Utilizing artificial-intelligence image recognition systems to assist in the quality control analysis of 3D printing...

Chocolate is often used in 3D food printing, however in 3D food printing cold extrusion systems, chocolate often faces the issue of temperature-induced clumping. To address this texture alteration, the method of adding oleogels is employed. This study examines the impact of monoglycerides (MAG), sucrose fatty acid ester (SE) and hydroxypropyl methylcellulose (HPMC) oleogels on the thermal and textural properties of 3D printed white and dark chocolates. It compares the effects of adding MAG, SE, and HPMC to dark and white chocolate for 3D printing. Thermal analysis shows distinct melting points: white chocolate with MAG peaks at 29.53 °C, 32.46 °C and 37.08 °C, while dark chocolate with SE peaks at 29.79 °C and 31.78 °C. Texture analysis indicates that white chocolate with 2% SE is harder than with 2% MAG, correlating with their respective melting points. AI image recognition effectively identifies shape variations and defects, achieving recognition rates over 90% for ideal shapes and flagging incomplete extrusions and structural issues. These findings highlight 3D printing's potential in chocolate manufacturing for precise customization and quality control, supported by AI inspection systems. Besides, the method offers a simpler approach for future applications for non-thermal extrusion. Future research could optimize oleogel formulations and expand AI applications to enhance production efficiency and product consistency.