Chicken meat has a high nutrient content. However, its quality is easy to be degraded. The degradation is normally characterized by the formation of metabolite gases (NH3 and H2S) as deterioration indicators. Sensors detect phenomena better than human senses. This study aimed to classify meat quality based on gas formation during meat storage. In addition, a kinetics model of gas changes was determined. The gases were detected using a set of equipment consisting of Raspberry Pi and Metal-Oxide-Semiconductor (MOS) gas sensors. Samples were put in a 10 x 10 x 10 (cm) black container. MOS sensors were put inside the box to detect the gases at room temperature for 24 hours, with data collection being recorded every hour. Obtained data were then analyzed using Principle Component Analysis (PCA) for quality classification. The study showed that the quality of chicken meat was classified into three groups with a total variance of more than 95%. PC1 explained 88.2%, and PC2 explained 9.0%. The constant rate of H2S and NH3 changes followed the first-order kinetics with a constant rate of 0.2641 and 0.2925, respectively. The equation for H2S and NH3 changes were Ct=1.70 e0.2641 t and Ct=1.00 e0.2925 t, respectively. Keywords: Chicken meat, Freshness, H2S gas, NH3 gas, Sensor

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Chicken meat has a high nutritional content that makes its freshness rapidly deteriorates. A color change characterized the degradation. Color changes could influence the consumer perception toward food quality. Human perception and evaluation of color are often subjective. Sensors can provide better detection accuracy toward this phenomenon than the human senses. This study aims to determine the change of color attribute of chicken breast meat kinetically and classify meat quality based on color changes during meat storage using Principal Component Analysis (PCA). The experiment was performed with equipment consisting of a Raspberry Pi, Arduino, and a TCS 3200 color sensor. The meat sample was stored in a dark-colored container along with the sensor for 24 hours storage at room temperature. The measurement was done every hour in three replications. Color data from sensor readings in the frequency form was then converted into RGB (Red, Green, Blue) values and finally to L*, a*, b* values during the experiment. The data obtained was sent to the database for kinetic analysis and quality classification using PCA. It was found that the change of color attribute of Chroma (C), Hue Angle (Ho), Color Difference with True Red (DE), and Color Difference (AE) followed zero-order or first-order kinetics reactions. While from the PCA resulted, two chicken meat quality classes, PC 1, explained 85.4%, and PC 2 explained 12.5%.