Quantitative Caffeine Analysis in Robusta Coffee Utilizing Amperometric Biosensing Technology

V. A. Rosandi, L. Umar, R. N. Setiadi, Ari Sulistyo Rini, Erwin Erwin, Yanuar Yanuar, T. Linda
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Abstract

Consuming caffeine in inappropriate amounts can disrupt various aspects, especially health. Controlling intake by knowing the caffeine levels in coffee is necessary to reduce the potential negative impacts. This research focuses on the detection of caffeine in Robusta coffee at two different concentrations (1:10 and 1:20 g/mL) and its relationship with yeast metabolism. An amperometric biosensor with a transimpedance amplifier to measure caffeine levels is used which has the advantages of sensitivity, cost-effectiveness, real time monitoring, biocompatibility, and reliable measurements. The data were statistically analyzed using ANOVA and visualized using Principal Component Analysis (PCA). The results revealed a concentration -dependent decrease in biosensor readings as caffeine levels increased (0.1, 0.5, 1, 1.5, and 2 mM), indicating caffeine's ability to inhibit yeast oxygen consumption and oxygen-dependent metabolic processes. The sensitivity of the biosensor in detecting caffeine is 36.66 mV/mM. PCA uncovered complex patterns, relationships, and variations within the caffeine data. PC1 and PC2, the first two principal components, collectively explained 86.3% of the data's variance. Eigenvalues for both PCs were greater than 1, highlighting their significance in understanding the dataset's complexity. This research enhances our understanding of caffeine content in Robusta coffee and its effects on yeast metabolism, providing valuable insights for the coffee industry. This use of yeast biosensors offers efficiency, and adaptability that make that biosensor valuable in a variety of scientific and industrial contexts.
利用安培生物传感技术定量分析罗布斯塔咖啡中的咖啡因
摄入过量的咖啡因会破坏各方面的健康,尤其是健康。通过了解咖啡中的咖啡因含量来控制摄入量,对减少潜在的负面影响很有必要。本研究的重点是检测罗布斯塔咖啡中两种不同浓度(1:10 和 1:20 g/mL)的咖啡因及其与酵母代谢的关系。该传感器具有灵敏度高、成本效益高、可实时监测、生物相容性好和测量可靠等优点。使用方差分析对数据进行了统计分析,并使用主成分分析(PCA)对数据进行了可视化处理。结果表明,随着咖啡因含量的增加(0.1、0.5、1、1.5 和 2 mM),生物传感器读数随浓度而下降,这表明咖啡因能够抑制酵母的耗氧量和依赖氧气的代谢过程。生物传感器检测咖啡因的灵敏度为 36.66 mV/mM。PCA 发现了咖啡因数据中复杂的模式、关系和变化。前两个主成分 PC1 和 PC2 共解释了 86.3% 的数据方差。这两个主成分的特征值都大于 1,突出了它们在理解数据集复杂性方面的重要性。这项研究加深了我们对罗布斯塔咖啡中咖啡因含量及其对酵母新陈代谢影响的了解,为咖啡行业提供了宝贵的见解。酵母生物传感器的高效性和适应性使其在各种科学和工业环境中都具有重要价值。
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