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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Animal Medicine, Production and Health, University of Padova, 35020, Legnaro, PD, Italy

    Severino Segato, Vittoria Bisutti, Lorenzo Serva, Massimo Mirisola, Barbara Contiero, Daniele Conficoni & Giorgio Marchesini

  2. Department of Comparative Biomedicine and Food Science, University of Padova, 35020, Legnaro, PD, Italy

    Roberta Merlanti, Ludovica Montanucci, Lorena Lucatello, Stefania Balzan & Francesca Capolongo

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  1. Severino Segato
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Correspondence to Severino Segato.

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Segato, S., Merlanti, R., Bisutti, V. et al. Multivariate and machine learning models to assess the heat effects on honey physicochemical, colour and NIR data. Eur Food Res Technol 245, 2269–2278 (2019). https://doi.org/10.1007/s00217-019-03332-x

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  • DOI: https://doi.org/10.1007/s00217-019-03332-x

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