A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp

Siyu Yao; Christopher Ball; Gonzalo Miyagusuku-Cruzado; M Monica Giusti; Didem P Aykas; Luis E Rodriguez-Saona

doi:10.1016/j.talanta.2022.123559

A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp

Talanta. 2022 Sep 1:247:123559. doi: 10.1016/j.talanta.2022.123559. Epub 2022 May 21.

Authors

Siyu Yao¹, Christopher Ball², Gonzalo Miyagusuku-Cruzado³, M Monica Giusti³, Didem P Aykas⁴, Luis E Rodriguez-Saona⁵

Affiliations

¹ Rodriguez-Saona Vibrational Spectroscopy Lab, Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH, 43210, USA.
² ElectroScience Laboratory, College of Engineering, The Ohio State University, 1330 Kinnear Road, Columbus, OH, 43212, USA.
³ Giusti Phytochemicals Laboratory, Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH, 43210, USA.
⁴ Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, Aydin, 09100, Turkey.
⁵ Rodriguez-Saona Vibrational Spectroscopy Lab, Department of Food Science and Technology, The Ohio State University, Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH, 43210, USA. Electronic address: rodriguez-saona.1@osu.edu.

PMID: 35636366
DOI: 10.1016/j.talanta.2022.123559

Abstract

A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of Δ9-tetrahydrocannabinol (Δ⁹-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91-0.95) and a low standard error of prediction (SEP = 0.02-0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.

Keywords: Cannabidiol (CBD); In-situ quality assessment; Novel sensor; Partial least square regression (PLSR); Rapid IR screening; Δ9-tetrahydrocannabinol (Δ(9)-THC).

MeSH terms

Cannabidiol*
Cannabinoids* / analysis
Cannabis* / chemistry
Dronabinol
Spectroscopy, Near-Infrared / methods
Tandem Mass Spectrometry / methods

Substances

Cannabinoids
Cannabidiol
Dronabinol