Department of Food Science and Nutrition, School of Food Engineering, State University of Campinas - UNICAMP, Campinas, São Paulo, Brazil.
Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185 Rome, Italy.
Analyst. 2023 Mar 27;148(7):1524-1533. doi: 10.1039/d3an00104k.
Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.
罗布斯塔亚马逊咖啡是一种受欢迎的咖啡，最近已在巴西注册为地理标志。它由地理上非常接近的土著和非土著咖啡种植者生产。需要对咖啡是否真的由土著人生产进行认证，近红外（NIR）光谱是一种很好的技术。为了满足近红外光谱小型化的巨大趋势，本工作比较了台式和便携式近红外仪器，使用偏最小二乘判别分析（PLS-DA）来区分罗布斯塔亚马逊咖啡样品。为了确保结果具有可比性，同时为判别分析的训练集和测试集提供有代表性的选择，应用了一种基于耦合 ComDim 多块分析和双工算法的样品选择策略。测试了不同的预处理技术，以创建多个要用于 ComDim 的矩阵，并构建判别模型。台式近红外最佳的 PLS-DA 模型对测试样本的准确率为 96%，而便携式近红外的正确分类率为 92%。通过执行无偏样品选择策略，证明便携式近红外可用于咖啡产地分类，提供与台式近红外相似的结果。
