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Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety

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Abstract

Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen–antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.

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References

  1. Galanakis CM. Food technological disruptions. Academic, 2021.

  2. Olaimat AN, Shahbaz HM, Fatima N, Munir S, Holley RA. Food safety during and after the era of covid-19 pandemic. Front Microbiol. 2020;11:1854. https://doi.org/10.3389/fmicb.2020.01854.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Machado Nardi VA, Auler DP, Teixeira R. Food safety in global supply chains: a literature review. J Food Sci. 2020;85:883–91. https://doi.org/10.1111/1750-3841.14999.

    Article  CAS  PubMed  Google Scholar 

  4. Singh S, Kumar R, Panchal R, Tiwari MK. Impact of covid-19 on logistics systems and disruptions in food supply chain. Int J Product Res. 2020;59:1–16. https://doi.org/10.1080/00207543.2020.1792000.

    Article  CAS  Google Scholar 

  5. Mu W, van Asselt ED, van der Fels-Klerx HJ. Towards a resilient food supply chain in the context of food safety. Food Control. 2021;125:107953. https://doi.org/10.1016/j.foodcont.2021.107953. (ISSN 0956-7135).

    Article  Google Scholar 

  6. Law JW, Ab Mutalib NS, Chan KG, Lee LH. Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations. Front Microbiol. 2015;5:770. https://doi.org/10.3389/fmicb.2014.00770.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Velusamy V, Arshak K, Korostynska O, Oliwa K, Adley C. An overview of foodborne pathogen detection: in the perspective of biosensors. Biotechnol Adv. 2010;28:232–54. https://doi.org/10.1016/j.biotechadv.2009.12.00.

    Article  CAS  PubMed  Google Scholar 

  8. Lingyuan Xu, Abd El-Aty AM, Eun JB, Shim JH, Zhao J, Lei X, Gao S, She Y, Jin F, Wang J, Jin M, Hammock BD. Recent advances in rapid detection techniques for pesticide residue: a review. J Agric Food Chem. 2022;70(41):13093–117.

    Article  Google Scholar 

  9. Alder L, Greulich K, Kempe G, Vieth B. Residue analysis of 500 high priority pesticides: better by GC–MS or LC–MS/MS? Mass Spectrom Rev. 2006;25:838–65.

    Article  CAS  PubMed  Google Scholar 

  10. Fernández-Alba AR, García-Reyes JF. Large-scale multi-residue methods for pesticides and their degradation products in food by advanced LC-MS. TrAC Trends Anal Chem. 2008;27:973–90.

    Article  Google Scholar 

  11. Freitag S, Sulyok M, Logan N, Elliott CT, Krska R. The potential and applicability of infrared spectroscopic methods for the rapid screening and routine analysis of mycotoxins in food crops. Compr Rev Food Sci Food Saf. 2022;21:5199–224.

    Article  CAS  PubMed  Google Scholar 

  12. Prodhan MDH, Alam SN, Uddin MJ. Analytical methods in measuring pesticides in foods, in pesticide residue in foods. Cham: Springer International Publishing; 2017. p. 135–45.

    Book  Google Scholar 

  13. Lambropoulou DA, Albanis TA. Methods of sample preparation for determination of pesticide residues in food matrices by chromatography-mass spectrometry-based techniques: a review. Anal Bioanal Chem. 2007;389:1663.

    Article  CAS  PubMed  Google Scholar 

  14. Malviya, R, Bansal V, Pal O, Sharma P. High performance liquid chromatography: a short review. System. 2010; 85.

  15. Shrivastava A, Gupta V. Methods for the determination of limit of detection and limit of quantitation of the analytical methods. Chron Young Sci. 2011;2:21.

    Article  Google Scholar 

  16. Osselton MD, Snelling RD. Chromatographic identification of pesticides. J Chromatogr A. 1986;368:265.

    Article  CAS  Google Scholar 

  17. Van der Hoff GR, Van Zoonen P. Trace analysis of pesticides by gas chromatography. J Chromatogr A. 1999;843:301.

    Article  PubMed  Google Scholar 

  18. Masiá A, Blasco C, Picó Y. Last trends in pesticide residue determination by liquid chromatography-mass spectrometry. Trends Environ Anal Chem. 2014;2:11.

    Article  Google Scholar 

  19. Kawczak P, Baczek T, Kaliszan R. Mode of chromatographic method for analysis of pesticides. In Choice of the mode of chromatographic method for analysis of pesticides on the basis of the properties of analytes. 2016; 3: 99–114.

  20. Luxminarayan L, Neha S, Amit V, Khinchi MP. A review on chromatography techniques. Asian J Pharm Res Dev. 2017;5:1.

    Google Scholar 

  21. van Belkum A, Bachmann TT, Lüdke G, Lisby JG, Kahlmeter G, Mohess A, Becker K, Hays JP, Woodford N, Mitsakakis K, Moran-Gilad J, Vila J, Peter H, Rex JH, WmM D. Developmental roadmap for antimicrobial susceptibility testing systems. Nat Rev Microbiol. 2019;17:51–62. https://doi.org/10.1038/s41579-018-0098-9.

    Article  CAS  PubMed  Google Scholar 

  22. Vasala A, Hytönen VP, Laitinen OH. Modern tools for rapid diagnostics of antimicrobial resistance. Front Cell Infect Microbiol. 2020;10:308. https://doi.org/10.3389/fcimb.2020.00308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bec KB, Grabska J, Huck CW. Review near-infrared spectroscopy in bio-applications. Molecules. 2020;25:2948. https://doi.org/10.3390/molecules25122948.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bec KB, Huck CW. Breakthrough potential in near-infrared spectroscopy: spectra simulation. A review of recent developments. Front Chem. 2019. https://doi.org/10.3389/fchem.2019.00048.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cozzolino D. Advantages, opportunities, and challenges of vibrational spectroscopy as tool to monitor sustainable food systems. Food Anal Methods. 2022;15:1390–6. https://doi.org/10.1007/s12161-021-02207-w.

    Article  Google Scholar 

  26. Ellis DI, Muhamadali H, Haughey SA, Elliott CT, Goodacre R. Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis – moving out of the laboratory and into the food supply chain. Anal Methods. 2015;7:9401–14.

    Article  Google Scholar 

  27. Cattaneo TMP, Stellari A. Review: NIR spectroscopy as a suitable tool for the investigation of the horticultural field. Agronomy. 2019;9:503. https://doi.org/10.3390/agronomy9090503.

    Article  CAS  Google Scholar 

  28. Pasquini C. Near infrared spectroscopy: a mature analytical technique with new perspectives—a review. Anal Chim Acta. 2018;1026:8–36.

    Article  CAS  PubMed  Google Scholar 

  29. Walsh KB, McGlone VA, Hanc DH. The uses of near infra-red spectroscopy in postharvest decision support: a review. Post Biol Technol. 2020;163: 111139.

    Article  CAS  Google Scholar 

  30. Saeys W, Do Trong NN, Van Beers R, Nicolai BM. Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: a review. Post Biol Technol. 2019;158:110981.

    Article  CAS  Google Scholar 

  31. Cozzolino D, Roberts JJ. Applications and developments on the use of vibrational spectroscopy imaging for the analysis, monitoring and characterisation of crops and plants. Molecules. 2016;21:755–63.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J. Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Post Biol Technol. 2007;46:99–118.

    Article  Google Scholar 

  33. Amigo JM, Martí I, Gowen A. Hyperspectral imaging and chemometrics: a perfect combination for the analysis of food structure, composition and quality. Data Handling Sci Technol. 2013;28:343–70.

    Article  Google Scholar 

  34. Cortes V, Blasco J, Aleixos N, Cubero S, Talensa P. Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: a review. Trends Food Sci Technol. 2019;85:138–48.

    Article  CAS  Google Scholar 

  35. Sorak D, Herberholz L, Iwascek S, Altinpinar S, Pfeifer F, Siesler HW. New developments and applications of handheld Raman, mid-infrared, and near infrared spectrometers. App Spectros Rev. 2012;47:83–115.

    Article  Google Scholar 

  36. Thygesen LG, Løkke MM, Micklander E, Engelsen SB. Vibrational microspectroscopy of food. Raman vs. FT-IR. Trends Food Sci Technol. 2003;14:50–7.

    Article  CAS  Google Scholar 

  37. Gilbert S. Vibrational, Rotational and Raman Spectroscopy, Historical Perspective, Editor(s): John C. Lindon, George E. Tranter, David W. Koppenaal, Encyclopedia of Spectroscopy and Spectrometry (Third Edition), Academic Press, 2017; 600–609.

  38. Le Pevelen DD. NIR FT-Raman, Editor(s): John C. Lindon, George E. Tranter, David W. Koppenaal, Encyclopedia of Spectroscopy and Spectrometry (Third Edition), Academic Press, 2017; 98–109.

  39. Bureau S, Cozzolino D, Clark CJ. Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: a review. Post Biol Technol. 2019;148:1–14.

    Article  CAS  Google Scholar 

  40. Agelet L, Hurburgh ChH Jr. A tutorial on near infrared spectroscopy and its’ calibration. Crit Rev Anal Chem. 2010;40:246–60.

    Article  CAS  Google Scholar 

  41. Bevilacqua M, Bro R, Marini F, Rinnan A, Rasmussen MA, Skov T. Recent chemometrics advances for foodomics. Trends Anal Chem. 2017;97:42–51.

    Article  Google Scholar 

  42. Jimenez-Carvelo AM, Cuadros-Rodríguez L. Data mining/machine learning methods in foodomics. Current Opin Food Sci. 2021;37:76–82.

    Article  CAS  Google Scholar 

  43. Szymańska E, Gerretzen J, Engel J, Geurts B, Blanchet L, Buydens LM. Chemometrics and qualitative analysis have a vibrant relationship. TrAC Trends Anal Chem. 2015;69:34–51.

    Article  Google Scholar 

  44. Szymanska E. Modern data science for analytical chemical data: a comprehensive review. Anal Chim Acta. 2018;1028:1–10.

    Article  CAS  PubMed  Google Scholar 

  45. Dayananda B, Owen S, Kolobaric A, Chapman J, Cozzolino D. Pre-processing applied to instrumental data in analytical chemistry: a brief review of the methods and examples. Crit Rev Anal Chem. 2023;13:1–9. https://doi.org/10.1080/10408347.2023.2199864.

    Article  CAS  Google Scholar 

  46. Rinnan A, Van den Berg F, Engelsen SB. Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal Chem. 2009;28:1201–22.

    Article  CAS  Google Scholar 

  47. Rinnan A. Pre-processing in vibrational spectroscopy—when, why and how. Anal Methods. 2014;6:7124–9.

    Article  Google Scholar 

  48. Engel J, Gerretzen J, Szymanska E, Jansen JJ, Downey G, Blanchet L, Buydens LMC. Breaking with trends in pre-processing. Trends Anal Chem. 2013;50:96–106.

    Article  CAS  Google Scholar 

  49. Gnonlonfin GJB, Hell K, Adjovi Y, Fandohan P, Koudande DO, Mensah GA, Sanni A, Brimer L. A review on aflatoxin contamination and its implications in the developing world: a Sub-Saharan African perspective. Crit Rev Food Sci Nutr. 2013;53:349–65. https://doi.org/10.1080/10408398.2010.535718.

    Article  CAS  PubMed  Google Scholar 

  50. Bhardwaj K, Meneely JP, Haughey S, Dean M, Wall P, Zhang G, Baker B, Elliott C. Risk assessments for the dietary intake aflatoxins in food: a systematic review (2016-2022). Food Control. 2021.

  51. Matulaprungsan B, Wongs-Aree C, Penchaiya P, Maniwara P, Kanlayanarat S, Ohashi S, et al. Feasibility of determination of foodborne microbe contamination of fresh-cut shredded cabbage using SW-NIR. Agri Eng. 2019;1:246–56. https://doi.org/10.3390/agriengineering1020018.

    Article  Google Scholar 

  52. Abu-Khalaf N. Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA). Palest Tech Univ Res J. 2015;3:12–22. https://doi.org/10.53671/pturj.v3i1.35.

    Article  Google Scholar 

  53. Rahi S, Mobli H, Jamshidi B, Azizi A, Sharifi M. Visible/near-infrared spectroscopy as a novel technology for nondestructive detection of Escherichia coli ATCC 8739 in lettuce samples. 2019; 24–6. https://doi.org/10.33422/worldcet.2019.10.285

  54. Rahi S, Mobli H, Jamshidi B, Azizi A, Sharifi M. Different supervised and unsupervised classification approaches based on visible/near infrared spectral analysis for discrimination of microbial contaminated lettuce samples: case study on E. coli ATCC. Infrared Phys Technol. 2020;108:103355. https://doi.org/10.1016/j.infrared.2020.103355.

    Article  CAS  Google Scholar 

  55. Siedliska A, Baranowski P, Zubik M, Mazurek W, Sosnowska B. Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Post Biol Technol. 2018;139:115–26. https://doi.org/10.1016/j.postharvbio.2018.01.018.

    Article  CAS  Google Scholar 

  56. Liu SH, Wen BY, Lin JS, Yang ZW, Luo SY, Li JF. Rapid and quantitative detection of Aflatoxin B 1 in grain by portable raman spectrometer. Appl Spectrosc. 2020;74(1365–421):1373. https://doi.org/10.1177/0003702820951891.

    Article  CAS  Google Scholar 

  57. Kos G, Sieger M, McMullin D, Zahradnik C, Sulyok M, Öner T, Mizaikoff B, Krska R. A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits. Food Addit Contam Part A. 2016;33:1596–607. https://doi.org/10.1080/19440049.2016.1217567.

    Article  CAS  Google Scholar 

  58. Lee KM, Davis J, Herrman TJ, Murray SC, Deng Y. An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize. Food Chem. 2015;173:629–39. https://doi.org/10.1016/j.foodchem.2014.10.099.

    Article  CAS  PubMed  Google Scholar 

  59. Lee KM, Herrman TJ, Yun U. Application of Raman spectroscopy for qualitative and quantitative analysis of aflatoxins in ground maize samples. J Cereal Sci. 2014;59:70–8. https://doi.org/10.1016/j.jcs.2013.10.004.

    Article  CAS  Google Scholar 

  60. Dowell FE, Ram M, Seitz L. Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy. Cereal Chem. 1999;76(4):573–6.

    Article  CAS  Google Scholar 

  61. Dowell FE, Pearson TC, Maghirang EB, Xie F, Wicklow DT. Reflectance and transmittance spectroscopy applied to detecting fumonisin in single corn kernels infected with Fusarium verticillioides. Cereal Chem. 2002;79(2):222–6.

    Article  CAS  Google Scholar 

  62. Peiris KHS, Pumphrey M, Dong Y, Maghirang E, Berzonsky W, Dowell FE. Near-infrared spectroscopic method for identification of fusarium head blight damage and prediction of deoxynivalenol in single wheat kernels. Cereal Chem. 2010;87(6):511–7.

    Article  CAS  Google Scholar 

  63. Peiris KHS, Dong Y, Bockus WW, Dowell FE. Moisture effects on the prediction performance of a single-kernel near-infrared deoxynivalenol calibration. Cereal Chem. 2016;93(6):631–7. https://doi.org/10.1094/CCHEM-04-16-0120-R.

    Article  CAS  Google Scholar 

  64. Peiris KHS, Dong Y, Davis MA, Bockus WW, Dowell FE. Estimation of the deoxynivalenol and moisture contents of bulk wheat grain samples by FT-NIR spectroscopy. Cereal Chem. 2017;94(4):677–82.

    Article  CAS  Google Scholar 

  65. Niu C, Guo H, Wei J, Sajid M, Yuan Y, Yue T. Fourier transform near-Infrared spectroscopy and chemometrics to predict Zygosacchromyces rouxii in apple and kiwi fruit juices. J Food Prot. 2018;81:1379–85. https://doi.org/10.4315/0362-028X.JFP-17-512.

    Article  PubMed  Google Scholar 

  66. Saranwong S, Kawano S. Rapid determination of fungicide contaminated on tomato surfaces using the DESIR-NIR: a system for ppm-order concentration. J Near Infrared Spectros. 2005;13:169–75.

    Article  CAS  Google Scholar 

  67. Wu M, Sun J, Lu B, Ge X, Zhou X, Zou M. Application of deep brief network in transmission spectroscopy detection of pesticide residues in lettuce leaves. J Food Process Eng. 2019;42:13005.

    Article  Google Scholar 

  68. Sánchez MT, Pérez-Marín D, Flores-Rojas K, Guerrero JE, Garrido-Varo A. Measurement of pesticide residues in peppers by near infra-red reflectance spectroscopy. Pest Manag Sci. 2010;66:580–6.

    Article  PubMed  Google Scholar 

  69. Salguero-Chaparro L, Gaitán-Jurado AJ, Ortiz-Somovilla V, Peña-Rodríguez F. Feasibility of using NIR spectroscopy to detect herbicide residues in intact olives. Food Control. 2013;30:504–9.

    Article  CAS  Google Scholar 

  70. Moros J, Armenta S, Garrigues S, De La Guardia M. Near infrared determination of Diuron in pesticide formulations. Anal Chim Acta. 2005;543:124–9.

    Article  CAS  Google Scholar 

  71. García-Reyes JF, Ferrer C, Gómez-Ramos MJ, Molina-Díaz A, Fernández-Alba AR. Determination of pesticide residues in olive oil and olives. TrAC-Trends Anal Chem. 2007;26:239–51.

    Article  Google Scholar 

  72. Blanco M, Castillo M, Peinado A, Beneyto R. Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pre-treatments and estimation of multivariate detection limits. Anal Chim Acta. 2007;581:318–23.

    Article  CAS  PubMed  Google Scholar 

  73. Armenta S, Moros J, Garrigues S, De La Guardia M. The use of near-infrared spectrometry in the olive oil industry. Crit Rev Food Sci Nutr. 2010;50:567–82.

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Daniel Cozzolino.

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Cozzolino, D., Chapman, J. Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety. Anal Bioanal Chem 416, 611–620 (2024). https://doi.org/10.1007/s00216-023-04849-7

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