Assay of honey freshness by a novel optical technique

Botanical Origin Influence on Some Honey Physicochemical Characteristics and Antioxidant Properties

Five types of honey (multifloral, sunflower, linden, rapeseed, and acacia), from Southern Romania, were classified using chemometrics methods coupled with IR spectroscopy. The botanical origin's effect on the physicochemical characteristics of honey was studied to highlight the most valuable plant source of honey. Except for antioxidant activity, the moisture, ash, electrical conductivity (EC), pH, free acidity (FA), total sugar content (TSC), hydroxymethylfurfural (HMF), total phenolic (TPC), tannin (TTC), and flavonoid content (TFC) were significantly influenced by the botanical origin of the honey. The results showed that sunflower honey had the highest moisture (15.53%), free acidity (16.67 mEq kg^-1), electrical conductivity (483.92 µS cm^-1), phenolics (167.59 mg GAE 100 g^-1), and flavonoids (19.00 mg CE 100 g^-1), whereas multifloral honey presented the highest total sugar content (69.64 g Glu 100 g^-1). The highest HMF content was found in linden honey (33.94 mg kg^-1). The HMF contents of all tested honey were within the standard recommended limit, and they confirmed that the tested honey was free of any heat treatment. All five types of tested honey presented a safe moisture content for storage and consumption (12.21-18.74%). The honey's free acidity was in the range of 4.00 to 25.00 mEq kg^-1; this indicated the freshness of the samples and the absence of any fermentation processes in the tested honey. Honey with a total sugar content over 60% (except for linden honey, with 58.05 g glucose 100 g^-1) showed the characteristic of nectar-derived honey. The elevated antioxidant activity of honey was correlated with its high moisture, flavonoids, and HMF, whereas the tannins and HMF were positively correlated with ash and electrical conductivity. The higher content of phenolics, flavonoids, and tannins was correlated with higher free acidity. The chemometric method, coupled with ATR-FTIR spectra, revealed a clear separation between linden honey from acacia, multifloral, and sunflower honey.

Internal reflectance cell fluorescence measurement combined with multi-way analysis to detect fluorescence signatures of undiluted honeys and a fusion of fluorescence and NIR to enhance predictability

Honey is a complex food matrix that contains diverse polyphenolic compounds. Some phenolics exhibit fluorescence signatures which can be used to evaluate honey quality, and authenticity and to determine botanical origin. Mānuka honey contains two unique fluorescence markers: Leptosperin (MM1) and Lepteridine^TM (MM2) that are derived from Leptospermum scoparium nectar. Fluorescence measurement of supersaturated solutions such as undiluted honeys can be challenged by complex inner filter effects. The current study shows the ability of internal reflectance cell fluorescence measurement and multi-way analysis to detect fluorophores in undiluted honeys. This study scanned honeys from different geographic districts generating excitation emission matrices (250-400/300-600 nm), and by near infrared (NIR) hyperspectral camera (547-1701 nm). PARAFAC and tri-PLS could track two fluorescence markers: MM1 (R² = 0.82 & RMSEP = 138.65) and MM2 (R² = 0.82 & RMSEP = 2.75) from undiluted honey fluorescence data with > 80 % accuracy. Classification of mono-floral, multi-floral and non-mānuka honeys achieved 90 % overall accuracy. Fusion of fluorescence data at ƛ_ex 270 & 330 nm and NIR hyperspectral data combined with multi-block PLS analysis enhances predictability of fluorescence markers further. The study revealed the potential of internal reflectance cell fluorescence measurement combined with chemometrics and data fusion for rapid evaluation of honey quality and botanical origin.

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.