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Classification of Milk Samples Using CART

  • Lucas HansenEmail author
  • Marco Flôres Ferrão
Article
  • 33 Downloads

Abstract

Classification and regression tree (CART) analyses have not been explored yet in the field of food physicochemical analysis, to the best of our knowledge. In this work, we tested its classification performance on a set of physicochemical data from raw milk samples from Southern Brazil we already analyzed via well-known supervised methods in a previous work. CART performed better than most of the previously employed methods regarding specificity, sensitivity, and accuracy when classifying samples from the training set. These findings suggest CART could also be employed to classify milk samples as compliant or not to Brazilian regulations and possibly to other countries’ regulations as well.

Keywords

Milk Multivariate analysis Physicochemical analysis CART 

Notes

Compliance with Ethical Standards

Conflict of Interest

Lucas Hansen declares that he has no conflict of interest. Marco Flôres Ferrão declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

Publication has been approved by all individual participants.

References

  1. Ashman WP, Lewis JH, Poziomek EJ (1985) Decision tree for chemical detection applications. Anal Chem 57:1951–1955.  https://doi.org/10.1021/ac00296a020 CrossRefGoogle Scholar
  2. Azad T, Ahmed S (2016) Common milk adulteration and their detection techniques. Int J Food Contam 3:22 (review).  https://doi.org/10.1186/s40550-016-0045-3 CrossRefGoogle Scholar
  3. Bae H-K, Olson BH, Hsu K-L, Sorooshian S (2010) Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area. Water Sci Technol 61(2):545–553.  https://doi.org/10.2166/wst.2010.842 CrossRefGoogle Scholar
  4. Ballabio D, Consonni V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods 5:3790–3798.  https://doi.org/10.1039/C3AY40582F CrossRefGoogle Scholar
  5. Bougrini M, Tahri K, Haddi Z, el Bari N, Llobet E, Jaffrezic-Renaul TN, Bouchikhi B (2014) Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue. Mater Sci Eng C 45:348–358.  https://doi.org/10.1016/j.msec CrossRefGoogle Scholar
  6. Brasil (2006) Ministério da Agricultura, Pecuária e Abastecimento. Instrução Normativa n° 68 de 12 de dezembro de 2006. http://extranet.agricultura.gov.br/sislegis-consulta/consultarLegislacao.do?operacao=visualizar&id=17472. Accessed 02/09/19.
  7. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, BelmontGoogle Scholar
  8. Callao MP, Ruisánchez I (2018) An overview of multivariate qualitative methods for food fraud detection. Food Control 86:283–293 (review).  https://doi.org/10.1016/j.foodcont.2017.11.034 CrossRefGoogle Scholar
  9. Cevoli C, Gori A, Nocetti M, Cuibus L, Caboni MF, Fabbri A (2013) FT-NIR and FT-MIR spectroscopy to discriminate competitors, noncompliance and compliance grated Parmigiano Reggiano cheese. Food Res Int 52:214–220.  https://doi.org/10.15835/buasvmcn-fst:10795 CrossRefGoogle Scholar
  10. Das S, Goswami B, Biswas K (2016) Milk adulteration and detection: a review. Sens Lett 14:4–18 (review).  https://doi.org/10.1166/sl.2016.3580 CrossRefGoogle Scholar
  11. De Carvalho BMA, de Carvalho LM, dos Reis JS, Coimbra LAM, de Souza EB, da Silva Júnior WB, Detmann E, de Carvalho GGP (2015) Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration. Food Chem 174:1–7.  https://doi.org/10.1016/j.foodchem.2014.11.003 CrossRefGoogle Scholar
  12. Doyle P (1973) The use of automatic interaction detector and similar search procedures. Oper Res Q 24:465–467CrossRefGoogle Scholar
  13. Eisenberg JS, McKone TE (1998) Decision tree method for the classification of chemical pollutants: incorporation of across-chemical variability and within-chemical uncertainty. Environ Sci Technol 32:3396–3404.  https://doi.org/10.1021/es970975s CrossRefGoogle Scholar
  14. Falk FR, Miller NBA (1992) Primer for soft modelling, 1st edn. University of Akron, Akron, p 80Google Scholar
  15. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7(2):179–188.  https://doi.org/10.1111/j.1469-1809.1936.tb02137.x CrossRefGoogle Scholar
  16. Fox PF, McSweeney PLH, Uniacke-Lowe T, O’Mahony JA (2015) Production and Utilization of Milk. In: Dairy chemistry and biochemistry. Springer International Publishing, Cham, p 1.  https://doi.org/10.1007/978-3-319-14892-2_1 CrossRefGoogle Scholar
  17. Garcia JS, Sanvido GB, Saraiva SA, Zacca JJ, Cosso RG, Eberlin MN (2012) Bovine milk powder adulteration with vegetable oils or fats revealed by MALDI-QTOF MS. Food Chem 131:722–726 (short communication).  https://doi.org/10.1186/s40550-016-0045-3 CrossRefGoogle Scholar
  18. Gondim C d S, Junqueira RG, Souza SVC, Ruisánchez I, Callao MP (2017a) Detection of several common adulterants in raw milk by MID-infrared spectroscopy and one-class and multi-class multivariate strategies. Food Chem 230:68–75.  https://doi.org/10.1016/j.foodchem.2017.03.022 CrossRefGoogle Scholar
  19. Gondim C, dos S, Junqueira RG, de Souza SVC, Callao MP, Ruisánchez I (2017b) Determining performance parameters in qualitative multi-variate methods using probability of detection (POD) curves. Case study: two common milk adulterants. Talanta 168:23–30.  https://doi.org/10.1186/s40550-016-0045-3 CrossRefGoogle Scholar
  20. Gori A, Maggio RM, Cerretani L, Nocetti M, Caboni MF (2012) Discrimination of grated cheeses by Fourier transform infrared spectroscopy coupled with chemometric techniques. Int Dairy J 23:115–120.  https://doi.org/10.3390/ma9020081 CrossRefGoogle Scholar
  21. Haddad K, Rahman A, Zaman M, Shrestha S (2013) Applicability of Monte Carlo cross validation technique for model development and validation using generalised least squares regression. J Hydrol (Amst) 48 2:119–128.  https://doi.org/10.1016/j.jhydrol.2012.12.041 CrossRefGoogle Scholar
  22. Hansen L, Ferrão MF (2018) Identification of possible milk adulteration using physicochemical data and multivariate analysis. Food Anal Methods 11:1–10.  https://doi.org/10.1007/s12161-018-1181-6 CrossRefGoogle Scholar
  23. India (2015) Ministry of Health and Family Welfare. Manual of methods of analysis of foods. Milk and Milk products, p 187Google Scholar
  24. ISO (1980) ISO 6092:1980. Dried milk – determination of titratable acidity (Routine method).Google Scholar
  25. ISO (2007) ISO 22662:2007. Milk and milk products – determination of lactose content by high-performance liquid chromatography (reference method)Google Scholar
  26. ISO (2008). ISO 488:2008 (IDF 105:2008). Milk – determination of fat content - Gerber butyrometersGoogle Scholar
  27. ISO (2009). ISO 5764:2009 (IDF 108:2009). Milk – determination of freezing point — thermistor cryoscope method (reference method)Google Scholar
  28. ISO (2010a) ISO 6731:2010 (IDF 21:2010). Milk, cream and evaporated milk – determination of total solids content (reference method).Google Scholar
  29. ISO (2010b) ISO 1211:2010 (IDF 1:2010). Milk – determination of fat content – gravimetric method (reference method).Google Scholar
  30. ISO (2010c) ISO 6091:2010. Dried milk – determination of titratable acidity (reference method)Google Scholar
  31. ISO (2016) ISO 8968-4:2016 (IDF 20-4). Milk and milk products – determination of nitrogen content–part 4: determination of protein and non-protein nitrogen content and true protein content calculation (reference method)Google Scholar
  32. Jaiswal P, Jha SN, Kaur J, Borah A (2017) Detection and quantification of anionic detergent (lissapol) in milk using attenuated total reflectance-Fourier transform infrared spectroscopy. Food Chem 221:815–821.  https://doi.org/10.1016/j.foodchem.2016.11.095 CrossRefGoogle Scholar
  33. Kamal M, Karoui R (2015) Analytical methods coupled with chemometric tools for determining the authenticity and detecting the adulteration of dairy products: a review. Trends Food Sci Technol 46:27–48 (review.  https://doi.org/10.1016/j.tifs.2015.07.007 CrossRefGoogle Scholar
  34. Karoui R, De Baerdemaker J (2007) A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chem 102:621–640 (review).  https://doi.org/10.1016/j.foodchem.2006.05.042 CrossRefGoogle Scholar
  35. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11(1):137–148.  https://doi.org/10.1080/00401706.1969.10490666 CrossRefGoogle Scholar
  36. Khan MK, Krishna H, Majumder SH, Gupta PK (2015) Detection of urea adulteration in milk using near-infrared Raman spectroscopy. Food Anal Methods 8:93–102.  https://doi.org/10.1007/s12161-014-9873-z CrossRefGoogle Scholar
  37. Lane JH, Eynon L (1934) Determination of reducing sugars by Fehling’s solution with methylene blue indicator. Norman Rodger, London, p 8Google Scholar
  38. Liu J (2017) Terahertz spectroscopy and chemometric tools for rapid identification of adulterated dairy product. Opt Quant Electron 49(1).  https://doi.org/10.1007/s11082-016-0848-8
  39. Loh W-Y (2008) Classification and regression tree methods. In: Ruggeri F, Kenett R, Faltin F (eds) Encyclopedia of statistics in quality and reliability. Wiley, pp 315–323Google Scholar
  40. 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 (review.  https://doi.org/10.1016/j.tifs.2015.08.003 CrossRefGoogle Scholar
  41. Mackay H (1929) The detection of milk adulteration (note). Can Med Assoc J 21(3):309Google Scholar
  42. Mendes T d O, Porto BLS, Bell MJV, Perrone IT, de Oliveira MAL (2016) Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration. Food Chem 213:647–653.  https://doi.org/10.1016/j.foodchem.2014.11.003 CrossRefGoogle Scholar
  43. Messenger RC, Mandell ML (1972) A model search technique for predictive nominal scale multivariate analysis. J Am Stat Assoc 67:768–772.  https://doi.org/10.1080/01621459.1972.10481290 Google Scholar
  44. Mironiuk M, Barańska M, Chojnacka K, Górecki H (2016) Determination of the reference value of nitrogen mass fraction in the reference material of Polish soil. Accred Qual Assur 21:409–415.  https://doi.org/10.1007/s00769-016-1240-x CrossRefGoogle Scholar
  45. Morgan JN, Sonquist JA (1963) Problems in the analysis of survey data, and a proposal. J Am Stat Assoc 58:415–435CrossRefGoogle Scholar
  46. Nedeljkovic A, Tomasevic I, Miocinovic J, Pudja P (2017) Feasibility of discrimination of dairy creams and cream-like analogues using Raman spectroscopy and chemometric analysis. Food Chem 232:487–492 (short communication).  https://doi.org/10.1016/j.foodchem.2017.03.165 CrossRefGoogle Scholar
  47. People’s Republic of China (2010a) Ministry of Health of the People’s Republic of China. GB 5413.5-2010. National food safety standard. Determination of lactose and sucrose in foods for infants and young children, milk and milk products.Google Scholar
  48. People’s Republic of China (2010b) Ministry of Health of the People’s Republic of China. GB 5009.4-2010. National food safety standard. Determination of Ash in Foods.Google Scholar
  49. People’s Republic of China (2010c) Ministry of Health of the People’s Republic of China. China GB 5413.33-2010. National food safety standard. Determination of specific gravity in raw milkGoogle Scholar
  50. People’s Republic of China (2010d) Ministry of Health of the People’s Republic of China. China GB 5009.5-2010 National Food Safety Standard Determination of protein in foodsGoogle Scholar
  51. People’s Republic of China (2010e) Ministry of Health of the People’s Republic of China. China GB 5413.34-2010 National food safety standard. Determination of acidity in milk and milk products.Google Scholar
  52. People’s Republic of China (2010f) Ministry of Health of the People’s Republic of China. China GB 5413.39—2010. National food safety standard. Determination of nonfat total milk solids in milk and milk productsGoogle Scholar
  53. Poonia A, Jha A, Sharma R, Singh HB, Rai AK, Sharma N (2017) Detection of adulteration in milk: a review. Int J Dairy Technol 70:1–19.  https://doi.org/10.1111/1471-0307.12274 CrossRefGoogle Scholar
  54. Rebechi SR, Vélez MA, Vaira S, Perotti MC (2016) Adulteration of Argentinean milk fats with animal fats: detection by fatty acids analysis and multivariate regression techniques. Food Chem 192:1025–1032.  https://doi.org/10.1016/j.foodchem.2015.07.107 CrossRefGoogle Scholar
  55. Rezende PS, Carmo GPD, Esteves EG (2015) Optimization and validation of a method for the determination of the refractive index of milk serum based on the reaction between milk and copper(II) sulfate to detect milk dilutions. Talanta 138:196–202.  https://doi.org/10.1016/j.talanta.2015.02.020 CrossRefGoogle Scholar
  56. Santos PM, Pereira-Filho ER, Colnago LA (2013a) Detection and quantification of milk adulteration using time domain nuclear magnetic (TD-NMR). Microchem J 124:15–19.  https://doi.org/10.1016/j.microc.2015.07.013 CrossRefGoogle Scholar
  57. Santos PM, Pereira-Filho ER, Rodriguez-Saona LE (2013b) Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food Chem 636(138):1 19–1 24.  https://doi.org/10.1016/j.foodchem.2012.10.024 Google Scholar
  58. Scholl PF, Bergana MM, Yakes BJ, Xie Z, Zbylut S, Downey G, Mossoba M, Jablonski J, Magaletta R, Holroyd SE, Buehler M, Qin J, Hurst W, LaPointe JH, Roberts D, Zrybko C, Mackey A, Holton JD, Israelson GA, Payne A, Kim MS, Chao K, Moore JC (2017) Effects of the adulteration technique on the near-infrareddetection of melamine in milk powder. J Agric Food Chem 65(28):5799–5809.  https://doi.org/10.1021/acs.jafc.7b02083 CrossRefGoogle Scholar
  59. Tomaszewska-Gras J (2016) Rapid quantitative determination of butter adulteration with palm oil using the DSC technique. Food Control 60:629–635.  https://doi.org/10.1016/j.foodcont.2015.09.001 CrossRefGoogle Scholar
  60. Trbović D, Petronijević R, Đorđević V (2017) Chromatography methods and chemometrics for determination of milk fat adulterants. IOP Conf Ser: Earth Environ Sci 85:012025.  https://doi.org/10.1088/1755-1315/85/1/012025 CrossRefGoogle Scholar
  61. Vacchina V, Séby F, Chekri R, Verdeil J, Dumont J, Hulin M, Sirot V, Volatier JL, Serreau R, Rousseau A, Simon T, Guérin T (2017) Optimization and validation of the methods for the total mercury and methylmercury determination in breast milk. Talanta 167:404–410.  https://doi.org/10.1016/j.talanta.2017.02.046 CrossRefGoogle Scholar
  62. Velioglu SD, Elioglu E, Boyaci IH (2017) Rapid discrimination between buffalo and cow milk and detection ofadulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods. J Dairy Res 84:2214–2219.  https://doi.org/10.1017/S0022029917000073 Google Scholar
  63. Wojciechowski KL, Melilli C, Barbano DM (2016) A proficiency test system to improve performance of milk analysis methods and produce reference values for component calibration samples for infrared milk analysis. J Dairy Sci 99:6808–6827.  https://doi.org/10.3168/jds.2016-10936 CrossRefGoogle Scholar
  64. Wu T, Chen H, Lin Z, Tan C (2016) Identification and quantitation of melamine in milk by near-infrared spectroscopy and chemometrics. J Spectrosc 2016:1–8.  https://doi.org/10.1155/2016/6184987 Google Scholar
  65. Xu QS, Liang Y-Z (2001) Monte Carlo cross validation. Chemom Intell Lab Syst 56:1–11CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Instituto de Química - Universidade Federal do Rio Grande do SulPorto AlegreBrazil
  2. 2.LANAGRO - Laboratório Nacional AgropecuárioPorto AlegreBrazil

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