Núcleo Avançado de Tecnologias Analíticas (NATA), Universidade da Integração Internacional da Lusofonia Afro-brasileira (Unilab), Brazil.
Laboratório de Estudos em Química Aplicada (LEQA), Departamento de Química Analítica e Físico-Química, Universidade Federal do Ceará (UFC), Brazil.
Food Chem. 2022 Jan 1;366:130480. doi: 10.1016/j.foodchem.2021.130480. Epub 2021 Jun 26.
The near-infrared spectrometry combined with the one-class classification method was applied as quality control of the agroforestry-grown specialty coffee. A total of 34 samples were analyzed in this study. Spectral data were obtained using a NIR portable and different pre-treatment strategies for baseline correction were evaluated. Unsupervised pattern recognition (PCA and HCA) techniques were performed. The construction of the classification model was carried out using the dd-SIMCA algorithm with 19 samples acquired directly from producers that are recognized for the best quality control of the specialty type coffee. In order to test the model, 15 samples of non-specialty type, obtained in local markets, were evaluated. The classification model with the highest correct classification rate (CCR) scored 100% and 87% in the validation and test groups, respectively. The results demonstrated that the application of this strategy was successful in verifying the authenticity of specialty type agroforestry-grown coffee samples.
近红外光谱结合单类分类方法被应用于农林复合种植特种咖啡的质量控制。本研究共分析了 34 个样本。使用 NIR 便携式光谱仪获得了光谱数据，并评估了不同的基线校正预处理策略。采用无监督模式识别（PCA 和 HCA）技术进行了分析。采用 dd-SIMCA 算法构建分类模型，使用直接从被认为是特种咖啡质量控制最佳的生产者处获得的 19 个样本进行了构建。为了测试模型，评估了在当地市场获得的 15 个非特种类型的样本。在验证组和测试组中，分类模型的最高正确分类率（CCR）分别为 100%和 87%。结果表明，该策略的应用成功验证了农林复合种植特种咖啡样本的真实性。
