ArticleACS omega2026
Design and Research of an Intelligent Digestion System Based on Machine Vision and Full-Process Control.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research proposes an intelligent digestion system that integrates full-process automation with machine vision technology, aiming to overcome the limitations of operator-dependent procedures and subjective end point assessment in traditional digestion processes. The system employs collaborative robotic arms, electric grippers, and high-precision peristaltic pumps to enable fully automated acid addition, heating, and volume calibration operations, with integrated safety features including an acid mist absorption unit and real-time liquid level monitoring. The end point determination module utilizes a machine vision model in conjunction with an optical turbidity detection unit to achieve dual-mode verification of digestion completion, where the turbidity measurement subsystem attains a precision of ±0.5 NTU (nephelometric turbidity units). This system enables end point validation through analysis of liquid transmittance characteristics combined with visual feature recognition from real-time imaging. The system supports simultaneous batch processing of up to 24 samples and is equipped with an integrated acid vapor condensation recovery unit. Upon completion of the digestion process, the system automatically switches to standby mode. Comparative evaluation between the intelligent digestion system and conventional manual operation demonstrates that the automated system achieves equivalent completeness of digestion, while significantly reducing processing time and reagent consumption. A quantitative analysis of the characteristic elements (e.g., Al, Ca, Cu, Fe, Li, and Na) in the resulting digestates was conducted, and it was found that there was excellent agreement with the manual method. This serves to further validate the technical reliability and analytical consistency of the proposed system. In conclusion, the system demonstrates robust adaptability to end point detection requirements for complex matrices such as catalysts, providing a reliable smart automation solution for sample pretreatment in environmental monitoring and pharmaceutical applications.
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Registered trials
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