Multivariate and machine learning models to assess the heat effects on honey physicochemical, colour and NIR data
- 70 Downloads
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.
KeywordsHoney Heating treatment Colour NIR spectroscopy Predictive model
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.
- 7.Ruoff K, Luginbühl W, Bogdanov S, Bosset JO, Estermann B, Ziolko T, Kheradmandan S, Amadò R (2007) Quantitative determination of physical and chemical measurands in honey by near-infrared spectrometry. Eur Food Res Technol 225:415–423. https://doi.org/10.1007/s00217-006-0432-8 CrossRefGoogle Scholar
- 15.Bisutti V, Merlanti R, Serva L, Lucatello L, Mirisola M, Balzan S, Tenti S, Fontana F, Trevisan G, Montanucci L, Contiero B, Segato S, Capolongo F (2019) Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy. J Near Infrared Spectrosc 27(1):65–74. https://doi.org/10.1177/0967033518824765 (096703351882476) CrossRefGoogle Scholar
- 16.Il Ministero delle Politiche Agricole e Forestali (2003) D.M. 25 luglio 2003 Metodi di analisi per la valutazione delle caratteristiche di composizione del miele. Gazz Uff della Repubb Ital 185:24–54Google Scholar
- 18.Grané A, Jach A (2014) Application of principal component analysis (PCA) in food science and technology. Wiley, ChichesterGoogle Scholar
- 19.Serva L, Balzan S, Bisutti V, Montemurro F, Marchesini G, Bastianello E, Segato S, Novelli E, Fasolato L (2019) Use of near infrared spectroscopy and chemometrics to evaluate the shelf-life of cloudy sonicated apple juice. J Near Infrared Spectrosc 27:75–85. https://doi.org/10.1177/0967033518821833 (096703351882183) CrossRefGoogle Scholar