Classification of Grain Maize (Zea mays L.) from Different Geographical Origins with FTIR Spectroscopy—a Suitable Analytical Tool for Feed Authentication?
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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.
KeywordsInfrared 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.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent is not applicable in this article.
- Agricultural Market Information System (2018) http://statistics.amis-outlook.org/data/index.html. Accessed 20 Mar 2018
- 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
- 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. https://doi.org/10.1016/B978-0-444-59528-7.00005-3 Google Scholar
- Dabbene F, Gay P, Tortia C (2014) Traceability issues in food supply chain management: a review. Biosyst Eng 120:65–80. https://doi.org/10.1016/j.biosystemseng.2013.09.006 CrossRefGoogle Scholar
- Dijkstra A, Segers J (2007) Production and refining of oils and fats. In: The lipid handbook, pp 143–262Google Scholar
- 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
- 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
- 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
- Guillén MD, Cabo N (1997) Infrared spectroscopy in the study of edible oils and fats. J Sci Food Agric 75:1–11. https://doi.org/10.1002/(SICI)1097-0010(199709)75:1<1::AID-JSFA842>3.0.CO;2-R CrossRefGoogle Scholar
- 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. https://doi.org/10.1016/B978-0-08-100719-8.00011-5 CrossRefGoogle Scholar
- 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. https://doi.org/10.1016/j.foodchem.2018.03.007 CrossRefGoogle Scholar
- 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
- Office of Global Analysis (2018) Grain: World Markets and Trade. Foreign Agricultural Service/United States Department of Agriculture. https://apps.fas.usda.gov/psdonline/circulars/grain.pdf. Accessed 24.07.2018 2018
- 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
- Socrates G (2004) Infrared and Raman characteristic group frequencies: tables and charts. Wiley, HobokenGoogle Scholar
- White PJ, Johnson LA (2003) Corn: chemistry and technology. eds. vol 633.15 WHI. CIMMYTGoogle Scholar
- 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. https://doi.org/10.1016/j.biosystemseng.2013.03.006 CrossRefGoogle Scholar
- 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. https://doi.org/10.1016/j.foodchem.2014.09.104 CrossRefGoogle Scholar
- 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