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European Food Research and Technology

, Volume 245, Issue 10, pp 2269–2278 | Cite as

Multivariate and machine learning models to assess the heat effects on honey physicochemical, colour and NIR data

  • Severino SegatoEmail author
  • Roberta Merlanti
  • Vittoria Bisutti
  • Ludovica Montanucci
  • Lorenzo Serva
  • Lorena Lucatello
  • Massimo Mirisola
  • Barbara Contiero
  • Daniele Conficoni
  • Stefania Balzan
  • Giorgio Marchesini
  • Francesca Capolongo
Original Paper
  • 70 Downloads

Abstract

We evaluated the effects of pre-processing thermal treatments on the physicochemical, colour and near-infrared (NIR) spectral data of 30 honey samples. The trial was settled as a bi-factorial experimental design that considered nine experimental groups according to the fixed effects of heating treatment and honey phase: none, mild (39 °C for 30′) and high heating (55 °C for 24 h) per crystallised, bi-phase and liquid honey samples. Increasing temperatures significantly modified moisture, hydroxymethylfurfural content and lightness. The multivariate classifier models showed that NIR data of warmed crystallised and bi-phase honeys were significantly different from that of the untreated ones, while they sorted a similar assignment for all the liquid samples. The support vector machine model confirmed that the highest tested temperature represented a bias in the informative feature of NIR data, if they would be used in further analytical assessment of the intrinsic qualities of crystallised or bi-phase honey.

Keywords

Honey Heating treatment Colour NIR spectroscopy Predictive model 

Notes

Acknowledgements

This study was financially supported by the FONDAZIONE CARIVERONA (Project SAFIL, call 2016) and Padova University (Project CPDA 158894/15-PRAT).

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest exist in the submission of this manuscript.

Compliance with ethics requirements

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

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Severino Segato
    • 1
    Email author
  • Roberta Merlanti
    • 2
  • Vittoria Bisutti
    • 1
  • Ludovica Montanucci
    • 2
  • Lorenzo Serva
    • 1
  • Lorena Lucatello
    • 2
  • Massimo Mirisola
    • 1
  • Barbara Contiero
    • 1
  • Daniele Conficoni
    • 1
  • Stefania Balzan
    • 2
  • Giorgio Marchesini
    • 1
  • Francesca Capolongo
    • 2
  1. 1.Department of Animal Medicine, Production and HealthUniversity of PadovaLegnaroItaly
  2. 2.Department of Comparative Biomedicine and Food ScienceUniversity of PadovaLegnaroItaly

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