Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps

Xingfan ZHOU, Zengling YANG, Longjian CHEN, Lujia HAN

PDF(1283 KB)
PDF(1283 KB)
Front. Agr. Sci. Eng. ›› 2016, Vol. 3 ›› Issue (2) : 171-179. DOI: 10.15302/J-FASE-2016095
RESEARCH ARTICLE
RESEARCH ARTICLE

Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps

Author information +
History +

Abstract

Amino acids are the dominant organic components of processed animal proteins, however there has been limited investigation of differences in their composition between various protein sources. Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods. In this study, self-organizing feature maps (SOFM) were used to visualize amino acid composition of fish meal, and meat and bone meal (MBM) produced from poultry, ruminants and swine. SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency. Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine, lysine and proline. However, the amino acid composition of the three MBMs was quite similar. The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward. SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining.

Keywords

self-organizing feature maps / visualization / processed animal proteins (PAPs) / amino acid

Cite this article

Download citation ▾
Xingfan ZHOU, Zengling YANG, Longjian CHEN, Lujia HAN. Visualization of amino acid composition differences between processed protein from different animal species by self-organizing feature maps. Front. Agr. Sci. Eng., 2016, 3(2): 171‒179 https://doi.org/10.15302/J-FASE-2016095

References

[1]
Muir W I, Lynch G W, Williamson P, Cowieson A J. The oral administration of meat and bone meal-derived protein fractions improved the performance of young broiler chicks. Animal Production Science, 2013, 53(5): 369–377
CrossRef Google scholar
[2]
Liu X, Han L, Veys P, Baeten V, Jiang X, Dardenne P. An overview of the legislation and light microscopy for detection of processed animal proteins in feeds. Microscopy Research and Technique, 2011, 74(8): 735–743
CrossRef Google scholar
[3]
European Commission. Regulation (EC) No 1774/2002 of the european parliament and of the council of 3 october 2002 laying down health rules concerning animal by-products not intended for human consumption. Official Journal of the European Communities, 2002, 10: 1–95
[4]
European Commission. Commission regulation (EC) No 1234/2003 of 10 July 2003 amending annexes I, IV and XI to regulation (EC) No 999/2001 of the european parliament and of the council and regulation (EC) No 1326/2001 as regards transmissible spongiform encephalopathies and animal feeding. Official Journal of the European Union, 2003, 1234: 6–13
[5]
van Raamsdonk L W D, von Holst C, Baeten V, Berben G, Boix A, de Jong J. New developments in the detection and identification of processed animal proteins in feeds. Animal Feed Science and Technology, 2007, 133(1–2): 63–83
CrossRef Google scholar
[6]
Tacon A G J, Metian M. Global overview on the use of fish meal and fish oil in industrially compounded aquafeeds: trends and future prospects. Aquaculture, 2008, 285(1–4): 146–158
CrossRef Google scholar
[7]
Kirstein D D. Composition and quality of porcine meat and bone meal. In: Proceedings of Tri-State Dairy Nutrition Conference 1999 , Fort Wayne: Cite Seer, 1999, 223–242
[8]
Bellorini S, Strathmann S, Baeten V, Fumiere O, Berben G, Tirendi S, von Holst C. Discriminating animal fats and their origins: assessing the potentials of fourier transform infrared spectroscopy, gas chromatography, immunoassay and polymerase chain reaction techniques. Analytical and Bioanalytical Chemistry, 2005, 382(4): 1073–1083
CrossRef Google scholar
[9]
Buckley M, Collins M, Thomas-Oates J, Wilson J C. Species identification by analysis of bone collagen using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 2009, 23(23): 3843–3854
CrossRef Google scholar
[10]
Buckley M, Penkman K E H, Wess T J, Reaney S, Collins M J. Protein and mineral characterisation of rendered meat and bone meal. Food Chemistry, 2012, 134(3): 1267–1278
CrossRef Google scholar
[11]
Campagnoli A, Pinotti L, Tognon G, Cheli F, Baldi A, Dell'Orto V. Potential application of electronic nose in processed animal proteins (PAP) detection in feedstuffs. Biotechnologie, Agronomie, Société et Environnement, 2004, 8(4): 253–255
[12]
Fumiere O, Veys P, Boix A, von Holst C, Baeten V, Berben G. Methods of detection, species identification and quantification of processed animal proteins in feedingstuffs. Biotechnologie, Agronomie, Société et Environnement, 2009, 13: 59–70
[13]
Li X, Rezaei R, Li P, Wu G. Composition of amino acids in feed ingredients for animal diets. Amino Acids, 2011, 40(4): 1159–1168
CrossRef Google scholar
[14]
Wu G. Amino acids: metabolism, functions, and nutrition. Amino Acids, 2009, 37(1): 1–17
CrossRef Google scholar
[15]
De Runz C, Desjardin E, Herbin M. Unsupervised visual data mining using self-organizing maps and a data-driven color mapping. In: Proceedings of the 2012 16th international conference on information visualisation. Montpellier: IEEE, 2012, 241–245
CrossRef Google scholar
[16]
Melssen W, Wehrens R, Buydens L. Supervised Kohonen networks for classification problems. Chemometrics and Intelligent Laboratory Systems, 2006, 83(2): 99–113
CrossRef Google scholar
[17]
Pandey M, Pandey A K, Mishra A, Tripathi B D. Application of chemometric analysis and self organizing map-artificial neural network as source receptor modeling for metal speciation in river sediment. Environmental pollution, 2015, 204: 64–73
[18]
Shieh S L, Liao I E. A new approach for data clustering and visualization using self-organizing maps. Expert Systems with Applications, 2012, 39(15): 11924–11933
CrossRef Google scholar
[19]
Folch J, Lees M. Stanley G H S. A simple method for the isolation and purification of total lipides from animal tissues. Journal of Biological Chemistry, 1957, 226(1): 497–509
[20]
Daszykowski M, Walczak B, Massart D L. Representative subset selection. Analytica Chimica Acta, 2002, 468(1): 91–103
CrossRef Google scholar
[21]
Wold S, Esbensen K, Geladi P. Principal component analyisis. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1–3): 37–52
CrossRef Google scholar
[22]
Ballabio D, Consonni V, Todeschini R. The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for self organizing maps and counterpropagation artificial neural networks. Chemometrics and Intelligent Laboratory Systems, 2009, 98(2): 115–122
CrossRef Google scholar
[23]
Kim D H, Cho W S, Chon T S. Self-organizing map and species abundance distribution of stream benthic macroinvertebrates in revealing community patterns in different seasons. Ecological Informatics, 2013, 17: 14–29
CrossRef Google scholar
[24]
Barker M, Rayens W. Partial least squares for discrimination. Journal of Chemometrics, 2003, 17(3): 166–173
CrossRef Google scholar
[25]
Sauvant D, Perez J M, Tran G. Tables of composition and nutritional value of feed materials: pigs, poultry, cattle, sheep, goats, rabbits, horses and fish. Wageningen Academic Pub, 2004
[26]
Malomo G A, Bolu S A, Olutade S G. Effects of dietary crude protein on performance and nitrogen economy of broilers. Research Opinions in Animal and Veterinary Sciences, 2013, 3(9): 330– 334
[27]
Pérez-Marín D C, Garrido-Varo A, Guerrero-Ginel J E, Gomez-Cabrera A. Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration. Animal Feed Science and Technology, 2004, 116(3–4): 333–349
CrossRef Google scholar
[28]
Yang Z, Han L, Liu X, Li Q. Detecting and quantifying meat meal or meat and bone meal contamination in fishmeal by visible and near infrared reflectance spectra. Animal Feed Science and Technology, 2008, 147(4): 357–367
CrossRef Google scholar
[29]
Tena N, Fernández Pierna J A, Boix A, Baeten V, von Holst C. Differentiation of meat and bone meal from fishmeal by near-infrared spectroscopy: extension of scope to defatted samples. Food Control, 2014, 43: 155–162
CrossRef Google scholar

Acknowledgements

This research was supported by the International Science and Technology Cooperation Project, Ministry of Science and Technology, China (2015DFG32170).

Compliance with ethics guidelines

Xingfan Zhou, Zengling Yang, Longjian Chen, and Lujia Han declare that they have no conflict of interest or financial conflicts to disclose.
All applicable institutional and national guidelines for the care and use of animals were followed.

RIGHTS & PERMISSIONS

The Author(s) 2016. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
AI Summary AI Mindmap
PDF(1283 KB)

Accesses

Citations

Detail

Sections
Recommended

/