Integrating Quantitative Methods and Digital Sensors to Enhance Product Quality in the Gummy Candy Industry

The quality of food products is essential for consumer safety and the perception of the product’s value. In production processes, traditional quality control methods are often carried out retrospectively, limiting the possibility of immediately interventions and causing inefficiencies. The Industrial Internet of Things (IIoT) and machine learning offer an innovative approach, enabling real-time data collection and processing to optimize several parameters that influence the final product's quality. This study discusses how these technologies can be applied to the gummy candy sector, where variables such as ingredient quantity, temperature, moisture and viscosity are critical parameters. Their precise control ensures standard products that meet consumer expectations. Smart sensors and artificial vision systems, implemented along the production lines, enable continuous monitoring, while predictive algorithms identify and correct deviations from optimal parameters. The integration of these tools enhances product quality, reduces waste, and optimizes the production process, also supporting the development of new and innovative formulations. Moreover, improved traceability enables detailed monitoring of the entire production cycle, ensuring greater food safety, regulatory compliance, and a faster response to potential issues. Real time traceability also facilitates supply chain management, improving logistical efficiency and increasing consumer trust through greater transparency in the final product.