Kelly J F Daniel, Downey Gerard, Fouratier Vanessa
Teagasc, The National Food Centre, Ashtown, Dublin 15, Ireland.
J Agric Food Chem. 2004 Jan 14;52(1):33-9. doi: 10.1021/jf034985q.
Fourier transform infrared (FTIR) spectroscopy and attenuated total reflection (ATR) sampling have been used to detect adulteration of honey samples. The sample set comprised 320 spectra of authentic (n = 99) and adulterated (n = 221) honeys. Adulterants used were solutions containing both d-fructose and d-glucose prepared in the following respective weight ratios: 0.7:1.0, 1.2:1.0 (typical of honey composition), and 2.3:1.0. Each adulterant solution was added to individual honeys at levels of 7, 14, and 21% w/w. Spectral data were compressed and analyzed using k-nearest neighbors (kNN) and partial least squares (PLS) regression techniques. A number of data pretreatments were explored. Best classification models were achieved with PLS regression on first derivative spectra giving an overall correct classification rate of 93%, with 99% of samples adulterated at levels of 14% w/w or greater correctly identified. This method shows promise as a rapid screening technique for detection of this type of honey adulteration.
傅里叶变换红外（FTIR）光谱法和衰减全反射（ATR）采样法已被用于检测蜂蜜样品的掺假情况。该样本集包含320个纯正蜂蜜（n = 99）和掺假蜂蜜（n = 221）的光谱。所使用的掺假物是含有d-果糖和d-葡萄糖的溶液，其各自的重量比分别如下：0.7:1.0、1.2:1.0（典型的蜂蜜成分比例）和2.3:1.0。每种掺假物溶液以7%、14%和21%（w/w）的水平添加到各个蜂蜜样品中。光谱数据通过k近邻（kNN）和偏最小二乘法（PLS）回归技术进行压缩和分析。研究了多种数据预处理方法。通过对一阶导数光谱进行PLS回归获得了最佳分类模型，总体正确分类率为93%，其中99%的掺假水平为14%（w/w）或更高的样品被正确识别。该方法有望成为检测此类蜂蜜掺假的快速筛选技术。
