Browsing by Person "Munyendo, Leah"

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Monitoring a coffee roasting process based on near‐infrared and raman spectroscopy coupled with chemometrics
(2025) Munyendo, Leah; Schuster, Katharina; Armbruster, Wolfgang; Babor, Majharulislam; Njoroge, Daniel; Zhang, Yanyan; von Wrochem, Almut; Schaum, Alexander; Hitzmann, Bernd
Roasting is a fundamental step in coffee processing, where complex reactions form chemical compounds related to the coffee flavor and its health‐beneficial effects. These reactions occur on various time scales depending on the roasting conditions. To monitor the process and ensure reproducibility, the study proposes simple and fast techniques based on spectroscopy. This work uses analytical tools based on near‐infrared (NIR) and Raman spectroscopy to monitor 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 different roasting times. The spectra of the samples were taken using the spectrometers and modeled by the k‐nearest neighbor regression (KNR), partial least squares regression (PLSR), and multiple linear regression (MLR) to predict concentrations from the spectral data sets. For NIR spectra, all the models 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 < 9.649%) and high coefficient of determination ( R 2 > 0.915). The predictions for ABTS radical scavenging activity were reasonably good. On the contrary, the models poorly predicted the caffeine and total phenolic content (TPC). Similarly, all the models based on the Raman spectra provided good prediction accuracies for monitoring the dynamics of chlorogenic acid, trigonelline, and DPPH radical scavenging activity (pRMSEP < 7.849% and R 2 > 0.944). The results for ABTS radical scavenging activity, caffeine, and TPC were similar to those of NIR spectra. These findings demonstrate the potential of Raman and NIR spectroscopy methods in tracking chemical changes in coffee during roasting. By doing so, it may be possible to control the quality of coffee in terms of its aroma, flavor, and roast level.
Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder
(2023) Munyendo, Leah; Njoroge, Daniel; Zhang, Yanyan; Hitzmann, Bernd
Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) 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. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.
The potential of spectroscopic techniques in coffee analysis - a review
(2021) Munyendo, Leah; Njoroge, Daniel; Hitzmann, Bernd
This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.