National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania.
Int J Mol Sci. 2022 Sep 1;23(17):9977. doi: 10.3390/ijms23179977.
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.
新开发的预测模型旨在通过将 ATR-FTIR 光谱数据与统计方法 PLS-DA 相关联，对罗马尼亚蜂蜜样品进行分类，从而可靠地区分样品在植物学和地理来源以及收获年份方面的差异。基于这种方法，109 个蜂蜜样品中有 105 个被正确归属，收获差异模型的准确率为 95%和 97%。对于植物学起源分类，当同时区分四种蜂蜜品种时，83%的调查样本被正确预测。对于特兰西瓦尼亚的样品，地理评估的准确率为 91%，而对于其他地区的样品，准确率为 85%，在交叉验证过程中的总体准确率为 88%。基于这些信号，我们得到了最佳分类模型，从而确定了每种区分方法中最重要的化合物。
