The Environmental Sample Processor (ESP)

Sensors: Underwater Research of the Future (SURF Center)

Mechanical Engineer Doug Pargett and Electrical Engineer Scott Jensen work on integrating the third generation ESP into a long-range autonomous underwater vehicle. Photo: Todd Walsh © MBARI 2014

The MBARI Environmental Sample Processor—the ESP—provides on-site (in situ) collection and analysis of water samples from the ocean, identifying the presence of organisms and/or biological toxins.

The instrument uses an electromechanical fluidic system designed to autonomously collect and filter water samples. Then it either preserves and archives the sample for use after the ESP is recovered or directly applies molecular detection technology to investigate the biology of the sample in near real-time.

Why build an ESP?

Whether trying to identify microorganisms or understand why certain algae produce toxins, analysis typically requires collecting water samples at sea and returning them to a fully equipped laboratory. This can introduce a significant time lag between sample collection and informative results. Additionally, water collection can become very expensive if boats or ships are needed on a regular basis.

Thus in the early 2000s, MBARI initiated the development of the ESP and new techniques to allow the remote application of molecular probe technology. The hope was to provide a “persistent presence” in the ocean, bringing the laboratory to the sea, and thus reaping cost savings in the most expensive part of microbial oceanography—sample collection.

How the ESP Works

The ESP uses a complex water filtration and biochemical assay system to determine the presence and quantity of specific microorganisms.

ESP Web Portal

The Environmental Sample Processor web portal provides near-real time access to the results of molecular assays conducted at sea as well as data on the environmental conditions during sampling.

Genomic sensors

By utilizing cellular-level molecular biology, the ESP can assist multiple areas of oceanographic research and resource management.

Team

Kevin Gomes

Information Engineering Group Lead

John Ryan

Senior Research Specialist

Technology

Solving challenges
Taking the laboratory into the ocean
In Situ Ultraviolet Spectrophotometer
Midwater Respirometer System
Mobile flow cytometer
Enabling targeted sampling
Automated Video Event Detection
Gulper autonomous underwater vehicle
Advancing a persistent presence
Aerostat hotspot
Benthic event detectors
Benthic rover
Fault Prognostication
Long-range autonomous underwater vehicle Tethys
Marine “soundscape” for passive acoustic monitoring
Monterey Ocean-Bottom Broadband Seismometer
Shark Café camera
Vehicle Persistence
Wave Glider-based communications hotspot
Emerging and current tools
Communications
Aerostat hotspot
Wave Glider-based communications hotspot
Data management
Oceanographic Decision Support System
Spatial Temporal Oceanographic Query System (STOQS) Data
Video Annotation and Reference System
Instruments
Apex profiling floats
Benthic event detectors
Deep particle image velocimetry
Environmental Sample Processor
How the ESP Works
Genomic sensors
ESP Web Portal
The ESP in the news
Investigations of imaging for midwater autonomous platforms
Lagrangian sediment traps
Laser Raman Spectroscopy
Midwater Respirometer System
Mobile flow cytometer
Smart underwater connector
Power
Wave-Power Buoy
Vehicle technology
Benthic Rover
Gulper autonomous underwater vehicle
Imaging autonomous underwater vehicle
In Situ Ultraviolet Spectrophotometer
Seafloor mapping AUV
Long-range autonomous underwater vehicle Tethys
Mini remotely operated vehicle
ROV Doc Ricketts
ROV Ventana
Video
Automated Video Event Detection
Deep learning
SeeStar Imaging System
Shark Café camera
Video Annotation and Reference System
Technology publications
Technology transfer
Publications

Bowers, H.A., Marin, R.III, Birch, J.A., Scholin, C.A., and Doucette, G.J. (2016). Recovery and identification of Pseudo-nitzschia frustlules from natural samples acquired using the Environmental Sample Processor (ESP). Journal of Phycology, 52:135–140. http://doi.org/10.1111/jpy.12369

Herfort, L., Seaton, C., Wilkin, M., Roman, B., Preston, C., Marin, R., Seitz, K., Smith, M., Haynes, V., Scholin, C., Baptista, A., Simon, H. (2016). Use of continuous, real-time observations and model simulations to achieve autonomous, adaptive sampling of microbial processes with a robotic sampler.  Limnology and Oceanography: Methods, 14:50-67. http://doi.org/10.1002/lom3.10069

Full publications list