1932

Abstract

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-food-032818-121233
2019-03-25
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/food/10/1/annurev-food-032818-121233.html?itemId=/content/journals/10.1146/annurev-food-032818-121233&mimeType=html&fmt=ahah

Literature Cited

  1. Abad-Fuentes A, Ceballos-Alcantarilla E, Mercader JV, Agulló C, Abad-Somovilla A, Esteve-Turrillas FA 2015. Determination of succinate-dehydrogenase-inhibitor fungicide residues in fruits and vegetables by liquid chromatography–tandem mass spectrometry. Anal. Bioanal. Chem. 407:4207–11
    [Google Scholar]
  2. Abbas O, Zadravec M, Baeten V, Mikuš T, Lešić T et al. 2018. Analytical methods used for the authentication of food of animal origin. Food Chem 246:6–17
    [Google Scholar]
  3. Acunha T, Ibáñez C, Garcia-Cañas V, Simó C, Cifuentes A 2016. Recent advances in the application of capillary electromigration methods for food analysis and foodomics. Electrophoresis 37:111–41
    [Google Scholar]
  4. Albero B, Sánchez-Brunete C, Miguel E, Tadeo JL 2017. Application of matrix solid-phase dispersion followed by GC-MS/MS to the analysis of emerging contaminants in vegetables. Food Chem 217:660–67
    [Google Scholar]
  5. Baduel C, Mueller JF, Tsai H, Gomez Ramos MJ 2015. Development of sample extraction and clean-up strategies for target and non-target analysis of environmental contaminants in biological matrices. J. Chromatogr. A 1426:33–47
    [Google Scholar]
  6. Bean HD, Hill JE, Dimandja JM 2015. Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography–mass spectrometry data. J. Chromatogr. A 1394:111–17
    [Google Scholar]
  7. Becerra-Herrera M, Sánchez-Astudillo M, Beltrán R, Sayago A 2014. Determination of phenolic compounds in olive oil: new method based on liquid–liquid micro extraction and ultra high performance liquid chromatography–triple–quadrupole mass spectrometry. LWT Food Sci. Technol. 57:49–57
    [Google Scholar]
  8. Ben Mansour A, Gargouri B, Melliou E, Magiatis P, Bouaziz M 2016. Oil quality parameters and quantitative measurement of major secoiridoid derivatives in Neb Jmel olive oil from various Tunisian origins using qNMR. J. Sci. Food Agric. 96:4432–9
    [Google Scholar]
  9. Bengtstrom L, Rosenmai AK, Trier X, Jensen LK, Granby K et al. 2016. Non-targeted screening for contaminants in paper and board food-contact materials using effect-directed analysis and accurate mass spectrometry. Food Addit. Contam. Part A 33:1080–93
    [Google Scholar]
  10. Bogialli S, Bortolini C, Di Gangi IM, Di Gregorio FN, Lucentini L et al. 2017. Liquid chromatography–high resolution mass spectrometric methods for the surveillance monitoring of cyanotoxins in freshwaters. Talanta 170:322–30
    [Google Scholar]
  11. Botros LL, Jablonski J, Chang C, Bergana MM, Wehling P et al. 2013. Exploring authentic skim and nonfat dry milk powder variance for the development of nontargeted adulterant detection methods using near-infrared spectroscopy and chemometrics. J. Agric. Food Chem. 61:9810–18
    [Google Scholar]
  12. Boxall AB, Sinclair CJ, Fenner K, Kolpin D, Maund SJ 2004. When synthetic chemicals degrade in the environment. Environ. Sci. Technol. 38:368A–75A
    [Google Scholar]
  13. Boyaci E, Rodriguez-Lafuente A, Gorynski K, Mirnaghi F, Souza-Silva EA et al. 2015. Sample preparation with solid phase microextraction and exhaustive extraction approaches: comparison for challenging cases. Anal. Chim. Acta 873:14–30
    [Google Scholar]
  14. Bueno MJM, Diaz-Galiano FJ, Rajski L, Cutillas V, Fernández-Alba AR 2018. A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops. J. Chromatogr. A 1546:66–76
    [Google Scholar]
  15. Carvalho LM, Carvalho F, de Lourdes Bastos M, Baptista P, Moreira N et al. 2014. Non-targeted and targeted analysis of wild toxic and edible mushrooms using gas chromatography–ion trap mass spectrometry. Talanta 118:292–303
    [Google Scholar]
  16. Castro-Puyana M, Herrero M 2013. Metabolomics approaches based on mass spectrometry for food safety, quality and traceability. Trends Anal. Chem. 52:74–87
    [Google Scholar]
  17. Català-Clariana S, Benavente F, Giménez E, Barbosa J, Sanz-Nebot V 2010. Identification of bioactive peptides in hypoallergenic infant milk formulas by capillary electrophoresis–mass spectrometry. Anal. Chim. Acta 683:119–25
    [Google Scholar]
  18. Celeiro M, Facorro R, Dagnac T, Llompart M 2018. Simultaneous determination of trace levels of multiclass fungicides in natural waters by solid–phase microextraction–gas chromatography-tandem mass spectrometry. Anal. Chim. Acta 1020:51–61
    [Google Scholar]
  19. Cervera MI, Portoles T, Pitarch E, Beltran J, Hernandez F 2012. Application of gas chromatography time-of-flight mass spectrometry for target and non-target analysis of pesticide residues in fruits and vegetables. J. Chromatogr. A 1244:168–77
    [Google Scholar]
  20. Cevallos-Cevallos JM, Reyes-De-Corcuera JI 2012. Metabolomics in food science. Adv. Food Nutr. Res. 67:1–24
    [Google Scholar]
  21. Charlton AJ, Robb P, Donarski JA, Godward J 2008. Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics. Anal. Chim. Acta 618:196–203
    [Google Scholar]
  22. Chem. Insp. Regul. Serv. (CIRS). 2013. The Inventory of Existing Chemical Substance in China: IECSC (2013 and updates). Chemical Inspection and Regulation Service http://www.cirs-reach.com/news-and-articles/the-inventory-of-existing-chemical-substance-in-china-iecsc-2013-and-updates.html
  23. Chen H, Jin L, Fan C, Wang W 2017. Non-targeted volatile profiles for the classification of the botanical origin of Chinese honey by solid-phase microextraction and gas chromatography-mass spectrometry combined with chemometrics. J. Sep. Sci. 40:4377–84
    [Google Scholar]
  24. Chen P, Harnly JM, Lester GE 2010. Flow injection mass spectral fingerprints demonstrate chemical differences in Rio Red grapefruit with respect to year, harvest time, and conventional versus organic farming. J. Agric. Food Chem. 58:4545–53
    [Google Scholar]
  25. Cherta L, Portoles T, Pitarch E, Beltran J, Lopez FJ et al. 2015. Analytical strategy based on the combination of gas chromatography coupled to time-of-flight and hybrid quadrupole time-of-flight mass analyzers for non-target analysis in food packaging. Food Chem 188:301–8
    [Google Scholar]
  26. Cifuentes A 2012. Food analysis: present, future, and foodomics. ISRN Anal. Chem. 2012:1–16
    [Google Scholar]
  27. Consonni R, Cagliani LR 2010. Nuclear magnetic resonance and chemometrics to assess geographical origin and quality of traditional food products. Adv. Food Nutr. Res. 59:87–165
    [Google Scholar]
  28. Contreras-Gutiérrez PK, Hurtado-Fernández E, Gómez-Romero M, Hormaza JI, Carrasco-Pancorbo A, Fernandez-Gutierrez A 2013. Determination of changes in the metabolic profile of avocado fruits (Persea americana) by two CE-MS approaches (targeted and non-targeted). Electrophoresis 34:2928–42
    [Google Scholar]
  29. Cordewener JH, Luykx DM, Frankhuizen R, Bremer MG, Hooijerink H, America AH 2009. Untargeted LC-Q-TOF mass spectrometry method for the detection of adulterations in skimmed-milk powder. J. Sep. Sci. 32:1216–23
    [Google Scholar]
  30. Cotton J, Leroux F, Broudin S, Marie M, Corman B et al. 2014. High-resolution mass spectrometry associated with data mining tools for the detection of pollutants and chemical characterization of honey samples. J. Agric. Food Chem. 62:11335–45
    [Google Scholar]
  31. Crimmins BS, Xia XY, Hopke PK, Holsen TM 2014. A targeted/non-targeted screening method for perfluoroalkyl carboxylic acids and sulfonates in whole fish using quadrupole time-of-flight mass spectrometry and MSe. Anal. Bioanal. Chem. 406:1471–80
    [Google Scholar]
  32. Croley TR, White KD, Wong J, Callahan JH, Musser SM et al. 2013. Combining targeted and nontargeted data analysis for liquid chromatography high-resolution mass spectrometric analyses. J. Sep. Sci. 36:971–79
    [Google Scholar]
  33. Dai W, Qi D, Yang T, Lv H, Guo L et al. 2015. Nontargeted analysis using ultraperformance liquid chromatography–quadrupole time-of-flight mass spectrometry uncovers the effects of harvest season on the metabolites and taste quality of tea (Camellia sinensis L.). J. Agric. Food Chem. 63:9869–78
    [Google Scholar]
  34. Djatmika R, Hsieh CC, Chen JM, Ding WH 2016. Determination of paraben preservatives in seafood using matrix solid-phase dispersion and on-line acetylation gas chromatography-mass spectrometry. J. Chromatogr. B 1036–1037:93–99
    [Google Scholar]
  35. Du L, Lu W, Cai ZJ, Bao L, Hartmann C et al. 2018. Rapid detection of milk adulteration using intact protein flow injection mass spectrometric fingerprints combined with chemometrics. Food Chem 240:573–78
    [Google Scholar]
  36. Esslinger S, Fauhl-Hassek C, Wittkowski R 2015. Authentication of wine by H-1-NMR spectroscopy: opportunities and challenges. ACS Symp. Ser. 1203:85–108
    [Google Scholar]
  37. Fan K, Zhang M 2018. Recent developments in the food quality detected by non-invasive nuclear magnetic resonance technology. Crit. Rev. Food Sci. Nutr. 16:1–12
    [Google Scholar]
  38. Filigenzi MS, Ehrke N, Aston LS, Poppenga RH 2011. Evaluation of a rapid screening method for chemical contaminants of concern in four food-related matrices using QuEChERS extraction, UHPLC and high resolution mass spectrometry. Food Addit. Contam. Part A 28:1324–39
    [Google Scholar]
  39. Franitza L, Nicolotti L, Granvogl M, Schieberle P 2018. Differentiation of rums produced from sugar cane juice (rhum agricole) from rums manufactured from sugar cane molasses by a metabolomics approach. J. Agric. Food Chem. 66:3038–45
    [Google Scholar]
  40. Fraser K, Harrison SJ, Lane GA, Otter DE, Hemar Y et al. 2012. Non-targeted analysis of tea by hydrophilic interaction liquid chromatography and high resolution mass spectrometry. Food Chem 134:1616–23
    [Google Scholar]
  41. Fu Y, Zhou Z, Kong H, Lu X, Zhao X et al. 2016. Nontargeted screening method for illegal additives based on ultrahigh-performance liquid chromatography–high-resolution mass spectrometry. Anal. Chem. 88:8870–77
    [Google Scholar]
  42. Furukawa H, Ko N, Go YB, Aratani N, Choi SB et al. 2010. Ultrahigh porosity in metal-organic frameworks. Science 329:424–28
    [Google Scholar]
  43. Garcia R, Cabrita MJ, Costa Freitas AM 2011. Application of molecularly imprinted polymers for the analysis of pesticide residues in food—a highly selective and innovative approach. Am. J. Anal. Chem. 2:16–25
    [Google Scholar]
  44. García-Cañas V, Simó C, Herrero M, Ibáñez E, Cifuentes A 2012. Present and future challenges in food analysis: foodomics. Anal. Chem. 84:10150–59
    [Google Scholar]
  45. García-García AB, Lamichhane S, Castejón D, Cambero MI, Bertram HC 2018. 1H HR-MAS NMR-based metabolomics analysis for dry-fermented sausage characterization. Food Chem 240:514–23
    [Google Scholar]
  46. García-Reyes JF, Hernando MD, Molina-Díaz A, Fernández-Alba AR 2007. Comprehensive screening of target, non-target and unknown pesticides in food by LC-TOF-MS. Trends Anal. Chem. 26:828–41
    [Google Scholar]
  47. García-Villalba R, León C, Dinelli G, Segura-Carretero A, Fernández-Gutiérrez A et al. 2008. Comparative metabolomic study of transgenic versus conventional soybean using capillary electrophoresis–time-of-flight mass spectrometry. J. Chromatogr. A 1195:164–73
    [Google Scholar]
  48. Gerhardt N, Birkenmeier M, Sanders D, Rohn S, Weller P 2017. Resolution-optimized headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) for non-targeted olive oil profiling. Anal. Bioanal. Chem. 409:3933–42
    [Google Scholar]
  49. Gilbert-López B, García-Reyes JF, Meyer C, Michels A, Franzke J et al. 2012. Simultaneous testing of multiclass organic contaminants in food and environment by liquid chromatography/dielectric barrier discharge ionization-mass spectrometry. Analyst 137:5403–10
    [Google Scholar]
  50. Godelmann R, Fang F, Humpfer E, Schutz B, Bansbach M et al. 2013. Targeted and nontargeted wine analysis by 1H NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage. J. Agric. Food Chem. 61:5610–19
    [Google Scholar]
  51. Golay PA, Giuffrida F, Dionisi F, Destaillats F 2009. Streamlined methods for the resolution and quantification of fatty acids including trans fatty acid isomers in food products by gas chromatography. J. AOAC Int. 92:1301–9
    [Google Scholar]
  52. Gosetti F, Mazzucco E, Gennaro MC, Marengo E 2015. Contaminants in water: non-target UHPLC/MS analysis. Environ. Chem. Lett. 14:51–65
    [Google Scholar]
  53. Gouilleux B, Marchand J, Charrier B, Remaud GS, Giraudeau P 2018. High-throughput authentication of edible oils with benchtop ultrafast 2D NMR. Food Chem 244:153–58
    [Google Scholar]
  54. Graziosi R, Bertelli D, Marchetti L, Papotti G, Rossi MC, Plessi M 2017. Novel 2D-NMR approach for the classification of balsamic vinegars of modena. J. Agric. Food Chem. 65:5421–26
    [Google Scholar]
  55. Grimalt S, Pozo OJ, Sancho JV, Hernandez F 2007. Use of liquid chromatography coupled to quadrupole time-of-flight mass spectrometry to investigate pesticide residues in fruits. Anal. Chem. 79:2833–43
    [Google Scholar]
  56. Gullberg J, Jonsson P, Nordstrom A, Sjostrom M, Moritz T 2004. Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal. Biochem. 331:283–95
    [Google Scholar]
  57. Hakme E, Lozano A, Gomez-Ramos MM, Hernando MD, Fernandez-Alba AR 2017. Non-target evaluation of contaminants in honey bees and pollen samples by gas chromatography time-of-flight mass spectrometry. Chemosphere 184:1310–19
    [Google Scholar]
  58. Han L, Zhang YM, Song JJ, Fan MJ, Yu YJ et al. 2018. Automatic untargeted metabolic profiling analysis coupled with chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability. J. Chromatogr. A 1541:12–20
    [Google Scholar]
  59. Han Y, Zou N, Song L, Li Y, Qin Y et al. 2015. Simultaneous determination of 70 pesticide residues in leek, leaf lettuce and garland chrysanthemum using modified QuEChERS method with multi-walled carbon nanotubes as reversed-dispersive solid-phase extraction materials. J. Chromatogr. B 1005:56–64
    [Google Scholar]
  60. Hao H, Cui N, Wang G, Xiang B, Liang Y et al. 2008. Global detection and identification of nontarget components from herbal preparations by liquid chromatography hybrid ion trap time-of-flight mass spectrometry and a strategy. Anal. Chem. 80:8187–94
    [Google Scholar]
  61. Hayward DG, Wong JW, Zhang K, Chang J, Shi F et al. 2011. Multiresidue pesticide analysis in ginseng and spinach by nontargeted and targeted screening procedures. J. AOAC Int. 94:1741–51
    [Google Scholar]
  62. Hemmler D, Heinzmann SS, Wohr K, Schmitt-Kopplin P, Witting M 2018. Tandem HILIC-RP liquid chromatography for increased polarity coverage in food analysis. Electrophoresis 39:1645–53
    [Google Scholar]
  63. Hernández F, Portolés T, Pitarch E, López FJ 2007. Target and nontarget screening of organic micropollutants in water by solid-phase microextraction combined with gas chromatography high resolution time-of-flight mass spectrometry. Anal. Chem. 79:9494–504
    [Google Scholar]
  64. Herrera-Lopez S, Hernando MD, García-Calvo E, Fernández-Alba AR, Ulaszewska MM 2014. Simultaneous screening of targeted and non-targeted contaminants using an LC-QTOF-MS system and automated MS/MS library searching. J. Mass Spectrom. 49:878–93
    [Google Scholar]
  65. Herrero M, Simó C, García-Cañas V, Ibáñez E, Cifuentes A 2012. Foodomics: MS-based strategies in modern food science and nutrition. Mass Spectrom. Rev. 31:49–69
    [Google Scholar]
  66. Hong YS 2011. NMR-based metabolomics in wine science. Magn. Reson. Chem. 49:Suppl. 1S13–21
    [Google Scholar]
  67. Hu CX, Xu GW 2013. Mass-spectrometry-based metabolomics analysis for foodomics. Trends Anal. Chem. 52:36–46
    [Google Scholar]
  68. Hu X, Wang C, Li J, Luo R, Liu C et al. 2018. Metal-organic framework-derived hollow carbon nanocubes for fast solid-phase microextraction of polycyclic aromatic hydrocarbons. ACS Appl. Mater. Interfaces 10:15051–57
    [Google Scholar]
  69. Hurtado-Fernández E, Pacchiarotta T, Mayboroda OA, Fernández-Gutiérrez A, Carrasco-Pancorbo A 2015. Metabolomic analysis of avocado fruits by GC-APCI-TOF MS: effects of ripening degrees and fruit varieties. Anal. Bioanal. Chem. 407:547–55
    [Google Scholar]
  70. Ibáñez C, Acunha T, Valdes A, García-Cañas V, Cifuentes A, Simó C 2016. Capillary electrophoresis in food and foodomics. Methods Mol. Biol. 1483:471–507
    [Google Scholar]
  71. Ibáñez C, Simó C, García-Cañas V, Acunha T, Cifuentes A 2015. The role of direct high-resolution mass spectrometry in foodomics. Anal. Bioanal. Chem. 407:6275–87
    [Google Scholar]
  72. Ibáñez M, Sancho JV, Hernández F, McMillan D, Rao R 2008. Rapid non-target screening of organic pollutants in water by ultraperformance liquid chromatography coupled to time-of-flight mass spectrometry. Trends Anal. Chem. 27:481–89
    [Google Scholar]
  73. Inoue K, Tanada C, Hosoya T, Yoshida S, Akiba T et al. 2016. Principal component analysis of molecularly based signals from infant formula contaminations using LC-MS and NMR in foodomics. J. Sci. Food Agric. 96:3876–81
    [Google Scholar]
  74. Iwasa K, Setoyama D, Shimizu H, Seta H, Fujimura Y et al. 2015. Identification of 3-methylbutanoyl glycosides in green Coffea arabica beans as causative determinants for the quality of coffee flavors. J. Agric. Food Chem. 63:3742–51
    [Google Scholar]
  75. Jackson LS 2009. Chemical food safety issues in the United States: past, present, and future. J. Agric. Food Chem. 57:8161–70
    [Google Scholar]
  76. James SL 2003. Metal-organic frameworks. Chem. Soc. Rev. 32:276–88
    [Google Scholar]
  77. Jeong J, Zhang X, Shi X, Kim S, Shen C 2013. An efficient post-hoc integration method improving peak alignment of metabolomics data from GC×GC TOF-MS. BMC Bioinform 14:123–33
    [Google Scholar]
  78. Ji J, Zhu P, Pi F, Sun C, Jiang H et al. 2016. GC-TOF/MS-based metabolomic strategy for combined toxicity effects of deoxynivalenol and zearalenone on murine macrophage ANA-1 cells. Toxicon 120:175–84
    [Google Scholar]
  79. Jia W, Chu X, Chang J, Wang PG, Chen Y, Zhang F 2017. High-throughput untargeted screening of veterinary drug residues and metabolites in tilapia using high resolution Orbitrap mass spectrometry. Anal. Chim. Acta 957:29–39
    [Google Scholar]
  80. Jiang W, Qiu Y, Ni Y, Su M, Jia W, Du X 2010. An automated data analysis pipeline for GC-TOF-MS metabonomics studies. J. Proteome Res. 9:5974–81
    [Google Scholar]
  81. Jiao Z, Zhang S, Chen H 2015. Determination of tetracycline antibiotics in fatty food samples by selective pressurized liquid extraction coupled with high-performance liquid chromatography and tandem mass spectrometry. J. Sep. Sci. 38:115–20
    [Google Scholar]
  82. Johanningsmeier SD, Harris GK, Klevorn CM 2016. Metabolomic technologies for improving the quality of food: practice and promise. Annu. Rev. Food Sci. Technol. 7:413–38
    [Google Scholar]
  83. Johnson CH, Ivanisevic J, Siuzdak G 2016. Metabolomics beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17:451–59
    [Google Scholar]
  84. Johnson YS 2012. Determination of polycyclic aromatic hydrocarbons in edible seafood by QuEChERS-based extraction and gas chromatography–tandem mass spectrometry. J. Food Sci. 77:T131–37
    [Google Scholar]
  85. Kalogiouri NP, Aalizadeh R, Thomaidis NS 2018. Application of an advanced and wide scope non-target screening workflow with LC-ESI-QTOF-MS and chemometrics for the classification of the Greek olive oil varieties. Food Chem 256:53–61
    [Google Scholar]
  86. Kalogiouri NP, Alygizakis NA, Aalizadeh R, Thomaidis NS 2016. Olive oil authenticity studies by target and nontarget LC-QTOF-MS combined with advanced chemometric techniques. Anal. Bioanal. Chem. 408:7955–70
    [Google Scholar]
  87. Karunathilaka SR, Kia AF, Srigley C, Chung JK, Mossoba MM 2016. Nontargeted, rapid screening of extra virgin olive oil products for authenticity using near-infrared spectroscopy in combination with conformity index and multivariate statistical analyses. J. Food Sci. 81:C2390–97
    [Google Scholar]
  88. Kaserzon SL, Heffernan AL, Thompson K, Mueller JF, Gomez Ramos MJ 2017. Rapid screening and identification of chemical hazards in surface and drinking water using high resolution mass spectrometry and a case-control filter. Chemosphere 182:656–64
    [Google Scholar]
  89. Khan Z, Kamble N, Bhongale A, Girme M, Bahadur Chauhan V, Banerjee K 2018. Analysis of pesticide residues in tuber crops using pressurised liquid extraction and gas chromatography-tandem mass spectrometry. Food Chem 241:250–57
    [Google Scholar]
  90. Klockmann S, Reiner E, Bachmann R, Hackl T, Fischer M 2016. Food fingerprinting: metabolomic approaches for geographical origin discrimination of hazelnuts (Corylus avellana) by UPLC-QTOF-MS. J. Agric. Food Chem. 64:9253–62
    [Google Scholar]
  91. Knolhoff AM, Croley TR 2016. Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry. J. Chromatogr. A 1428:86–96
    [Google Scholar]
  92. Knolhoff AM, Zweigenbaum JA, Croley TR 2016. Nontargeted screening of food matrices: development of a chemometric software strategy to identify unknowns in liquid chromatography–mass spectrometry data. Anal. Chem. 88:3617–23
    [Google Scholar]
  93. Kopperi M, Riekkola ML 2016. Non-targeted evaluation of selectivity of water-compatible class selective adsorbents for the analysis of steroids in wastewater. Anal. Chim. Acta 920:47–53
    [Google Scholar]
  94. Krauss M, Singer H, Hollender J 2010. LC-high resolution MS in environmental analysis: from target screening to the identification of unknowns. Anal. Bioanal. Chem. 397:943–51
    [Google Scholar]
  95. Kunzelmann M, Winter M, Aberg M, Hellenas KE, Rosen J 2018. Non-targeted analysis of unexpected food contaminants using LC-HRMS. Anal. Bioanal. Chem. 410:5593–602
    [Google Scholar]
  96. Kwon H, Lehotay SJ, Geis-Asteggiante L 2012. Variability of matrix effects in liquid and gas chromatography–mass spectrometry analysis of pesticide residues after QuEChERS sample preparation of different food crops. J. Chromatogr. A 1270:235–45
    [Google Scholar]
  97. Lachenmeier DW, Humpfer E, Fang F, Schutz B, Dvortsak P et al. 2009. NMR-spectroscopy for nontargeted screening and simultaneous quantification of health-relevant compounds in foods: the example of melamine. J. Agric. Food Chem. 57:7194–99
    [Google Scholar]
  98. Lawal A, Wong RCS, Tan GH, Abdulra'uf LB, Alsharif AMA 2018. Recent modifications and validation of QuEChERS-dSPE coupled to LC-MS and GC-MS instruments for determination of pesticide/agrochemical residues in fruits and vegetables: review. J. Chromatogr. Sci. 56:656–69
    [Google Scholar]
  99. Lee SM, Kwon GY, Kim KO, Kim YS 2011. Metabolomic approach for determination of key volatile compounds related to beef flavor in glutathione–Maillard reaction products. Anal. Chim. Acta 703:204–11
    [Google Scholar]
  100. Lehotay SJ, Son KA, Kwon H, Koesukwiwat U, Fu W et al. 2010. Comparison of QuEChERS sample preparation methods for the analysis of pesticide residues in fruits and vegetables. J. Chromatogr. A 1217:2548–60
    [Google Scholar]
  101. Lei H, Guo J, Lv Z, Zhu X, Xue X et al. 2018. Simultaneous determination of nitroimidazoles and quinolones in honey by modified QuEChERS and LC-MS/MS analysis. Int. J. Anal. Chem. 2018:4271385
    [Google Scholar]
  102. Li CF, Ma JQ, Huang DJ, Ma CL, Jin JQ et al. 2018. Comprehensive dissection of metabolic changes in Albino and Green Tea cultivars. J. Agric. Food Chem. 66:2040–48
    [Google Scholar]
  103. Li JX, Li XY, Chang QY, Li Y, Jin LH et al. 2018. Screening of 439 pesticide residues in fruits and vegetables by gas chromatography–quadrupole-time-of-flight mass spectrometry based on TOF accurate mass database and Q-TOF spectrum library. J. AOAC Int. 101:1631–38
    [Google Scholar]
  104. Li N, Zhang L, Nian L, Cao B, Wang Z et al. 2015. Dispersive micro-solid-phase extraction of herbicides in vegetable oil with metal-organic framework MIL-101. J. Agric. Food Chem. 63:2154–61
    [Google Scholar]
  105. Li XJ, Zhang Q, Zhang AL, Gao JM 2012. Metabolites from Aspergillus fumigatus, an endophytic fungus associated with Melia azedarach, and their antifungal, antifeedant, and toxic activities. J. Agric. Food Chem. 60:3424–31
    [Google Scholar]
  106. Liao W, Lu X 2016. Determination of chemical hazards in foods using surface-enhanced Raman spectroscopy coupled with advanced separation techniques. Trends Food Sci. Technol. 54:103–13
    [Google Scholar]
  107. Lin S, Gan N, Qiao L, Zhang J, Cao Y, Chen Y 2015. Magnetic metal-organic frameworks coated stir bar sorptive extraction coupled with GC-MS for determination of polychlorinated biphenyls in fish samples. Talanta 144:1139–45
    [Google Scholar]
  108. Liu CS, Sun CX, Tian JY, Wang ZW, Ji HF et al. 2017. Highly stable aluminum-based metal-organic frameworks as biosensing platforms for assessment of food safety. Biosens. Bioelectron. 91:804–10
    [Google Scholar]
  109. Lommen A, van der Weg G, van Engelen MC, Bor G, Hoogenboom LA, Nielen MW 2007. An untargeted metabolomics approach to contaminant analysis: pinpointing potential unknown compounds. Anal. Chim. Acta 584:43–49
    [Google Scholar]
  110. Longobardi F, Ventrella A, Bianco A, Catucci L, Cafagna I et al. 2013. Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries. Food Chem 141:3028–33
    [Google Scholar]
  111. Lou Q, Ma C, Wen W, Zhou J, Chen L et al. 2011. Profiling and association mapping of grain metabolites in a subset of the core collection of Chinese rice germplasm (Oryza sativa L.). J. Agric. Food Chem. 59:9257–64
    [Google Scholar]
  112. Maier NM, Buttinger G, Welhartizki S, Gavioli E, Lindner W 2004. Molecularly imprinted polymer-assisted sample clean-up of ochratoxin A from red wine: merits and limitations. J. Chromatogr. B 804:103–11
    [Google Scholar]
  113. Mateos-Vivas M, Domínguez-Álvarez J, Rodríguez-Gonzalo E, Carabias-Martínez R 2017. Capillary electrophoresis coupled to mass spectrometry employing hexafluoro-2-propanol for the determination of nucleosides and nucleotide mono-, di- and tri-phosphates in baby foods. Food Chem 233:38–44
    [Google Scholar]
  114. Maulidiani M, Mediani A, Abas F, Park YS, Park YK et al. 2018. 1H NMR and antioxidant profiles of polar and non-polar extracts of persimmon (Diospyros kaki L.): metabolomics study based on cultivars and origins. Talanta 184:277–86
    [Google Scholar]
  115. McEachran AD, Sobus JR, Williams AJ 2017. Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard. Anal. Bioanal. Chem. 409:1729–35
    [Google Scholar]
  116. Merchak N, Rizk T, Silvestre V, Remaud GS, Bejjani J, Akoka S 2018. Olive oil characterization and classification by 13C NMR with a polarization transfer technique: a comparison with gas chromatography and 1H NMR. Food Chem 245:717–23
    [Google Scholar]
  117. Milman BL, Zhurkovich IK 2017. The chemical space for non-target analysis. Trends Anal. Chem. 97:179–87
    [Google Scholar]
  118. Mol HG, Van Dam RC, Zomer P, Mulder PP 2011. Screening of plant toxins in food, feed and botanicals using full-scan high-resolution (Orbitrap) mass spectrometry. Food Addit. Contam. Part A 28:1405–23
    [Google Scholar]
  119. Monakhova YB, Kuballa T, Lachenmeier DW 2012. Nontargeted NMR analysis to rapidly detect hazardous substances in alcoholic beverages. Appl. Magn. Reson. 42:343–52
    [Google Scholar]
  120. Morales ML, Callejon RM, Ordonez JL, Troncoso AM, Garcia-Parrilla MC 2017. Comparative assessment of software for non-targeted data analysis in the study of volatile fingerprint changes during storage of a strawberry beverage. J. Chromatogr. A 1522:70–77
    [Google Scholar]
  121. Myers OD, Sumner SJ, Li S, Barnes S, Du X 2017. Detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Anal. Chem. 89:8689–95
    [Google Scholar]
  122. Newton SR, McMahen RL, Sobus JR, Mansouri K, Williams AJ et al. 2018. Suspect screening and non-targeted analysis of drinking water using point-of-use filters. Environ. Pollut. 234:297–306
    [Google Scholar]
  123. Novotna H, Kmiecik O, Galazka M, Krtkova V, Hurajova A et al. 2012. Metabolomic fingerprinting employing DART-TOFMS for authentication of tomatoes and peppers from organic and conventional farming. Food Addit. Contam. Part A 29:1335–46
    [Google Scholar]
  124. Nurenberg G, Schulz M, Kunkel U, Ternes TA 2015. Development and validation of a generic nontarget method based on liquid chromatography – high resolution mass spectrometry analysis for the evaluation of different wastewater treatment options. J. Chromatogr. A 1426:77–90
    [Google Scholar]
  125. Ortiz X, Korenkova E, Jobst KJ, MacPherson KA, Reiner EJ 2017. A high throughput targeted and non-targeted method for the analysis of microcystins and anatoxin-A using on-line solid phase extraction coupled to liquid chromatography–quadrupole time-of-flight high resolution mass spectrometry. Anal. Bioanal. Chem. 409:4959–69
    [Google Scholar]
  126. Pang GF, Fan CL, Chang QY, Li JX, Kang J, Lu ML 2018. Screening of 485 pesticide residues in fruits and vegetables by liquid chromatography-quadrupole-time-of-flight mass spectrometry based on TOF accurate mass database and QTOF spectrum library. J. AOAC Int. 101:1156–82
    [Google Scholar]
  127. Pignitter M, Stolze K, Jirsa F, Gille L, Goodman BA, Somoza V 2015. Effect of copper on fatty acid profiles in non- and semifermented teas analyzed by LC-MS-based nontargeted screening. J. Agric. Food Chem. 63:8519–26
    [Google Scholar]
  128. Portoles T, Mol JG, Sancho JV, Lopez FJ, Hernandez F 2014. Validation of a qualitative screening method for pesticides in fruits and vegetables by gas chromatography quadrupole–time of flight mass spectrometry with atmospheric pressure chemical ionization. Anal. Chim. Acta 838:76–85
    [Google Scholar]
  129. Quell JD, Romisch-Margl W, Colombo M, Krumsiek J, Evans AM et al. 2017. Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies. J. Chromatogr. B 1071:58–67
    [Google Scholar]
  130. Rajski Ł, del Mar Gómez-Ramos M, Fernández-Alba AR 2014. Large pesticide multiresidue screening method by liquid chromatography–Orbitrap mass spectrometry in full scan mode applied to fruit and vegetables. J. Chromatogr. A 1360:119–27
    [Google Scholar]
  131. Raterink R-J, Lindenburg PW, Vreeken RJ, Ramautar R, Hankemeier T 2014. Recent developments in sample-pretreatment techniques for mass spectrometry–based metabolomics. Trends Anal. Chem. 61:157–67
    [Google Scholar]
  132. Ren JW, Ledwaba M, Musyoka NM, Langmi HW, Mathe M et al. 2017. Structural defects in metal–organic frameworks (MOFs): formation, detection and control towards practices of interests. Coord. Chem. Rev. 349:169–97
    [Google Scholar]
  133. Rizzetti TM, Kemmerich M, Martins ML, Prestes OD, Adaime MB, Zanella R 2016. Optimization of a QuEChERS based method by means of central composite design for pesticide multiresidue determination in orange juice by UHPLC-MS/MS. Food Chem 196:25–33
    [Google Scholar]
  134. Robbat A Jr, Kfoury N, Baydakov E, Gankin Y 2017. Optimizing targeted/untargeted metabolomics by automating gas chromatography/mass spectrometry workflows. J. Chromatogr. A 1505:96–105
    [Google Scholar]
  135. Rodriguez Robledo V, Smyth WF 2009. The application of CE-MS in the trace analysis of environmental pollutants and food contaminants. Electrophoresis 30:1647–60
    [Google Scholar]
  136. Römisch-Margl W, Prehn C, Bogumil R, Röhring C, Suhre K, Adamski J 2011. Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics 8:133–42
    [Google Scholar]
  137. Rongai D, Sabatini N, Del Coco L, Perri E, Del Re P et al. 2017. 1H NMR and multivariate analysis for geographic characterization of commercial extra virgin olive oil: a possible correlation with climate data. Foods 6:e96
    [Google Scholar]
  138. Ruan C, Zhao X, Liu C 2015. Determination of diflubenzuron and chlorbenzuron in fruits by combining acetonitrile-based extraction with dispersive liquid–liquid microextraction followed by high-performance liquid chromatography. J. Sep. Sci. 38:2931–37
    [Google Scholar]
  139. Sato M, Miyagi A, Yoneyama S, Gisusi S, Tokuji Y, Kawai-Yamada M 2017. CE-MS-based metabolomics reveals the metabolic profile of maitake mushroom (Grifola frondosa) strains with different cultivation characteristics. Biosci. Biotechnol. Biochem. 81:2314–22
    [Google Scholar]
  140. Schauer N, Semel Y, Roessner U, Gur A, Balbo I et al. 2006. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat. Biotechnol. 24:447–54
    [Google Scholar]
  141. Senyuva HZ, Gokmen V, Sarikaya EA 2015. Future perspectives in Orbitrap-high-resolution mass spectrometry in food analysis: a review. Food Addit. Contam. Part A 32:1568–606
    [Google Scholar]
  142. Shen G, Fan X, Yang Z, Han L 2016. A feasibility study of non-targeted adulterant screening based on NIRM spectral library of soybean meal to guarantee quality: the example of non-protein nitrogen. Food Chem 210:35–42
    [Google Scholar]
  143. Shepherd LV, Fraser P, Stewart D 2011. Metabolomics: a second-generation platform for crop and food analysis. Bioanalysis 3:1143–59
    [Google Scholar]
  144. Shi XR, Chen XL, Hao YL, Li L, Xu HJ, Wang MM 2018. Magnetic metal-organic frameworks for fast and efficient solid-phase extraction of six Sudan dyes in tomato sauce. J. Chromatogr. B 1086:146–52
    [Google Scholar]
  145. Simó C, Cifuentes A, Kasicka V 2013. Capillary electrophoresis–mass spectrometry for peptide analysis: target-based approaches and proteomics/peptidomics strategies. Methods Mol. Biol. 984:139–51
    [Google Scholar]
  146. Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G 2006. XCMS processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78:779–87
    [Google Scholar]
  147. Soares MV, Alves Filho EG, Silva LM, Novotny EH, Canuto KM et al. 2017. Tracking thermal degradation on passion fruit juice through nuclear magnetic resonance and chemometrics. Food Chem 219:1–6
    [Google Scholar]
  148. Stachniuk A, Szmagara A, Czeczko R, Fornal E 2017. LC-MS/MS determination of pesticide residues in fruits and vegetables. J. Environ. Sci. Health B 52:446–57
    [Google Scholar]
  149. Stahnke H, Kittlaus S, Kempe G, Alder L 2012. Reduction of matrix effects in liquid chromatography-electrospray ionization–mass spectrometry by dilution of the sample extracts: How much dilution is needed. Anal. Chem. 84:1474–82
    [Google Scholar]
  150. Steger J, Arnhard K, Haslacher S, Geiger K, Singer K et al. 2016. Successful adaption of a forensic toxicological screening workflow employing nontargeted liquid chromatography-tandem mass spectrometry to water analysis. Electrophoresis 37:1085–94
    [Google Scholar]
  151. Straczynski G, Ligor T 2018. Comprehensive gas chromatography: food and metabolomocs applications. Crit. Rev. Anal. Chem. 48:176–85
    [Google Scholar]
  152. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G 2012. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84:5035–39
    [Google Scholar]
  153. Tengstrand E, Rosen J, Hellenas KE, Aberg KM 2013. A concept study on non-targeted screening for chemical contaminants in food using liquid chromatography–mass spectrometry in combination with a metabolomics approach. Anal. Bioanal. Chem. 405:1237–43
    [Google Scholar]
  154. Titaley IA, Ogba OM, Chibwe L, Hoh E, Cheong PH, Simonich SLM 2018. Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples. J. Chromatogr. A 1541:57–62
    [Google Scholar]
  155. Tomita S, Nemoto T, Matsuo Y, Shoji T, Tanaka F et al. 2015. A NMR-based, non-targeted multistep metabolic profiling revealed l-rhamnitol as a metabolite that characterised apples from different geographic origins. Food Chem 174:163–72
    [Google Scholar]
  156. Trienekens J, Zuurbier P 2008. Quality and safety standards in the food industry, developments and challenges. Int. J. Prod. Econ. 113:107–22
    [Google Scholar]
  157. Vaclavik L, Lacina O, Hajslova J, Zweigenbaum J 2011. The use of high performance liquid chromatography–quadrupole time-of-flight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety. Anal. Chim. Acta 685:45–51
    [Google Scholar]
  158. Vaclavik L, Schreiber A, Lacina O, Cajka T, Hajslova J 2012. Liquid chromatography–mass spectrometry-based metabolomics for authenticity assessment of fruit juices. Metabolomics 8:793–803
    [Google Scholar]
  159. Vallverdu-Queralt A, Jauregui O, Medina-Remon A, Lamuela-Raventos RM 2012. Evaluation of a method to characterize the phenolic profile of organic and conventional tomatoes. J. Agric. Food Chem. 60:3373–80
    [Google Scholar]
  160. Veenaas C, Haglund P 2017. Methodology for non-target screening of sewage sludge using comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry. Anal. Bioanal. Chem. 409:4867–83
    [Google Scholar]
  161. Vidal JLM, Plaza-Bolaños P, Romero-González R, Garrido Frenich A 2009. Determination of pesticide transformation products: a review of extraction and detection methods. J. Chromatogr. A 1216:6767–88
    [Google Scholar]
  162. Vikrant K, Tsang DCW, Raza N, Giri BS, Kukkar D, Kim KH 2018. Potential utility of metal-organic framework-based platform for sensing pesticides. ACS Appl. Mater. Interfaces 10:8797–817
    [Google Scholar]
  163. Villalón-López N, Serrano-Contreras JI, Téllez-Medina DI, Gerardo Zepeda L 2018. An 1H NMR-based metabolomic approach to compare the chemical profiling of retail samples of ground roasted and instant coffees. Food Res. Int. 106:263–70
    [Google Scholar]
  164. Wang B 2013. A two-stage peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry–based metabolomics. Comput. Struct. Biotechnol. J. 7:e201304002
    [Google Scholar]
  165. Wang J, Leung D, Chow W, Chang J, Wong JW 2018. Target screening of 105 veterinary drug residues in milk using UHPLC/ESI Q-Orbitrap multiplexing data independent acquisition. Anal. Bioanal. Chem. 410:5373–89
    [Google Scholar]
  166. Wang X, Ye N 2017. Recent advances in metal-organic frameworks and covalent organic frameworks for sample preparation and chromatographic analysis. Electrophoresis 38:3059–78
    [Google Scholar]
  167. Wang Z, Jablonski JE 2016. Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics. Food Addit. Contam. Part A 33:560–73
    [Google Scholar]
  168. Wu Y, Yu W, Yang B, Li P 2018. Self-assembled two-dimensional gold nanoparticle film for sensitive nontargeted analysis of food additives with surface-enhanced Raman spectroscopy. Analyst 143:2363–68
    [Google Scholar]
  169. Wu YN, Chen Y 2013. Food safety in China. J. Epidemiol. Commun. Health 67:478–80
    [Google Scholar]
  170. Xiong L, Yan P, Chu M, Gao YQ, Li WH, Yang XL 2018. A rapid and simple HPLC-FLD screening method with QuEChERS as the sample treatment for the simultaneous monitoring of nine bisphenols in milk. Food Chem 244:371–77
    [Google Scholar]
  171. Xu B, Li P, Ma F, Wang X, Matthaus B et al. 2015. Detection of virgin coconut oil adulteration with animal fats using quantitative cholesterol by GC × GC-TOF/MS analysis. Food Chem 178:128–35
    [Google Scholar]
  172. Xu C-H, Chen G-S, Xiong Z-H, Fan Y-X, Wang X-C, Liu Y 2016. Applications of solid-phase microextraction in food analysis. Trends Anal. Chem. 80:12–29
    [Google Scholar]
  173. Yoshida H, Mizukoshi T, Hirayama K, Miyano H 2007. Comprehensive analytical method for the determination of hydrophilic metabolites by high-performance liquid chromatography and mass spectrometry. J. Agric. Food Chem. 55:551–60
    [Google Scholar]
  174. Zendong Z, McCarron P, Herrenknecht C, Sibat M, Amzil Z et al. 2015. High resolution mass spectrometry for quantitative analysis and untargeted screening of algal toxins in mussels and passive samplers. J. Chromatogr. A 1416:10–21
    [Google Scholar]
  175. Zhang F, Yu C, Wang W, Fan R, Zhang Z, Guo Y 2012. Rapid simultaneous screening and identification of multiple pesticide residues in vegetables. Anal. Chim. Acta 757:39–47
    [Google Scholar]
  176. Zheng G, Han C, Liu Y, Wang J, Zhu M et al. 2014. Multiresidue analysis of 30 organochlorine pesticides in milk and milk powder by gel permeation chromatography–solid phase extraction–gas chromatography–tandem mass spectrometry. J. Dairy Sci. 97:6016–26
    [Google Scholar]
  177. Zomer P, Mol HG 2015. Simultaneous quantitative determination, identification and qualitative screening of pesticides in fruits and vegetables using LC-Q-Orbitrap-MS. Food Addit. Contam. Part A 32:1628–36
    [Google Scholar]
/content/journals/10.1146/annurev-food-032818-121233
Loading
/content/journals/10.1146/annurev-food-032818-121233
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error