Differential Mobility Spectrometry Authenticates the Botanical Origins of Five Monofloral Honeys

Abstract

Differential mobility spectrometry (DMS) enables the detection and separation of volatile organic compounds, exploiting differences in ion mobility under varying electric fields, which enhance the resolution of complex mixtures. In this study, DMS, upon integration with chemometrics, enabled the differentiation of the botanical origins of Italian honeys. Utilizing a benchtop differential mobility spectrometer connected to pure air, 219 monofloral honeys of five different botanical origins (acacia, chestnut, citrus, eucalyptus, and linden) were simultaneously analyzed in positive and negative ion modes and with the analysis duration of less than 2 min. Repeatability of the resultant volatilomic fingerprints was verified by cosine similarity with the subsequent elimination of the nonrepeatable data. Afterward, the data for both polarities were concatenated, and an exploratory investigation was carried out into the discrimination capabilities of DMS by principal component analysis. The merged data were then submitted to a statistical analysis for classification. The performance of the resultant random forest (RF) classifier was evaluated by repeated cross-validation and resubstitution into the training set, and finally, this classifier was validated on a withheld subset of data. The performances of the classification model for each test were evaluated by calculating the contingency table-derived parameters of accuracy, Matthew's correlation coefficient (MCC), sensitivity, and specificity. The RF classifier produced high-performance values when predicting the botanical origin of the honey in both training (MCC 84.8%, accuracy 88.0%, sensitivity 87.5%, and specificity 96.9%) and validation subsets (MCC 82.6%, accuracy 86.3%, sensitivity 85.7%, and specificity 96.5%). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of acacia honey.

Keywords: authenticity assessment; botanical origin; differential mobility spectrometry; floral source; food authentication; nontargeted method.

Figure 1

Workflow of the study. (A) Two grams of each honey was individually placed on a glass tube, warmed up, and their volatile compounds transferred to the instrument through a stream of purified air. (B) Differential mobility spectrometer (DMS) setup allows the ionization of the volatile molecules, their separation in the drift tube, and the simultaneous detection of positive and negative ions. (C) Representative DMS spectrum in positive ion mode. (D) Representative DMS spectrum in negative ion mode. (E) Exploratory investigation of the discrimination capabilities of DMS by principal component analysis (PCA). (F) Building up of a machine learning classifier based on a random forest (RF) algorithm.

Figure 2

Representative differential ion mobility spectra (dispersion plots) in the positive ion mode of (A) acacia honey; (B) chestnut honey; (C) citrus honey; (D) eucalyptus honey; and (E) linden honey. The y-axis shows the separation voltage (U_sv), and the x-axis reports the compensation voltage (U_cv).

Figure 3

Representative differential ion mobility spectra in the negative ion mode of (A) acacia honey; (B) chestnut honey; (C) citrus honey; (D) eucalyptus honey; and (E) linden honey. The y-axis shows the separation voltage (U_sv), and x-axis reports the compensation voltage (U_cv).

Figure 4

PCA three-dimensional score plot (3D-PCA) generated from the merged differential ion mobility data.