Modules & Components

CannaSpec

Real-Time CBD/THC Reader

In-line sensors are at the forefront of Process Analytical Technology for monitoring and control of chemical manufacturing processes to optimize process performance and efficiency. Usually, these sensors require significant capital and time to develop and achieve ideal operation under very specific manufacturing conditions.

Off-line HPLC analysis is the current industry standard for monitoring process conditions and quality, which usually comes at hours if not days of turn-around time resulting in possible off-spec material that either needs to be reprocessed or wasted. CannaSpec has been developed specifically for Hemp and Cannabis process streams to provide reliable and robust real-time monitoring of CBD and THC concentrations in a cost-effective package.

The Ease
CannaSpec allows for real-time remote monitoring of THC and CBD concentration in many different process streams. The sensors can be integrated into process control loops to enable process automation. 2” tri-clamp fittings make it easy to integrate or retrofit into existing process equipment. No need to stop processing or encounter potentially dangerous materials during sampling procedures.

The Performance
CannaSpec has been calibrated to an HPLC method that was specifically designed to accurately measure THC levels to < 800 ppm and CBD levels as high as 80%. This makes CannaSpec especially well suited for THC remediation processes where reducing errors in sampling and turn-around time can have significant cost benefits by achieving optimal process times increasing throughput and product quality. Real-time feedback enables the user to adjust during processing to ensure optimal process performance and adapt to process deviations.

The Science
CannaSpec utilizes Near Infrared (NIR) spectroscopy. This relies on emitting a light source of a specific band of wavelengths into the process fluid. The chemical components vibrate and absorb unique wavelengths depending on their structure. NIR relies on even more subtle vibrational overtones and combinational vibrations, which results in complex absorbance spectra. This spectra data is then preprocessed and fit to a multivariate regression algorithm to generate a quantitative model, correlating in-line spectra to data measured off-line by HPLC. Through thousands of data points in numerous process streams and conditions, a robust calibration is developed with an R2 > 0.98.