No abstract

Bacterial cellulose is an exopolysaccharide that has a higher level of purity compared to cellulose from plants. Bacterial Cellulose (BC) is widely used for various uses so that it requires certain initial conditions, one of which is thickness. During the fermentation process, cellulose will be secreted into the medium to form BC sheets and visually visible over the time period. The aim of this research was to study the relationship between variables that influence during the fermentation and to fit the kinetic model of the BC thickness formation using image processing approach during the fermentation process. A USB camera was placed in front of the fermenter to capture the formation of BC thickness. Python programming language was used to process the image and calculated the thickness of the BC sheet from the beginning to the end of the fermentation process. Several supporting parameters were observed by placing the turbidity, pH, and medium temperature sensors. Observations were made in real time with a range of data collection every 15 mins during fermentation. The highest correlation value was obtained from the relationship between time and thickness. The fermentation process is divided into 2 clusters, a change in cluster occurs at the 61st hour. The model that describes the relationship between time and thickness was the Gompertz model.

The thickness of the bacterial cellulose (BC) sheet is an important parameter that determines the end of the fermentation process. During the fermentation process, BC sheets produced will be visually visible. Commonly, the end of the fermentation process is determined using manual observation based on fermentation time and approximation of BC thickness which are subjective and susceptible to error especially for routine and large samples. To overcome those limitations, a new approach for accurate and real-time observation system to monitor the formation of BC thickness is developed in this research. The system can perform several tasks from image capturing and processing, image conversion to BC thickness, until data collection. The system is also able to send notification of fermentation conditions including BC thickness through the email system during the fermentation process regularly. The system consists of USB camera to capture image, the Python programming language to process image, and Raspberry Pi 3 installed with MySQL database to store the BC thickness data. Thickness calculation algorithm is compiled using python programming language and has succeeded in calculating various thickness of BC sheets during the fermentation process every 15 minutes for 8 days. The BC thickness data is automatically sent to the MySQL database and at the same time sent to user’s email.

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