Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB, UK.
Anal Methods. 2022 May 5;14(17):1663-1670. doi: 10.1039/d2ay00219a.
Detecting food adulteration has always been an important task for food safety, especially when grapefruit is the adulterant as components in the juice have undesired interactions with many medicines. In this study we employed a handheld Raman device to detect adulteration of orange juices with grapefruit juices. Fresh fruits of orange and grapefruit were purchased from five different sources and fruit juices were made using a handheld juicer. The extracted juices were then mixed in a way that concentrations of grapefruit juices varied from 0% to 100% in 5% increments. In order to study the impact of the different sources of the fruits, three different sets of mixtures were prepared based on their spectral similarity and dissimilarity. Raman spectra were collected using a handheld instrument with an excitation laser at 785 nm and data analysed using principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least squares regression (PLS-R). PLS-R models were trained and validated on: (i) the full data set from the three different mixture sets, and (ii) each set of the three mixtures separately. The results showed that a good calibration model was obtained using full data which had a coefficient of determination () of 0.81 and a root mean square error of prediction (RMSEP) of 12.5%. Such results were improved when the PLS-R model was trained and validated on the three separate mixture combinations, where the varied from 0.85 to 0.89 and RMSEP varied from 9.9% to 11.6%. Finally, we adopted a two step approach in which a partial least squares for discriminant analysis (PLS-DA) was trained first to classify the three sample sources and then three different PLS-R models were subsequently trained on samples from the same source. This resulted in a of 0.83 and RMSEP of 12.0%. In conclusion, we have demonstrated that Raman spectroscopy can be used as a portable and rapid analytical tool for detecting adulteration of grapefruit juice added to orange juice.
检测食品掺假一直是食品安全的重要任务，特别是当掺假物是葡萄柚时，因为葡萄柚汁中的成分与许多药物会产生不良相互作用。在这项研究中，我们使用手持式拉曼设备来检测橙汁中掺入葡萄柚汁的情况。从五个不同的来源购买新鲜的橙和葡萄柚水果，并使用手持式榨汁机制作果汁。然后，将提取的果汁以 5%的增量混合，使葡萄柚汁的浓度从 0%变化到 100%。为了研究水果来源的不同影响，根据光谱相似性和相异性，制备了三组不同的混合物。使用具有 785nm 激发激光的手持式仪器收集拉曼光谱，使用主成分分析（PCA）、主成分判别函数分析（PC-DFA）和偏最小二乘回归（PLS-R）对数据进行分析。PLS-R 模型在以下方面进行了训练和验证：（i）来自三组不同混合物的完整数据集，以及（ii）三组混合物的每一组分别。结果表明，使用完整数据集获得了良好的校准模型，其决定系数（）为 0.81，预测均方根误差（RMSEP）为 12.5%。当 PLS-R 模型在三组混合物的单独组合上进行训练和验证时，结果得到了改善，其中在 0.85 到 0.89 之间变化，RMSEP 在 9.9%到 11.6%之间变化。最后，我们采用了两步法，首先用偏最小二乘判别分析（PLS-DA）训练一个模型来对三个样品来源进行分类，然后再用三个不同的 PLS-R 模型对来自同一来源的样本进行训练。结果得到了 0.83 的，RMSEP 为 12.0%。总之，我们已经证明，拉曼光谱可以用作检测橙汁中添加葡萄柚汁掺假的便携式快速分析工具。
