Design and Fabrication of Fluid Bed Air Sample Coffee Roaster with Manual and Automated Control of Roasting and Roast Profile Identification Using Arduino Uno and Raspberry Pi Color Mapping
Abstract
With increasing coffee consumption, some local farmers struggled to meet the public’s demand. They lack quality profiling methods that align with local standards for coffee beans. Additionally, regular coffee roasters require 1-3 kilos of beans, creating challenges for small farmers. This study aims to help farmers assess and standardize bean quality using 50 grams of green coffee beans in the experimental coffee roaster machine, enabling accurate pricing of their products. The machine's design incorporates an Arduino Uno for real-time sensor data acquisition and a Raspberry Pi for automated control of the roasting process. The system allows manual and automated operation, giving farmers flexibility in controlling roast degree profiles (light, medium, and dark roast) and adjusting the time and temperature to match different bean characteristics. Color mapping, powered by the Raspberry Pi, ensures precise roast profiling by analyzing color changes during roasting. The automated control system enhances consistency by reliably regulating temperature, airflow, and roast time to achieve target profiles. Statistical analysis using a One-Way ANOVA and Tukey's HSD test confirmed the machine's consistency in achieving specific roast levels. The results showed that the coffee roaster performed consistently across three roasting levels, with significant effects on time, temperature, and moisture loss, as the Pvalue for the dependent variable was below the 0.05 significance level. Tukey's HSD indicated that the dependent variable differed significantly across the three roast levels, leading to the rejection of the null hypothesis and acceptance of the alternative hypothesis.
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