Food Analytical Methods

, Volume 11, Issue 5, pp 1380–1389 | Cite as

Feasibility of Discriminating Dried Dairy Ingredients and Preheat Treatments Using Mid-Infrared and Raman Spectroscopy

  • Xiao Wang
  • Carlos Esquerre
  • Gerard Downey
  • Lisa Henihan
  • Donal O’Callaghan
  • Colm O’Donnell


This study investigated the feasibility of mid-infrared (MIR) and Raman spectroscopy for (i) discrimination of three dried dairy ingredients, namely skim milk powder (SMP), whey protein concentrate (WPC) and demineralised whey protein (DWP) powder, and (ii) discrimination of preheat treatments of dried dairy ingredients using partial least squares discriminant analysis (PLS-DA). PLS1-DA models developed using MIR ranges of 800–1800 and 1200–1800 cm−1 yielded the best discrimination (correct identification of 97.2% for SMP discrimination and 100% for WPC and DWP discrimination). The best PLS2-DA model using MIR spectroscopy was developed over the spectral range of 800–1800 cm−1 and produced correct identification of 100% for dairy ingredient discrimination. Models developed using Raman 800–1800 and 1200–1800 cm−1 spectral ranges correctly discriminated (100% correctly identified) each dairy ingredient. Although all PLS1-DA and PLS2-DA models developed using both spectral technologies for preheat treatment discrimination had good discrimination accuracy (86–100%), they employed a high number of factors (8–9 for the best model). The use of the Martens uncertainty test successfully reduced the number of factors employed (3–4 for the best models) and improved the performance of PLS1-DA models for preheat treatment discrimination (all 100% correctly identified). This feasibility study demonstrates the potential of both MIR and Raman spectroscopy for rapid characterisation of dried dairy ingredients.


Mid-infrared spectroscopy Raman spectroscopy Dairy ingredients Preheat treatment Partial least squares discriminant analysis (PLS-DA) 



Xiao Wang wishes to acknowledge the Chinese Scholarship Council for financially supporting his PhD study.

Funding Information

This study was funded by the Irish Department of Agriculture, Food and the Marine through its Food Institutional Research Measure (FIRM) (Project Reference No: 11/F/052).

Compliance with Ethical Standards

Conflict of Interest

Xiao Wang declares that he has no conflict of interest. Carlos Esquerre declares that he has no conflict of interest. Gerard Downey declares that he has no conflict of interest. Lisa Henihan declares that she has no conflict of interest. Donal O’Callaghan declares that he has no conflict of interest. Colm O’Donnell declares that he has no conflict of interest.

Supplementary material

12161_2017_1114_MOESM1_ESM.docx (39 kb)
Table S1 (DOCX 38 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.UCD School of Biosystems and Food EngineeringUniversity College DublinDublin 4Ireland
  2. 2.Food Chemistry and Technology DepartmentTeagasc Food Research CentreDublin 15Ireland
  3. 3.Food Chemistry and Technology DepartmentTeagasc Food Research CentreCo. CorkIreland

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