Browsing by Person "Munyendo, Leah Masakhwe"

Now showing 1 - 1 of 1

Development of rapid analytical methods for coffee quality assessment: Spectroscopy and chemometrics approach
(2024) Munyendo, Leah Masakhwe; Hitzmann, Bernd; Zhang, Yanyan
The assessment of coffee quality is based on the physical characteristics (bean quality), chemical constituents, and cup quality. Different factors, including altitude, genetics, management conditions, presence of adulterants, roasting, geographical origin, processing methods, and storage, affect the coffee quality. To meet the consumers' expectations regarding quality, the development of fast, new, and advanced analytical techniques for assessing the factors affecting coffee quality is a central aspect. Therefore, this research aimed to develop spectroscopic techniques complemented with chemometrics for evaluating the factors affecting coffee quality. The first specific objective was to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted Arabica coffee and to determine a coffee’s geographical origin using near‐infrared (NIR) spectroscopy. Arabica coffee was adulterated with Robusta coffee or chicory at adulteration levels ranging from 2.5 % to 30 % in increments of 2.5 % at light, medium, and dark roast levels. Based on the results, all the samples adulterated with chicory were detectable by the autoencoder at all roast levels. For Robusta-adulterated samples, the detection was possible at adulteration levels above 7.5 % at medium and dark roasts. One can attribute the observations to potential differences in the chemical composition among the samples. Additionally, it was possible to differentiate coffee samples from different geographical origins. As a continuation of the first objective, the potential of NIR spectroscopy to quantify Robusta coffee or chicory in roasted Arabica coffee using different regression models constructed from the linear discriminant analysis (LDA) or principal component analysis (PCA) features was investigated. In addition, two classification methods (k-nearest neighbor regression (KNR) and LDA) were used. The regression models derived from LDA-extracted features exhibited better accuracies than those derived from PCA-extracted features. The two feature extraction methods exhibit differences in their working principle. PCA focuses on identifying the direction of maximum variance regardless of the adulteration levels. In contrast, LDA identifies the feature subspace that optimizes the separability of the classes (adulteration levels) and minimizes the variance within the class. Therefore, LDA extracted the features better than PCA, explaining the better performance of the regression models constructed from its features. The models provided satisfactory results with the coefficient of determination (R2) values above 0.92 for both the adulterants, indicating their efficiency in quantifying Robusta coffee or chicory in roasted Arabica coffee. For the classification methods, the LDA model performed better than KNR. Another focus of this doctoral research was to develop analytical tools based on Raman and NIR spectroscopy for real-time monitoring of the coffee roasting process by predicting chemical changes in coffee beans during roasting. Green coffee beans of Robusta and Arabica species were roasted at 240 °C for 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, and 29 minutes. Four process runs were performed for each coffee species. The spectra of the ground samples were taken using the two spectrometers and modeled by the KNR, partial least squares regression (PLSR), and multiple linear regression (MLR). All the models based on the NIR spectra provided satisfactory results for the prediction of chlorogenic acid, trigonelline, and DPPH radical scavenging activity with low relative root mean square error of prediction (pRMSEP < 9.469 %) and high R2 (> 0.916) values. Similarly, all the models based on the Raman spectra provided acceptable prediction accuracies for monitoring the dynamics of chlorogenic acid, trigonelline, and DPPH radical scavenging activity (pRMSEP < 7.849 % and R2> 0.944). In conclusion, this research proposes different approaches that would allow valuable decisions regarding coffee quality to be made quickly and efficiently. The study suggests using NIR spectroscopy to determine a coffee’s geographical origin and detect and quantify adulterants in roasted coffee. The findings reveal that the method could be a promising tool for routine coffee quality control applications in the coffee industry and other legal sectors. The study also proposes using different spectroscopic methods (NIR and Raman) to monitor a coffee roasting process. One can consider the presented approaches as essential steps toward optimizing the roasting process at an industrial scale as they permit instantaneously taking significant process decisions.