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.

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

Segato S.
;
Merlanti R.;Bisutti V.;Montanucci L.;Serva L.
;
Lucatello L.;Mirisola M.;Contiero B.;Conficoni D.;Balzan S.;Marchesini G.;Capolongo F.
2019

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3307154
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