Abstract The potential of Fourier Transform Near-Infrared spectroscopy (FT-NIR) and High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) in combination with multivariate data analysis was examined to classify 70 honey samples (belonging to 7 different varieties) according to their botanical origin. In the first part of the work, classification was achieved by applying PLS-DA to the individual data blocks: this approach led to promising results from the prediction point of view. In the second part of the study, the multi-block data set has been handled by data-fusion techniques which led to comparable or better results than those obtained by the analysis of individual matrices. These satisfactory results confirm the feasibility of the proposed methodology and encourage the development of similar approaches for honey quality assessment.

中文翻译：

---

应用高效液相色谱、近红外光谱和化学计量学对蜂蜜进行分类

摘要 傅里叶变换近红外光谱 (FT-NIR) 和带二极管阵列检测的高效液相色谱 (HPLC-DAD) 结合多变量数据分析对 70 个蜂蜜样品（属于 7 个不同品种）进行分类的潜力进行了研究。 ) 根据它们的植物来源。在工作的第一部分，分类是通过将 PLS-DA 应用于单个数据块来实现的：从预测的角度来看，这种方法产生了有希望的结果。在研究的第二部分，多块数据集已通过数据融合技术处理，这导致了与通过分析单个矩阵获得的结果相当或更好的结果。