near-infrared (NIR) spectroscopy (25), and neutron tomography (26). These techniques have been studied for their detection of fungal and mycotoxin contamination in food products.

We describe in this article the current improvements in nondestructive measurements of fungal infections and mycotoxin contamination along with food safety testing in different food products like cereals, pulses, fruits, and vegetables.

There are different types of aflatoxins present in food. Aflatoxin B1 is a familiar human carcinogen, and B1 is also considered a Group 1 carcinogen. The World Health Organization (WHO) also reported liver cancer or hepatocellular carcinoma (HCC), which is considered the third leading cause of death in the world (27). Therefore, the International Agency for Research on Cancer (IARC) has categorized aflatoxin B1 as a carcinogen for humans and animals years ago (28). Aflatoxin B1 causes contamination in agricultural products; as a result, many countries have instituted very strict limits for aflatoxin B1 in food and agricultural products. The European Commission (EU) has defined the maximum limits of aflatoxin B1 contamination in grains, beans, ground nuts, corn, cereals, spices, and rice as 5 mg/kg (29).

Citrinin is widely produced by penicillium (30) and causes nephrotoxic and hepatotoxic effects in different animal species. Citrinin had a role in the Bakin endemic nephropathy that was recorded and also reported inhibiting the protein, DNA, and RNA synthesis in porcine kidneys at the concentration level of 0.01 mM (31). Therefore, toxicologically, it is linked with a destructive synergistic effect, like DNA, damaging in renal cells (32). Citrinin also causes health and environmental issues in different types of agricultural products (33).

Ochratoxin A is considered one of the most powerful mycotoxins that may be found in human food and in animal feed. It is also responsible for major contamination in staple foods and different types of commodities. Humans are directly or indirectly affected by ochratoxin A; it enters the food chain by contaminating the ingredients of food products consumed by humans. It also contaminates animal feed ingredients (34). Studies show that ochratoxin A is mainly caused by nephrotoxicity, genotoxicity, immunotoxicity, and neurotoxicity in the different type of animals (16).

Fumonisins cause different problems in the human body. Fumonisin B1 stimulates the proliferation of regular human esophageal epithelial cells, increasing the protein appearance of cyclin D1 and reducing cyclin E, p21, and p27 (35). The most common present fumonisin analogs is fumonisin B1; it is categorized as a 2B cancer-causing agent for humans since it is harmful to them (27,36). Studies show that FB1, FB2, and FB3 are major contamination-causing fumonisins in cereals (16). Therefore, fumonisins have been found to be toxic to kidneys and liver; they have also been shown in vivo to be nephracarcinogenic in male rats and hepracarcenogenic in male rats and female mice (37).

Trichothecenes consist of more than 200 compounds, which are further classified into four subgroups (Types A, B, C, and D) based on their different functional groups. Therefore, a group of B has a ketone at position C-8, and the group of A has a ketone other than the position of C-8, group C has a second proxy group at C-7, C-8, C-9, or C-10, and group D has a macrocyclic rig between C4 and C5 (16).

Trichothecene affects ribosomal protein synthesis, which also causes a disturbance in the synthesis process of DNA and RNA (36–39). Trichothecene is also responsible for apoptosis and cytotoxicity (16). It is causing toxicity in animals, resulting in decreased appetite, so animal decreased intake of feed (anorexia) and exhibit vomiting (emesis) resulting in the reduction of animal growth and causing adverse effects on the heart, spleen, thymus, liver, and kidneys (40).

Detection of Fungal and Mycotoxins Contamination in Cereals

Fungal infection is a major cause of reduced product quality and market value of the cereal products all over the world. Penicillium, fusarium, and aspergillus are the main species of fungi that cause contamination problems in cereals and grains. Therefore, if the fungal problem is not identified during the early contamination levels, some species produce harmful mycotoxins (41). Major cereals of the world are rice, barley, wheat, maize, millet, oat, sorghum, and rye. A major problem related to cereals is that fungal infections belong to the genera fusarium, alternaria, aspergillus, and penicillium (12).

Studies show that different fungal problems in cereals, producing toxins like fusarium in wheat grains, is the cause of deoxynivalenol (DON), a prominent toxin (42). The contamination caused due to the presence of DON results in nausea, vomiting, and acute illness (43). The aflatoxin varieties B1, B2, G1, and G3, are well-known major causes of disease. Aflatoxin B1 contains the highest risk for causing toxicity and can also cause hepatitis in human beings (28). Aflatoxins act as nephrotoxins and also cause teratogenesis, and is found in many food products, such as wheat, rice, and maize (44). To reduce the health issues and economic losses, it is compulsory to monitor fungal problems properly and regularly during storage. On hard surfaces, fungal growth can easily be removed, but the different types of mycotoxins that grow in grains are not removed during most different processing steps. Therefore, timely recognition of fungal infections in cereals and grains is useful for controlling this problem (45).

Detection of Fungal and Mycotoxins Contamination in Fruits

Early detection of fungal problems in citrus fruits is useful in packing lines to minimize economic losses and protect food quality. Studies show that ultraviolet illumination is used to identify and remove rotten affected fruits manually. Early detection of rotten fruits is possible by the use of a hyperspectral imaging system, noting the rottenness problem caused by penicillium, is detectable by hyperspectral imaging in a closed area (46). It is problematic to detect different fungal ailments that lay in or on a small number of fruits; therefore, these fungal problems spread to adjacent objects such as healthy strawberry fruits in a storage container. The result is that you suffer losses in low quality and less revenue in the market (47). For example, a strawberry fruit, which has a short post-harvest lifespan, lasts less than five days due to physiological disorders, dryness, infections, and mechanical injuries produced by a wide range of bacteria, fungi, and viruses (48, 49). Apple and unpasteurized apple juice can be contaminated with 

, which is considered a well-known problem related to food safety (45). Fusarium, rhizopus, and alternaria rot cause a major problem in tomato fruits at different levels. Fusarium rot is more damaging for ripe and red tomatoes than green ones (50,51).

Detection of Fungal and Mycotoxins Contamination in Vegetables

Leafy vegetables can also get contaminated, bringing about health-related concerns. Leafy vegetables are eaten fresh as a salad and can also be cooked. Studies show that 

 on packaged spinach was evaluated by using hyperspectroscopic technique applied over the spectral range of 400 to 1,000 nm (45). Deterioration of vegetables is mostly caused by different types of fungal infections, such as 

 (52,53). Damage of vegetables not only affects their sensory properties but also involves the risk of spreading infections to a complete growing patch and therefore leads to economic losses at a high level (46). Therefore, vegetables having defects that are not obvious are not detected and may seriously influence the vegetable quality and market value. Defective vegetables that can be easily identified are able to be removed by using nondestructive imaging techniques (54).

Detection of Fungal and Mycotoxins Contamination in Meats and Nuts

The meat industry also requires a nondestructive method for precisely detecting microorganisms that cause spoilage in meat products. FT-IR techniques are commonly used in the meat processing industry to determine biochemical changes occurring due to microbial spoilage of fresh beef placed at room temperature for 24 hours for study purpose (55). During storage, FT-IR images were taken after every hour and resulted in analysis of total viable counts (TVC) of the sample (56).

The different type of mycotoxins also pose a serious threat for the nuts during production, transportation, different processing practices, and even during storage. During the manual sorting of nuts, we cannot detect infectious and deteriorated nuts inside the shells. Studies show that we detect the inner quality of nuts without deshelling them by the use of NIR spectroscopy with the wavelength range of 2,200 to 2,500 nm (57). During the evaluation of pistachio nuts against the aflatoxin contamination and kernel necrotic spots, an X-ray line scanning system was successfully implemented (58). Ochratoxins (A, B, and C) are metabolites normally formed by the 

 (59). Ochratoxin A is a strong nephrotoxin and is often present in dry nuts (60,61).

Detection of Fungal and Mycotoxins Contamination in Seafood

Different types of seafood such as “sashimi” or “sushi” can be easily infected by pathogenic microorganisms and may be involved in foodborne diseases. Fillets of marine fish crustaceans, mollusks, different types of fish like roe, or other seafood products are mostly affected. 

 is primarily present in seafood whereas salmonella and staphylococcus can enter these food products during food processing, inappropriate handling practices, and poor storage conditions (62).

Detection of Fungal and Mycotoxins Contamination in Spices and Medicinal Herbs

A survey conducted in Spain showed that different types of mycotoxins, aflatoxin, zearalenone, ochratoxin A, fumonisin, and citrinin were found in 84 different types of medicinal herbs (63). A survey conducted in South Korea evaluated spices and treated spice foods against aflatoxin infection and found that aflatoxin B1 was found in 13.6% of spices and can be responsible for harmful spice contamination (64). Different type of mycotoxins like aflatoxin, ochratoxin, and fumonisins have been found in ready-to-eat products that were collected from different food markets and food centers (65–67). A survey was conducted in Jordan, collecting milk and meat from local markets and examining samples for aflatoxins. The survey showed that aflatoxins G1, G2, B1, and B2 were found in the meat samples (68).

Therefore, different traditional methods are available for the detection of mycotoxin and fungal contaminations using standard chemical analysis (69), the colony counting method (70), adenosine triphosphate (ATP) bioluminescence, polymerase chain reaction (PCR) method (71), and enzyme-linked immunosorbent assay (ELISA) (72), biosensors (73) and chromatographic methods (74). In these methods, more time is required for sample preparation and sometimes leads to sample destruction—so the need to develop rapid, simple, and accurate methods for the determination of mycotoxin infections and mycotoxin contamination is essential. Nondestructive techniques for the detection of fungal and mycotoxin infections need to become more popular and rapidly deployed (17).

Nondestructive Techniques for Detection of Fungal and Mycotoxin Infections

Near-Infrared (NIR) Reflectance Spectroscopy

NIR spectroscopy is a nondestructive and rapid method now widely used for recognition of fungal and mycotoxin infection in food products. The electromagnetic spectrum in the range of 780 to 2500 nm (12,500 to 4000 cm

) is normally used for NIR spectroscopy (Figure 1) (75). This technique also detects the components that have small concentrations. Many food industry researchers use NIR spectroscopy for measuring adulteration in food products due to its safety and the reliability of the data (76). NIR provides much more data associated with the structure by vibration behavior of combination bonds. The NIR region of the electromagnetic spectrum comprises the response of the molecular bonds O–H, C–H, C–O, and N–H. These bonds are associated with vibrational energy, which is measured using NIR frequencies (75).

NIR spectroscopy in the spectral range of 950 to 1650 nm is generally used for the determination of fungal infections or mycotoxin contamination in rice samples. Original and preprocessed absorbance spectra are used with partial least squares regression (PLS-R). The statistical calibration model provides prediction results with a correlation coefficient (

) of 0.668, and a standard error of prediction (SEP) of 28.87%, with a bias of -0.10% (77). NIR spectra vary substantially for the different food products; so differences occur between the measured and predicted composition of varying food products (78). The extraction information from the large data sets and the complexity of the spectra are the main concerns with this nondestructive practice (79).

NIR spectroscopy techniques are also used for the estimation of fusarium head blight and identification of DON in wheat grains if the level is greater than 60 ppm of DON (80). NIR spectroscopy is also used for the detection of fumonisins in single maize seeds affected with Fusarium verticillioides. Maize seeds having a fumonisins level greater than 100 ppm were ranked as positive, whereas seeds with less than 10 ppm were ranked as negative (81). It is important to point out that a different types of mycotoxins being detected using NIR spectroscopy have not been visibly recognized. As a result, it is important that care must be taken when we develop calibration or classification models to measure or identify mycotoxins ppm levels, as several aspects contribute to the NIR method accuracy and sensitivity mentioned in Table I (82).

Compounds identified by the retention time evaluation of unknowns with those of standards and by standards adding or spiking (83). So, 

 are referred to standards; therefore, values of standards were calculated by external methods, using calibration curves of the peak area of compound versus concentration. The K1 value is obtained by the following equation 1 (84).

Fourier-Transform Infrared Mid-Infrared Spectroscopy (FT-IR MIRS)

FT-IR is a nondestructive technique widely used in the world that measures the total infrared energy absorbed by the studied sample as seen in Figure 2 (90). FT-IR is an easily useable technique with little interference due to surface light scattering—it allows examination at sample depths from micrometers to 100 mm (91). Thus, the main benefit of FT-IR is the depth profiling ability for the nondestructive assessment of food materials (92). FT-IR sampling techniques are normally used for the examination of porous or powdered samples, catalysis, solids, and surface species (93,94). Since FT-IR–photoacoustic spectroscopy (FT-IR-PAS) applications for the food products are defined, it is commonly used in microorganism detection (95), food characterization (96), analysis of potato chips (92), analysis of coffee (97), and pea seeds analysis, mentioned in Table II (98).

In mid-infrared spectroscopy (MIRS), the spectral region is extended from 4,000 cm

(2.5–25 μm), and it provides the compositional, chemical, and structural information on the constituent’s molecules for solid, liquid, and gas phase samples. As a result, MIRS has a wide application range in many fields (99,100), such as biotechnology (101,102), environmental analysis (103,104), material sciences (105), and medicine (106,107).

MIRS is an advantageous method for identifying microbial contamination and is also very effective at identifying mycotoxins and fungal infections in different agricultural products (108,109). MIRS shows better results in the reflectance technique for the identification of contaminated kernels (110), which is due to the high sensitivity of the MIR technique (111). MIRS has information from vibrations of all functional groups present in the food sample, especially C–O and C=O and not solely O–H, NH, and C–H. Thus, MIRS is considered better for this application than near-infrared spectroscopy (NIR) (112). Changes in the lipids, proteins, and carbohydrate contents can be easily identified and interpreted at the proper regions in the spectrum using MIRS (113).

Fourier transform infrared (FT-IR) spectroscopy is also a widely used technique in the food industry because it provides information at the molecular level and is also a rapid technique that does not require intensive sample preparation. This technique is recognized as being very useful for the study of different types of materials like fruit cuticles (114), leaves (115), wood (116), and antioxidants of herbs, grains, and fruits (117). FT-IR microscopy monitors the changes during the maturation of fruits like olive fruit to gain an understanding of the processes taking place and how they may be monitored using FT-IR (118). FT-IR has the potential to identify the maize kernels infected with 

 were analyzed, the resultant spectrum is entirely different from the spectrum of uninfected corn seeds (22).

The relationship between the FT-IR absorbance spectra and analyte concentration is defined by Beer–Lambert’s law (119), in equation 2.

Fungal infections and mycotoxin contamination in food products pose a major threat to the world population. Mycotoxins contaminate approximately 25% of the world’s food products and cause severe health problems through the utilization of affected food products. The major mycotoxins in different foods are aflatoxins, ochratoxins, fumonisins, zearalenone, trichothecenes, and deoxynivalenol. Today, various conventional and nondestructive techniques are available for the detection of mycotoxins across multiple food products. Conventional methods are time-consuming, require chemical reagents, and include many laborious steps. Therefore, nondestructive techniques like near-infrared (NIR) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, hyperspectral imaging, and the electronic nose are a priority for online detection of fungal and mycotoxin problems in different food products. In this article, we discuss recent improvements and utilization of different nondestructive techniques for the early detection of fungal and mycotoxin infections in various food products.

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