Buratti Susanna, Sinelli Nicoletta, Bertone Elisa, Venturello Alberto, Casiraghi Ernestina, Geobaldo Francesco
Dipartimento di Scienze per gli Alimenti, la Nutrizione e l'Ambiente, Università degli Studi di Milano, via Celoria 2, I-20133, Milan, Italy.
Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, c.so Duca degli Abruzzi 24, I-10129, Torino, Italy.
J Sci Food Agric. 2015 Aug 30;95(11):2192-200. doi: 10.1002/jsfa.6933. Epub 2014 Oct 27.
The aim of this study is to investigate the feasibility of a 'holistic' approach, using near infrared (NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica.
Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively.
NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples.
本研究旨在探讨一种“整体”方法的可行性，该方法使用近红外（NIR）光谱以及电子设备（电子鼻和电子舌）作为对不同咖啡品种进行分类的仪器工具。对生咖啡豆、研磨烘焙咖啡豆和咖啡饮品进行了分析。对光谱数据和感官数据应用主成分分析，以揭示样品与变量之间的相关性。在变量选择之后，使用线性判别分析根据三种咖啡类别（罗布斯塔咖啡、天然阿拉比卡咖啡和水洗阿拉比卡咖啡）对样品进行分类。
线性判别分析证明了该方法的实用性：使用近红外数据进行的外部测试集验证显示样品的正确分类率为100%。此外，电子设备在交叉验证中获得了令人满意的正确分类百分比：电子鼻和电子舌正确分类样品的平均值分别为81.83%和78.76%。
与传统分析相比，近红外光谱被证明是一种非常可靠且有用的工具，能够以快速、清洁且廉价的方式对咖啡样品进行分类，而电子设备可以作为研究技术来描述咖啡样品的香气和味道。
