Massaro Andrea, Volpe Ettore, Leone Alba, Piro Roberto, Mäkelä Meri, Kontunen Anton, Zacometti Carmela, Tata Alessandra
Experimental Chemistry Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Viale Fiume, 78, 36100 Vicenza, Vicenza, Italy.
Olfactomics Limited, 33720 Tampere, Finland.
J Agric Food Chem. 2025 Feb 19;73(7):4376-4384. doi: 10.1021/acs.jafc.4c10267. Epub 2025 Feb 4.
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.
差分离子迁移谱（DMS）利用不同电场下离子迁移率的差异，能够检测和分离挥发性有机化合物，从而提高复杂混合物的分辨率。在本研究中，DMS与化学计量学相结合，能够区分意大利蜂蜜的植物来源。使用连接到纯净空气的台式差分离子迁移谱仪，对219种来自五种不同植物来源（刺槐、栗子、柑橘、桉树和椴树）的单花蜂蜜在正离子和负离子模式下同时进行分析，分析时间不到2分钟。通过余弦相似度验证所得挥发物指纹图谱的重复性，并随后消除不可重复的数据。之后，将两种极性的数据合并，并通过主成分分析对DMS的鉴别能力进行探索性研究。然后将合并后的数据提交进行分类的统计分析。通过重复交叉验证并重新代入训练集来评估所得随机森林（RF）分类器的性能，最后，在保留的数据子集上对该分类器进行验证。通过计算列联表得出的准确度、马修斯相关系数（MCC）、灵敏度和特异性参数，评估每个测试的分类模型性能。在预测训练子集（MCC 84.8%，准确度88.0%，灵敏度87.5%，特异性96.9%）和验证子集（MCC 82.6%，准确度86.3%，灵敏度85.7%，特异性96.5%）中蜂蜜的植物来源时，RF分类器产生了高性能值。在刺槐蜂蜜的正确分类中遇到了一些局限性，预计将通过进一步研究加以缓解。
