He Jian, Rodriguez-Saona Luis E, Giusti M Monica
Department of Food Science, The Ohio State University, Columbus, Ohio 43210, USA.
J Agric Food Chem. 2007 May 30;55(11):4443-52. doi: 10.1021/jf062715c. Epub 2007 May 8.
The determination of food authenticity is a crucial issue for food quality and safety. Midinfrared spectroscopy provides rapid chemical profiling of agricultural products and could become an effective tool for authentication when coupled to chemometrics. This study developed a simple protocol for classifying commercial juices using attenuated total reflectance infrared spectroscopy. Spectra from a total of 52 juices together with their extracted sugar-rich and phenol-rich fractions were obtained to construct multivariate models [hierarchical cluster analysis (HCA) and soft independent modeling of class analogy (SIMCA)] for pattern recognition analysis and prediction. Spectra of the sugar-rich fraction, comprised primarily of sugars and simple acids, almost superimposed the whole juice spectra. Solid-phase extraction enriched phenol compounds and provided signature-like spectral information that substantially improved the SIMCA modeling power over the whole juice or sugar-rich fraction models and allowed for the differentiation of juices with different origins. Zero percent misclassification was achieved by the phenol-rich fraction model. HCA successfully recognized the natural grouping of juices based on ingredients similarity. The infrared technique assisted by a simple fractionation and chemometrics provided a promising analytical method for the assurance of juice quality and authenticity.
食品真实性的判定是食品质量与安全的关键问题。中红外光谱可对农产品进行快速化学分析，与化学计量学结合后有望成为一种有效的鉴别工具。本研究开发了一种利用衰减全反射红外光谱对市售果汁进行分类的简单方法。采集了总共52种果汁及其富含糖分和富含酚类的提取物的光谱，以构建用于模式识别分析和预测的多变量模型[层次聚类分析（HCA）和类软独立建模（SIMCA）]。富含糖分的提取物的光谱主要由糖和简单酸组成，几乎与全果汁光谱重叠。固相萃取富集了酚类化合物，并提供了类似特征的光谱信息，这大大提高了SIMCA相对于全果汁或富含糖分提取物模型的建模能力，并能够区分不同产地的果汁。富含酚类提取物的模型实现了零误分类。HCA成功地根据成分相似性识别出果汁的自然分组。通过简单的分馏和化学计量学辅助的红外技术为保证果汁质量和真实性提供了一种有前景的分析方法。
