Agroindustrial and food processing residues valorization for solid-state fermentation processes: A case for optimizing the co-production of hydrolytic enzymes.

In the pursuit of sustainability, managing agro-industrial and food processing residues (AFR) efficiently is crucial. This study proposes a systematic approach to convert AFR into valuable products via solid-state fermentation (SSF). Using fungal enzyme production as a case study, this adaptable methodology suits any SSF bioprocess. Initially, AFR's physicochemical properties were evaluated to assess their feasible use as carbon sources and solid matrices for SSF. Then, five strains were screened for their capability to produce enzymes (Xylanase, X; pectinase, P; cellulase, C). Apple pomace (AP) and brewery spent grain (BSG) with Aspergillus sp. (strain G5) were selected. Subsequent steps involved a two-phase statistical approach, identifying critical factors and optimizing them. Process conditions were screened using a Plackett-Burman design, narrowing critical variables to three (BSG/AP, pH, humidity). Response Surface Methodology (Central Composite Design) further optimized these factors for co-synthesis of X, P, and C. The humidity had the most significant effect on the three responses. The optimum conditions depended on each enzyme and were further validated to maximize either X, P or C. The obtained extracts were used for pectin extraction from orange peels. The extract containing primarily xylanase (X = 582.39, P = 22.86, C = 26.10 U mL-1) showed major pectin yield recovery (12.33 ± 0.53%) and it was obtained using the optimal settings of BSG/AP (81/19), humidity (50.40%), and pH (4.58). The findings will enable adjusting process conditions to obtain enzymatic cocktails with a tailored composition for specific applications.

Recycling and Updating an Educational Robot Manipulator with Open-Hardware-Architecture.

This article presents a methodology to recycle and upgrade a 4-DOF educational robot manipulator with a gripper. The robot is upgraded by providing it an artificial vision that allows obtaining the position and shape of objects collected by it. A low-cost and open-source hardware solution is also proposed to achieve motion control of the robot through a decentralized control scheme. The robot joints are actuated through five direct current motors coupled to optical encoders. Each encoder signal is fed to a proportional integral derivative controller with anti-windup that employs the motor velocity provided by a state observer. The motion controller works with only two open-architecture Arduino Mega boards, which carry out data acquisition of the optical encoder signals. MATLAB-Simulink is used to implement the controller as well as a friendly graphical interface, which allows the user to interact with the manipulator. The communication between the Arduino boards and MATLAB-Simulink is performed in real-time utilizing the Arduino IO Toolbox. Through the proposed controller, the robot follows a trajectory to collect a desired object, avoiding its collision with other objects. This fact is verified through a set of experiments presented in the paper.