A high-speed, low cost, machine learning enhanced, hyperspectral imaging system for improved identification of microplastics

Microplastics are environmental pollutants ubiquitous in marine and freshwater environments and have been established to have deleterious impacts on aquatic life and human health. Preventing Microplastics from getting into waterways or removing it once it is there, is the mission of EPA’s Trash Free Waters program and, as the production of plastics continues to grow, EPA is looking for innovative technologies to efficiently detect and quantify microplastics in support of environmental monitoring and removal processes. An ideal technology would rapidly and inexpensively characterize environmental samples of microplastics in environmental matrices, such as water, wastewater, or soil, and utilize artificial intelligence or other methods to automate rapid (sub second) classification, identification, and quantification of microplastic plastics. A detection technology that supports low-cost integration with a number of platforms to a number of use cases, including remote field monitoring, handheld point of use assessments, and lab scale rapid throughput sample measurement, would be highly advantageous. Hyperspectral imaging (HSI), where imaging and spectral scanning are combined to provide spatially represented spectral information, is emerging as a non-destructive, real-time detection tool for industrial and environmental sensing and inspection processes. High equipment costs, low data throughput, and operational complexity make such systems largely inaccessible and impractical for wide use. It would be a transformational step if inexpensive, highspeed, continuous-range hyperspectral (350-2600nm) measurement systems could be implemented in microplastic measurement utility enhancing form-factors. The long-term goal of the proposed work is to realize such a hyperspectral measurement system, implementing machine learning algorithm on hardware, by utilizing a novel optical system designed using a set of low-cost components to achieve cost, size, and performance goals. The Phase I effort will focus on: an in-depth study of how the technology can and should be adapted for portable and high throughput, microscopy-based scanning microscopy in target use cases; image and spectral ML algorithm evaluation; detailed design development; and experimental testing/demonstration of modified measurement hardware/firmware with relevant materials targets. While initial market entry applications and customers will be researchers studying the prevalence and impacts of microplastics, significant additional applications and commercial opportunity exists within water/wastewater instrumentation industry markets as well as food processing inspection/safety and intuitional machine vision applications.