In this work, an innovative screening platform is developed and validated for the on site detection of cannabinoids in hemp seed oil, for food safety control of commercial products. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that permits processing samples in a rapid and accurate way and to obtain in a single click the early detection of a residual amount of cannabinoids in oil, including cannabidiol (CBD), the psychoactive Δ9-tetrahydrocannabinol (THC) and the Δ9-tetrahydrocannabinolic acid (THCA). Simulated samples were realized to instruct the platform and prediction models were developed by chemometric analysis of the NIR spectra using partial least square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and on websites. Validation of the system was achieved by comparing results with those obtained from GC-MS analyses and a good correlation was observed.
Development of a "single-click" analytical platform for the detection of cannabinoids in hemp seed oil / Risoluti, R.; Gullifa, G.; Battistini, A.; Materazzi, S.. - In: RSC ADVANCES. - ISSN 2046-2069. - 10:71(2020), pp. 43394-43399. [10.1039/d0ra07142k]
Development of a "single-click" analytical platform for the detection of cannabinoids in hemp seed oil
Risoluti R.Primo
;Gullifa G.;Materazzi S.
Ultimo
2020
Abstract
In this work, an innovative screening platform is developed and validated for the on site detection of cannabinoids in hemp seed oil, for food safety control of commercial products. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that permits processing samples in a rapid and accurate way and to obtain in a single click the early detection of a residual amount of cannabinoids in oil, including cannabidiol (CBD), the psychoactive Δ9-tetrahydrocannabinol (THC) and the Δ9-tetrahydrocannabinolic acid (THCA). Simulated samples were realized to instruct the platform and prediction models were developed by chemometric analysis of the NIR spectra using partial least square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and on websites. Validation of the system was achieved by comparing results with those obtained from GC-MS analyses and a good correlation was observed.File | Dimensione | Formato | |
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