In this study, gelatins from black-bone chicken feet and skin (BCFG and BCSG) were extracted using different NaOH concentrations, and their physicochemical properties were characterized and compared to commercial bovine gelatin (BG). It was found that the yield of BCFG was higher than BCSG, however, it contained higher amount of ash. All studied gelatins were composed of two distinct a-chains, while b-chain and c-chain were not present. The BCFG and BCSG were found to have lower pH, lower hydroxyproline content and lower thermal stability, but higher gel strength as compared with the BG. The colors of BCSG and BCSG were slightly darker than BG. The NaOH concentration did not show strong influence on physicochemical properties of the extracted gelatins, however, thermal stability and gel strength of BCSG tended to decrease with increasing of NaOH concentration. These findings suggested that black-bone chicken feet and skin could be a great source for the production of gelatin.

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Nowadays, agricultural and food technology require the integration of advanced computer technology and sophisticated computational approach for enhancing the characterization and quality of produces and their products. Huge amount of data was gathered and it needs to be processed and analyzed with confidence that the useful information is being extracted accurately. Therefore, sophisticated computing methods are the most important parts of the overall system. Particle filtering has been recognized as an efficient tool to deliver the accurate state model prediction especially in highly nonlinear and non-Gaussian dynamical systems. In this work, a particle filter (PF) was designed for parameter estimation of respiratory of spinach storage under modified atmosphere. The Michaelis-Menten model was examined in this work for spinach respiratory mechanism under different atmospheric storage conditions to illustrate the performance of the method. The estimating results from the PF were compared to the conventional estimation techniques widely used in literature. From the experimental and computational results, we found that the particle filtering method delivers higher accuracy, outperforming the existing conventional regression method and the extended Kalman filter.

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