Coffee quality, and therefore its price, is determined by coffee species and varieties, geographic location, the method used to process green coffee beans, and particularly the care taken during coffee production. Determination of coffee quality is often done by the nondestructive and fast near infrared spectroscopy (NIRS), which provides a huge amount of data about the samples. NIRS data require sophisticated, multivariate data analysis methods, such as principal component analysis, or linear discriminant analysis. Since the obtained data are a set of spectra, they can also be analyzed by signal processing methods. In the present study, the applications of two novel methods, detrended fluctuation analysis (DFA) and yield stability index (YSI), is introduced on NIR spectra of different roasting levels of coffee samples. Fourteen green coffee samples from all over the world have been roasted on three different levels and their NIR spectra were analyzed. DFA successfully differentiated the green samples from the roasted ones, however, the joint analysis of all samples was not able to differentiate the roasting levels. On the other hand, DFA successfully differentiated the roasting levels on samples level, which was strengthened by a 100% accurate agglomerative hierarchical clustering. YSI was first used in NIR signal processing and was able to detect that a light roast is the most stable among all roasting levels. Future research should focus on the application of DFA in terms of the analysis of the effects of other transformation methods of the spectra.

中文翻译：

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应用去趋势波动分析和产量稳定性指数评估生咖啡和焙炒咖啡样品的近红外光谱

咖啡的质量及其价格取决于咖啡的种类和品种，地理位置，加工生咖啡豆的方法，尤其是咖啡生产过程中的保养。咖啡质量的确定通常是通过无损快速近红外光谱（NIRS）进行的，该技术可提供有关样品的大量数据。NIRS数据需要复杂的多元数据分析方法，例如主成分分析或线性判别分析。由于获得的数据是一组光谱，因此也可以通过信号处理方法进行分析。在本研究中，介绍了两种新方法的应用：去趋势波动分析（DFA）和产量稳定性指数（YSI），用于不同烘焙水平的咖啡样品的NIR光谱。来自世界各地的14个生咖啡样品已在三个不同的水平上进行了烘焙，并对其近红外光谱进行了分析。DFA成功地将绿色样品与烘烤的样品区分开，但是，所有样品的联合分析无法区分烘烤量。另一方面，DFA成功地将焙烧水平与样品水平区分开，并通过100％准确的聚集层次聚类得到了加强。YSI首先用于NIR信号处理，并且能够检测到轻度烘焙在所有烘焙级别中最稳定。未来的研究应集中在DFA的应用方面，以分析其他光谱转换方法的效果。DFA成功地将绿色样品与烘烤的样品区分开，但是，所有样品的联合分析无法区分烘烤量。另一方面，DFA成功地将焙烧水平与样品水平区分开，并通过100％准确的聚集层次聚类得到了加强。YSI首先用于NIR信号处理，并且能够检测到轻度烘焙在所有烘焙级别中最稳定。未来的研究应集中在DFA的应用方面，以分析其他光谱转换方法的效果。DFA成功地将绿色样品与烘烤的样品区分开，但是，所有样品的联合分析无法区分烘烤量。另一方面，DFA成功地将焙烧水平与样品水平区分开，并通过100％准确的聚集层次聚类得到了加强。YSI首先用于NIR信号处理，并且能够检测到轻度烘焙在所有烘焙级别中最稳定。未来的研究应集中在DFA的应用方面，以分析其他光谱转换方法的效果。DFA成功地在样品级别上区分了烘烤级别，并通过100％准确的聚集层次聚类得到了加强。YSI首先用于NIR信号处理，并且能够检测到轻度烘焙在所有烘焙级别中最稳定。未来的研究应集中在DFA的应用方面，以分析其他光谱转换方法的效果。DFA成功地在样品级别上区分了烘烤级别，并通过100％准确的聚集层次聚类得到了加强。YSI首先用于NIR信号处理，并且能够检测到轻度烘焙在所有烘焙级别中最稳定。未来的研究应集中在DFA的应用方面，以分析其他光谱转换方法的效果。