Validation of a combined Fast blue BB and 4-Aminophenol colorimetric test for indication of Hemp-type and Marijuana-type cannabis

• NCJ Number: 307448
• Journal: Forensic Chemistry Volume: 31 Dated: December 2022
• Author(s): Alexander Acosta; Li Li; Mike Weaver; Ryan Capote; Jeannette Perr; José Almirall
• Date Published: December 2022
• Length: 29 pages
• Grant Number(s): DJO-NIJ-21-RO-0002; 2014-DNR-4918
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Interagency Collaboration to Support Forensic Science Research; NIJ Co-funding for the NSF Center for Advanced Research in Forensic Science (CARFS)

Annotation

This study describes the miniaturization of a 4-Aminophenol (4-AP) colorimetric reaction on a substrate to be used in combination with the previously reported Fast Blue BB (FBBB) test; the observations and statistical results suggest that FBBB and 4-AP may be used either individually or in combination to reliably indicate hemp and marijuana when the THC:CBD is above two.

Abstract

The authors report the miniaturization of a 4-Aminophenol (4-AP) colorimetric reaction on a substrate to be used in combination with the previously reported Fast Blue BB (FBBB) test. The observations and statistical results suggest that FBBB and 4-AP may be used either individually or in combination to reliably indicate hemp and marijuana when the THC:CBD is above two.  Two effective presumptive tests used to indicate hemp-type and marijuana-type cannabis are the FBBB and 4-AP colorimetric tests. Both tests use <50 µL of reagents to effectively indicate hemp and marijuana. The FBBB and 4-AP tests analyzed 99 authentic marijuana samples and 93 authentic hemp samples and the performance of both tests, individually and in combination, are presented here. Red, Green, and Blue (RGB) scores were obtained for the chromophores (and fluorophore in the case of FBBB) from magnified images of the reaction products on the substrate and Linear Discriminant Analysis (LDA) and Data Driven-Soft Independent Modeling of Class Analogies (dd-SIMCA) models were constructed using the RGB scores. The LDA results showed that FBBB and 4-AP are effective (>90 % correct classification) at classifying THC-rich marijuana (THC:CBD > 2) from hemp individually but have a slightly higher specificity when both tests are used in combination (greater than or equal to 95 % correct classification). Marijuana samples with a THC:CBD below two were considered outliers for the SIMCA models. However, sensitivity and specificity above 95 % were achieved with the SIMCA models when these samples were removed. (Published Abstract Provided)