Characterization of volatile compounds of Turkish pine honeys from different regions and classification with chemometric studies

Characterization of unifloral Italian (Piedmont region) honeys by headspace solid phase microextraction coupled to gas chromatography–mass spectrometry

“…According to Ciotlaus et al 33 specific compounds of lime tree honeys include methylacetophenone and benzene derivatives. By contrast, Duru et al 48 selected 1‐dodecanol as the botanical discriminant of linden honeys.…”

“…As far as volatiles are concerned, Karabagias et al 47 indicated decanol as possible marker of fir honeydew from Aitoloakarnania (Greece). Duru et al 48 reported several compounds, mainly C 8 ‐C 10 alcohols and aldehydes, as possible markers for Turkish pine honeys; among these compounds, methyl salycilate is included. Dymerski et al 31 indicated (E)‐nonen‐2‐al and pentadecane as botanical discriminant of honeydew honey, while Plutovska et al 34 suggested higher straight chain alcohols as possible discriminant compounds for this kind of honey.…”

“…specific compounds of lime tree honeys include methylacetophenone and benzene derivatives. By contrast, Duru et al48 selected 1-dodecanol as the botanical discriminant of linden honeys.In our work, we found rose oxide and carvacrol in almost all lime tree honeys, and very rarely in other types of honeys. Moreover, we identified the terpenes cis and trans-p-mentha-2,8-dien-1-ol as typical for lime tree honeys, since they are present in almost all the lime tree honeys analyzed, and in none of the other types of honeys except for few samples of rhododendron honeys.…”

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

“…From the chemometric elaboration of the 92 molecules found, 51 of them contributed to the discrimination of the five classes. Similar studies, but ones which only took into account one honey class (Quercus ilex honeydew and pine honeys) and focused on geographical origin, with samples coming from several regions of Greece and Turkey, respectively, were carried out by Karabagias et al [36] and Duru et al [37] by PCA and stepwise Machine learning is a "new" frontier of chemometrics in which models are computed in an iterative way, with the computation that "learns" from data: once a model has been computed, the calculation starts again using the previous results as starting points, instead of the original data, and this procedure is carried out iteratively until a satisfactory result, or a convergence, is reached. Bogdal et al tested random forest, gradient boosting, support vector machine, naïve bayes, logistic regression [66] and convolutional neural networks on GC-MS spectra converted to images [67].…”

“…From the chemometric elaboration of the 92 molecules found, 51 of them contributed to the discrimination of the five classes. Similar studies, but ones which only took into account one honey class (Quercus ilex honeydew and pine honeys) and focused on geographical origin, with samples coming from several regions of Greece and Turkey, respectively, were carried out by Karabagias et al [36] and Duru et al [37] by PCA and stepwise LDA (SLDA), or by PCA and CA. Karabagias [38], instead, coupled GC-MS and chemometric analyses (PCA, LDA and univariate analysis of variance, ANOVA) to study the ageing of honeydew honey during one year of in-house storing.…”