A Multimodal Optical Sensing System for Automated and Intelligent Food Safety Inspection

Abstract

Highlights A multimodal optical sensing system was developed for food safety applications. The prototype system can conduct dual-band Raman spectroscopy at 785 and 1064 nm. The system can automatically measure samples in Petri dishes or well plates. The system with AI software is promising for identifying species of foodborne bacteria. Abstract. A novel multimodal optical sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of compact point lasers and dispersive spectrometers at 785 and 1064 nm to realize dual-band Raman spectroscopy and imaging, which is suitable to measure samples generating low- and high-fluorescence interference signals, respectively. Automated spectral acquisition can be performed using a direct-drive XY moving stage for solid, powder, and liquid samples placed in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two miniature color cameras are used for machine vision measurements of samples in the Petri dishes using different combinations of illuminations and imaging modalities (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to implement automated sample counting, positioning, sampling, and synchronization functions. System software was developed using LabVIEW with integrated artificial intelligence functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria, including Bacillus cereus, E. coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella spp.. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra automatically collected from 222 bacterial colonies of the five species grown on nutrient nonselective agar in 90 mm Petri dishes. The entire system was built on a 30×45 cm2 breadboard, enabling it compact and portable and its use for field and on-site biological and chemical food safety inspection in regulatory and industrial applications. Keywords: Artificial intelligence, Automated sampling, Bacteria, Food safety, Machine learning, Machine vision, Raman, Sensing.