Physicochemical characterization of Lavandula spp. honey with FT-Raman spectroscopy

Abstract

A B S T R A C TThis study aimed to evaluate the potential of FT-Raman spectroscopy in the prediction of the chemical composition of Lavandula spp. monofloral honey. Partial Least Squares (PLS) regression models were performed for the quantitative estimation and the results were correlated with those obtained using reference methods.Good calibration models were obtained for electrical conductivity, ash, total acidity, pH, reducing sugars, hydroxymethylfurfural (HMF), proline, diastase index, apparent sucrose, total flavonoids content and total phenol content. On the other hand, the model was less accurate for pH determination. The calibration models had high r 2 (ranging between 92.8% and 99.9%), high residual prediction deviation -RPD (ranging between 4.2 and 26.8) and low root mean square errors. These results confirm the hypothesis that FT-Raman is a useful technique for the quality control and chemical properties' evaluation of Lavandula spp honey. Its application may allow improving the efficiency, speed and cost of the current laboratory analysis.

Zantaz honey “monoflorality”: Chemometric applied to the routinely assessed parameters

“…For honey types with well-established monoflorality percentage, authors declared the importance of identified pollen species in shaping the final characteristics of honey samples (Oddo & Piro, 2004). An example is that of lavender honey (Estevinho et al, 2016;Anjos, Santos, Paixão, & Estevinho, 2018), for which authors, after confirming that all analysed samples presented lavender pollen more than the monoflorality threshold (15%), found different clustering profiles using the pollen analysis or the physicochemical characteristics. Furthermore, when analysing the differences between the established clusters, the same authors found no significant contribution of lavender pollen, highlighting so the importance of other pollens in shaping the physicochemical features of a honey.…”

FT-RAMAN methodology for the monitoring of honeys' spirit distillation process

“…The RAMAN spectrum provides a chemical fingerprint of a single chemical substance or a mixture with specific characteristics. Concerning this ability, RAMAN spectroscopy have been used in several contexts, among which the determination of methanol content in alcoholic drinks (Vaskova, 2014); food product characterization and quantification (Anjos, Santos, Paixão, & Estevinho, 2018) and real time monitoring of wine fermentation (Wang, Li, Ma, & Liang, 2014). Also, Mendes, Oliveira, Suarez, and Rubim (2003) applied FT-Raman spectrometry to evaluate the ethanol content in cachaça and whisky.…”

Single‐layer multiple‐kernel‐based convolutional neural network for biological Raman spectral analysis

“…Since the discovery of Raman scattering [1] in 1928, Raman spectroscopy has been applied to analyze and classify target materials in a variety of fields, including chemistry, food, environment, and medicine. [2][3][4][5][6] Naturally, a number of classification methods for Raman spectra have also been studied. [7][8][9][10] The most widely used classification methods are linear discriminant analysis (LDA), artificial neural networks (ANNs), and support vector machines (SVM).…”

“…Since the discovery of Raman scattering in 1928, Raman spectroscopy has been applied to analyze and classify target materials in a variety of fields, including chemistry, food, environment, and medicine . Naturally, a number of classification methods for Raman spectra have also been studied .…”