Food Analytical Methods

, Volume 12, Issue 10, pp 2172–2184 | Cite as

Classification of Grain Maize (Zea mays L.) from Different Geographical Origins with FTIR Spectroscopy—a Suitable Analytical Tool for Feed Authentication?

  • Elisabeth Achten
  • David Schütz
  • Markus Fischer
  • Carsten Fauhl-HassekEmail author
  • Janet Riedl
  • Bettina Horn


Feed is substantial in the production of animal food products and subject to regulations about traceability in the European Union. The geographical origin as one feature of authenticity was approached by spectroscopic techniques such as Fourier transform infrared spectroscopy. This fast and non-destructive method may be used to identify suspicious samples and initiate further investigations. The aim of the feasibility study was the development of classification models based on authentic grain maize samples from three different countries. Grain maize was used as demonstrator for unprocessed feed materials due to its wide cultivation and trade. Attenuated total reflexion Fourier transform infrared spectroscopy and multivariate analyses were applied to differentiate grain maize samples by their geographical origin (Ukraine, USA, Peru). Several sample preparations and data preprocessings were tested based on the results of a hard and a soft classification method. Model validation was performed with separate test sets and permutation tests. The use of an optimal data preprocessing resulted in 100% sensitivity for solid samples in both classification models, whereas oil extraction resulted in 100% and 70% sensitivity, respectively. These findings indicate the feasibility of FTIR spectroscopy combined with multivariate classification to verify the geographical origin of grain maize.


Infrared spectroscopy Authenticity Feed Maize 



The authors thank LAVES Stade and Oldenburg for providing samples from Ukraine, the Universidad de Trujillo in Peru for providing samples from Peru and Dr. P. Cotty from USDA Agricultural Research Service for providing samples from the USA.

Compliance with Ethical Standards

Conflict of Interest

Elisabeth Achten declares that she has no conflict of interest. David Schütz declares that he has no conflict of interest. Markus Fischer declares that he has no conflict of interest. Carsten Fauhl-Hassek declares that he has no conflict of interest. Janet Riedl declares that she has no conflict of interest. Bettina Horn declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent is not applicable in this article.

Supplementary material

12161_2019_1558_MOESM1_ESM.pdf (274 kb)
ESM 1 (PDF 273 kb)


  1. Abeysekara S, Damiran D, Yu P (2011) Spectroscopic impact on protein and carbohydrate inherent molecular structures of barley, oat and corn combined with wheat DDGS. Spectroscopy 26:255–277CrossRefGoogle Scholar
  2. Agricultural Market Information System (2018) Accessed 20 Mar 2018
  3. Arena E, Campisi S, Fallico B, Maccarone E (2007) Distribution of fatty acids and phytosterols as a criterion to discriminate geographic origin of pistachio seeds. Food Chem 104:403–408CrossRefGoogle Scholar
  4. Baeten V, Vermeulen P, Pierna JF, Dardenne P (2014) From targeted to untargeted detection of contaminants and foreign bodies in food and feed using NIR spectroscopy. New Food 17:16–23Google Scholar
  5. Ballabio D, Consonni V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods 5:3790–3798CrossRefGoogle Scholar
  6. Bevilacqua M, Bucci R, Magrì AD, Magrì AL, Nescatelli R, Marini F (2013) Chapter 5 - classification and class-modelling. In: Marini F (ed) Data handling in science and technology, vol 28. Elsevier, Amsterdam, pp 171–233. Google Scholar
  7. Cheli F, Battaglia D, Pinotti L, Baldi A (2012) State of the art in feedstuff analysis: a technique-oriented perspective. J Agric Food Chem 60:9529–9542. CrossRefGoogle Scholar
  8. Cozzolino D (2014) An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals. Food Res Int 60:262–265. CrossRefGoogle Scholar
  9. Dabbene F, Gay P, Tortia C (2014) Traceability issues in food supply chain management: a review. Biosyst Eng 120:65–80. CrossRefGoogle Scholar
  10. Daszykowski M, Walczak B, Massart DL (2002) Representative subset selection. Anal Chim Acta 468:91–103. CrossRefGoogle Scholar
  11. de Jong S (1993) SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst 18:251–263. CrossRefGoogle Scholar
  12. Dijkstra A, Segers J (2007) Production and refining of oils and fats. In: The lipid handbook, pp 143–262Google Scholar
  13. Dunlap FG, White PJ, Pollak LM (1995) Fatty acid composition of oil from exotic corn breeding materials. J Am Oil Chem Soc 72:989–993. CrossRefGoogle Scholar
  14. Duodu KG, Tang H, Grant A, Wellner N, Belton PS, Taylor JRN (2001) FTIR and solid State13C NMR spectroscopy of proteins of wet cooked and popped sorghum and maize. J Cereal Sci 33:261–269. CrossRefGoogle Scholar
  15. Engel J, Gerretzen J, Szymańska E, Jansen JJ, Downey G, Blanchet L, Buydens LMC (2013) Breaking with trends in pre-processing? TrAC Trends Anal Chem 50:96–106CrossRefGoogle Scholar
  16. Esslinger S, Riedl J, Fauhl-Hassek C (2014) Potential and limitations of non-targeted fingerprinting for authentication of food in official control. Food Res Int 60:189–204CrossRefGoogle Scholar
  17. European Commission (2002) Regulation (EC) No 178/2002 of the European Parliament and of the Council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European food safety authority and laying down procedures in matters of food safety. L 31/1. Official Journal of the European Union, European Parliament and Council. 178/2002.Google Scholar
  18. European Commission (2009) Regulation (EC) No 767/2009 of the European Parliament and of the Council of 13 July 2009 on the placing on the market and use of feed, amending European Parliament and Council Regulation (EC) No 1831/2003 and repealing Council Directive 79/373/EEC, Commission Directive 80/511/EEC, Council Directives 82/471/EEC, 83/228/EEC, 93/74/EEC, 93/113/EC and 96/25/EC and Commission Decision 2004/217/EC. L 229/1. Official Journal of the European Union, European Parliament and Council. 767/2009.Google Scholar
  19. European Commission (2017) Regulation (EU) 2017/625 of the European Parliament and of the Council of 15 March 2017 on official controls and other official activities performed to ensure the application of food and feed law, rules on animal health and welfare, plant health and plant protection products, amending Regulations (EC) No 999/2001, (EC) No 396/2005, (EC) No 1069/2009, (EC) No 1107/2009, (EU) No 1151/2012, (EU) No 652/2014, (EU) 2016/429 and (EU) 2016/2031 of the European Parliament and of the Council, Council Regulations (EC) No 1/2005 and (EC) No 1099/2009 and Council Directives 98/58/EC, 1999/74/EC, 2007/43/EC, 2008/119/EC and 2008/120/EC, and repealing Regulations (EC) No 854/2004 and (EC) No 882/2004 of the European Parliament and of the Council, Council Directives 89/608/EEC, 89/662/EEC, 90/425/EEC, 91/496/EEC, 96/23/EC, 96/93/EC and 97/78/ EC and Council Decision 92/438/EEC (Official Controls Regulation) vol 2017/625. European Parliament and Council, Official Journal of the European UnionGoogle Scholar
  20. Forato LA, Bernardes-Filho R, Colnago LA (1998) Protein Structure in KBr Pellets by Infrared Spectroscopy. Anal Biochem 259:136–141. CrossRefGoogle Scholar
  21. Guillén MD, Cabo N (1997) Infrared spectroscopy in the study of edible oils and fats. J Sci Food Agric 75:1–11.<1::AID-JSFA842>3.0.CO;2-R CrossRefGoogle Scholar
  22. Gurdeniz G, Ozen B, Tokatli F (2008) Classification of Turkish olive oils with respect to cultivar, geographic origin and harvest year, using fatty acid profile and mid-IR spectroscopy. Eur Food Res Technol 227:1275–1281. CrossRefGoogle Scholar
  23. Győri Z (2017) Chapter 11 - corn: grain-quality characteristics and management of quality requirements A2 - Wrigley, Colin. In: Batey I, Miskelly D (eds) Cereal Grains, 2nd edn. Woodhead Publishing, Sawston, pp 257–290. CrossRefGoogle Scholar
  24. Horn B, Esslinger S, Pfister M, Fauhl-Hassek C, Riedl J (2018) Non-targeted detection of paprika adulteration using mid-infrared spectroscopy and one-class classification – is it data preprocessing that makes the performance? Food Chem 257:112–119. CrossRefGoogle Scholar
  25. Ismail A, Van de Voort F, Emo G, Sedman J (1993) Rapid quantitative determination of free fatty acids in fats and oils by Fourier transform infrared spectroscopy. J Am Oil Chem Soc 70:335–341CrossRefGoogle Scholar
  26. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148CrossRefGoogle Scholar
  27. Kim Y, Himmelsbach DS, Kays SE (2007) ATR-Fourier transform mid-infrared spectroscopy for determination of trans fatty acids in ground cereal products without oil extraction. J Agric Food Chem 55:4327–4333CrossRefGoogle Scholar
  28. Kuhnen S et al (2010) ATR-FTIR spectroscopy and chemometric analysis applied to discrimination of landrace maize flours produced in southern Brazil. Int J Food Sci Technol 45:1673–1681. CrossRefGoogle Scholar
  29. Kumar N, Bansal A, Sarma GS, Rawal RK (2014) Chemometrics tools used in analytical chemistry: an overview. Talanta 123:186–199CrossRefGoogle Scholar
  30. Lee K-M, Herrman TJ, Bisrat Y, Murray SC (2014) Feasibility of surface-enhanced Raman Spectroscopy for rapid detection of aflatoxins in maize. J Agric Food Chem 62:4466–4474. CrossRefGoogle Scholar
  31. Lee LC, Liong CY, Jemain AA (2017) A contemporary review on Data Preprocessing (DP) practice strategy in ATR-FTIR spectrum. Chemom Intell Lab Syst 163:64–75. CrossRefGoogle Scholar
  32. Lee LC, Liong C-Y, Jemain AA (2018) Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps. Analyst 143(15):3526–3539CrossRefGoogle Scholar
  33. Leibovitz Z, Ruckenstein C (1983) Our experiences in processing maize (corn) germ oil. Fette, Seifen, Anstrichm 85:538–543. CrossRefGoogle Scholar
  34. Lohumi S, Lee S, Lee H, Cho B-K (2015) A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends Food Sci Technol 46:85–98. CrossRefGoogle Scholar
  35. Manetti C et al (2004) NMR-based metabonomic study of transgenic maize. Phytochemistry 65:3187–3198. CrossRefGoogle Scholar
  36. Martins AR, Talhavini M, Vieira ML, Zacca JJ, Braga JWB (2017) Discrimination of whisky brands and counterfeit identification by UV–Vis spectroscopy and multivariate data analysis. Food Chem 229:142–151. CrossRefGoogle Scholar
  37. Morrison WR (1988) Lipids in cereal starches: a review. J Cereal Sci 8:1–15. CrossRefGoogle Scholar
  38. Morrison WR, Milligan TP, Azudin MN (1984) A relationship between the amylose and lipid contents of starches from diploid cereals. J Cereal Sci 2:257–271. CrossRefGoogle Scholar
  39. Movasaghi Z, Rehman S, Rehman I (2008) Fourier transform infrared (FTIR) spectroscopy of biological tissues. Appl Spectrosc Rev 43:134–179. CrossRefGoogle Scholar
  40. Murray I (1996) Value of traditional analytical methods and near-infrared (NIR) spectroscopy to the feed industry. Recent advances in animal nutrition. P. Garnsworthy, J. Wiseman and W. Haresign. Nottingham, University Press: 87-110Google Scholar
  41. Naumann A, Heine G, Rauber R (2010) Efficient discrimination of oat and pea roots by cluster analysis of Fourier transform infrared (FTIR) spectra. Field Crops Res 119:78–84. CrossRefGoogle Scholar
  42. Nietner T, Pfister M, Glomb MA, Fauhl-Hassek C (2013) Authentication of the botanical and geographical origin of distillers dried grains and solubles (DDGS) by FT-IR spectroscopy. J Agric Food Chem 61:7225–7233CrossRefGoogle Scholar
  43. Nietner T, Lahrssen-Wiederholt M, Fauhl-Hassek C (2017) Authentizitätsprüfung von Futtermitteln - ein Thema von zunehmender Bedeutung? Lebensmittelchemie 71:33–56CrossRefGoogle Scholar
  44. Office of Global Analysis (2018) Grain: World Markets and Trade. Foreign Agricultural Service/United States Department of Agriculture. Accessed 24.07.2018 2018
  45. Parcerisa J et al (1993) Influence of variety and geographical origin on the lipid fraction of hazelnuts (Corylus avellana L.) from Spain: I. Fatty acid composition. Food Chem 48:411–414. CrossRefGoogle Scholar
  46. Rohman A, Che Man YB, Yusof FM (2013) The use of FTIR spectroscopy and chemometrics for rapid authentication of extra virgin olive oil. J Am Oil Chem Soc 91:207–213. CrossRefGoogle Scholar
  47. Setyaningrum D, Riyanto S, Rohman A (2013) Analysis of corn and soybean oils in red fruit oil using FTIR spectroscopy in combination with partial least square. Int Food Res J 20:1977–1981Google Scholar
  48. Socrates G (2004) Infrared and Raman characteristic group frequencies: tables and charts. Wiley, HobokenGoogle Scholar
  49. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437. CrossRefGoogle Scholar
  50. Su W-H, He H-J, Sun D-W (2017) Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a review. Critical Reviews in Food Science and Nutrition 57:1039–1051. CrossRefGoogle Scholar
  51. Thomas EV (2003) Non-parametric statistical methods for multivariate calibration model selection and comparison. J Chemom.: A Journal of the Chemometrics Society 17:653–659CrossRefGoogle Scholar
  52. Tres A, van der Veer G, Perez-Marin MD, van Ruth SM, Garrido-Varo A (2012) Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study. J Agric Food Chem 60:8129–8133. CrossRefGoogle Scholar
  53. van der Voet H (1994) Comparing the predictive accuracy of models using a simple randomization test. Chemom Intell Lab Syst 25:313–323CrossRefGoogle Scholar
  54. Vermeulen P et al (2015a) Origin authentication of distillers’ dried grains and solubles (DDGS)--application and comparison of different analytical strategies. Anal Bioanal Chem 407:6447–6461. CrossRefGoogle Scholar
  55. Vermeulen PH, Pierna JAF, Abbas O, Dardenne P, Baeten V (2015b) Origin identification of dried distillers grains with solubles using attenuated total reflection Fourier transform mid-infrared spectroscopy after in situ oil extraction. Food Chem 189:19–26CrossRefGoogle Scholar
  56. White PJ, Johnson LA (2003) Corn: chemistry and technology. eds. vol 633.15 WHI. CIMMYTGoogle Scholar
  57. Yao H, Hruska Z, Kincaid R, Brown RL, Bhatnagar D, Cleveland TE (2013) Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosyst Eng 115:125–135. CrossRefGoogle Scholar
  58. Zhou X, Yang Z, Haughey SA, Galvin-King P, Han L, Elliott CT (2015) Classification the geographical origin of corn distillers dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: a feasibility study. Food Chem 189:13–18. CrossRefGoogle Scholar
  59. Zhu L et al (2018) Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy. J Cereal Sci 82:175-182Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Elisabeth Achten
    • 1
  • David Schütz
    • 2
  • Markus Fischer
    • 2
  • Carsten Fauhl-Hassek
    • 1
    Email author
  • Janet Riedl
    • 1
  • Bettina Horn
    • 1
  1. 1.German Federal Institute for Risk Assessment (BfR), Department Safety in the Food ChainBerlinGermany
  2. 2.Hamburg School of Food Science, Institute of Food ChemistryUniversity of HamburgHamburgGermany

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