Miniaturized spectroscopy and AI-driven probes in food industry automation.

Spectroscopy is a rapidly advancing analytical technique, which is increasingly employed in the food industry as a non-destructive and rapid quality control tool. Based on spectral analysis and developed multivariate predictive models this technique is suitable for online and real-time monitoring of various food products. Integrated into in-line, on-line, or at-line systems, spectroscopy enables the monitoring of critical quality attributes, nutritional, bioactive and specific analyte molecules for enhancing product consistency and safety. Recent developments in spectroscopic instrumentation, coupled with machine learning algorithms, have further augmented its potential as a transformative technology in the automation and optimization of food production systems. Although it shows great potential for such applications there are still challenges in successful integration of spectroscopic techniques into food processing facilities. This could be done by system miniaturization and artificial intelligence modeling. In this review, the past and current knowledge of miniaturized spectroscopy have been summarized and presented. Emphasis was placed on an overview of inline miniaturized spectroscopy, design and architecture, application in food industry including process monitoring and control, advantages and limitations such as cost, models'' transferability, and instrument variations.