Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Xueyuan Street, Xiasha Higher Education District, Hangzhou 310018, China.
Food Chem. 2013 Dec 15;141(4):4132-7. doi: 10.1016/j.foodchem.2013.07.013. Epub 2013 Jul 10.
This paper investigates the feasibility of using FT-NIR spectroscopy and chemometrics for rapid analysis of poplar balata (PB) in Chinese propolis. Because practical adulterations usually involve addition of certain known active components, together with commercial PB, the commonly targeted analysis methods are insufficient to identify PB-adulterated propolis. Untargeted analysis of PB was performed by developing class models of pure propolis using one-class partial least squares (OCPLS). Quantitative analysis of PB was performed using partial least squares regression (PLSR). For untargeted analysis, the most accurate OCPLS model was obtained with SNV spectra with sensitivity 0.960 and specificity 0.941. OCPLS could detect adulterations with 2% (w/w) or more PB. For quantitative analysis, the root mean squared error of prediction (RMSEP) value of PB was 0.902 (w/w, %) with SNV-PLS. FT-NIR spectrometry and chemometrics demonstrate potential for rapid analysis of PB adulterations in Chinese propolis.
本文研究了傅里叶变换近红外光谱（FT-NIR）和化学计量学在快速分析中国蜂胶中杨槐乳胶（PB）中的应用。由于实际的掺假通常涉及添加某些已知的活性成分，以及商业 PB，因此通常针对的分析方法不足以识别 PB 掺假的蜂胶。通过使用单类偏最小二乘（OCPLS）为纯蜂胶开发分类模型，对 PB 进行了非靶向分析。使用偏最小二乘回归（PLSR）对 PB 进行定量分析。对于非靶向分析，使用 SNV 光谱获得的最准确的 OCPLS 模型，其灵敏度为 0.960，特异性为 0.941。OCPLS 可以检测到含有 2%（w/w）或更多 PB 的掺假。对于定量分析，SNV-PLS 中 PB 的预测均方根误差（RMSEP）值为 0.902（w/w，%）。FT-NIR 光谱和化学计量学为快速分析中国蜂胶中的 PB 掺假提供了潜力。
