Cheah Wai Lok, Fang Mingchih
Department of Food Science, College of Life Science, National Taiwan Ocean University, No 2, Beining Rd., Keelung City 20224, Taiwan.
Foods. 2020 Jul 4;9(7):880. doi: 10.3390/foods9070880.
Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (-LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%-60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and -LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and -LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.
咖啡是十大掺假最多的食品之一。咖啡掺假主要是在烘焙和研磨后的咖啡豆中混入其他低价值材料,如咖啡渣、玉米、大豆和其他谷物产品。用高效液相色谱法(HPLC)检测掺假咖啡被认为是一种靶向分析方法,通常将碳水化合物和其他酚类化合物用作标志物。然而,HPLC分析的准确定性和定量很耗时。本研究开发了一种化学计量分析方法,即所谓的非靶向分析方法来检测咖啡掺假。通过将液态咖啡直接注入HPLC而无需样品制备和目标分析物鉴定来获得HPLC色谱图。通过统计方法实现了咖啡和掺假咖啡之间的区分。与仅提供官能团信息的光谱化学计量学相比,基于HPLC的化学计量学提供了更多特征信息(分离的化合物)。在本研究中,将绿色阿拉比卡咖啡豆、大豆和绿色绿豆在工业咖啡豆烘焙机中烘焙,然后研磨。咖啡渣进行干燥处理。在进行HPLC分析之前,将咖啡和掺假物以不同比例混合。对HPLC数据(保留时间和峰强度)进行主成分分析(PCA)能够将咖啡与掺假咖啡区分开来。该方法的检测限为5%。还基于PCA数据建立了两个模型。第一个模型用于区分咖啡样品和掺假咖啡。第二个模型旨在识别掺假咖啡中混入的特定掺假物。应用各种参数,如灵敏度(SE)、特异性(SP)、可靠率(RLR)、阳性似然比(+LR)和阴性似然比(-LR)来评估所设计模型的性能。结果表明,基于PCA的模型能够区分纯咖啡和掺假样品(掺有5%-60%大豆、绿豆或咖啡渣的咖啡豆)。第一个模型的SE、SP、RLR、+LR和-LR分别为0.875、0.938、0.813、14.1和0.133。在第二个模型中,它可以正确区分掺假咖啡和纯咖啡。然而,从混入咖啡的三种设计掺假物中正确确定特定掺假物的机会只有约30%。第二个模型的SE、RLR和-LR分别为0.333、0.333和0.667。因此,基于HPLC的化学计量分析能够检测咖啡掺假。在区分咖啡和掺假咖啡方面非常可靠。然而,要辨别掺假咖啡中的哪种掺假物可能还需要更多工作。