In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000–600 cm⁻¹, with a spectral resolution of 4 cm⁻¹. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R² greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.

中文翻译：

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振动光谱和化学计量学工具对蜂蜜掺假检测和定量的快速分析方法的发展

在这项研究中，傅立叶变换中红外（FT-MIR）光谱技术与化学计量学方法相结合，用于监测蜂蜜与糖浆的掺假情况。记录的光谱数据来自4000–600 cm ^-1的波数区域，光谱分辨率为4 cm ^-1。使用主成分分析（PCA）和层次聚类分析（HCA）进行定性分析，以区分掺假蜂蜜和未掺假蜂蜜。对于定量分析，我们使用偏最小二乘回归（PLS-R）和支持向量机（SVM）来开发最佳校准模型。PCA的使用表明前两个主成分占总变异性的96％。PCA和HCA允许将数据集分为两组：掺假蜂蜜和未掺假蜂蜜。通过使用PLS-R和SVM-R校准模型对掺假进行量化，可以显示出以高相关系数R ²表示的高性能。如果使用PLS-R和SVM-R，均方根误差（RMSE）的最小值分别小于98％和95％，且均方根误差（RMSE）的最小值分别小于1.12和1.85。我们的结果表明，FT-MIR光谱结合化学计量学技术可以成功地用作一种简单，快速且无损的定量和鉴别掺假蜂蜜的方法。