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Want to create a better cannabis cultivation facility while strengthening relationships with third-party testing laboratories?
Consider using a Fourier transform-near infrared (FT-NIR) spectrometer for mandatory compliance analyses.
Several laboratory studies and instrument vendor applications have been developed to address rapid quantitation of cannabinoid potency without destroying the sample, which is necessary in traditional chromatographic testing.
In this 2018 study, analytical chemist Carolina Sánchez-Carnerero Callado and colleagues analyzed 189 ground and dried cannabis samples of various chemovars using gas chromatography (GC), dispersive NIR and FT-NIR.
Using partial least squares (PLS) regression, they found that FT-NIR successfully predicted cannabinoid concentrations determined by GC analysis.
Application notes from Perkin Elmer and ThermoScientific, for example, have discussed similar benefits of this approach using both PLS and principal component analysis.
NIR and FT-NIR are chemometric methods that analyze samples’ light reflectance using a statistical algorithm derived from chromatographic data.
Where traditional chromatography requires time for sample preparation, separation and calculations – and destroys the sample in the process – the NIR methods require essentially no sample preparation, do not destroy the sample and yield a result within minutes.
This proves a most valuable resource for in-process assessments of a cannabis batch or harvest with minimal economic cost.
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It does not, however, replace the need for batch testing to assure compliance with regulatory requirements by an independent, third-party analytical laboratory.
NIR analysis is an ideal technology for cultivators; it does not require in-depth knowledge nor training in the complexities of chromatographic separation or interpretation.
NIR does not replace independent lab testing using traditional chromatography, but it can be very useful during processing.
Because NIR and FT-NIR are founded on chemometrics and at best provide predictions of cannabinoid concentration, the quality of data used to build the chemometric algorithm is critical.
Partnership between the cultivator and laboratory is most beneficial.
Where the laboratory can provide the cultivator data from traditional chromatographic methods – most typically HPLC for cannabinoid concentration – the same data can be used to build the NIR model, thus maximizing its ability to predict concentration.
NIR gained momentum in the 1990s – primarily within the food industry – and soon thereafter among pharmaceutical companies.
FT-NIR is not subject to stray light interference, provides higher resolution and better accuracy than non-dispersive NIR.
This is an ideal technology for research and development applications but should not be considered equivalent nor superior to traditional chromatographic methods.
Susan Audino, who holds a doctorate in chemistry, is a chemistry consultant and instructor for the American Association for Laboratory Accreditation. She is based in Delaware.