Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study

Autor(es)

Pereira, Jose Francielson Q. ^[1] ; Pimentel, Maria Fernanda ^[2] ; Amigo, Jose Manuel ^{[3, 4, 5]} ; Honorato, Ricardo S. ^[6]

Afiliação do(s) autor(es)

^[1] Univ Fed Pernambuco, Dept Fundamental Chem, Recife, PE - Brazil

^[2] Univ Fed Pernambuco, Dept Chem Engn, LITPEG, Av Arquitetura Cidade Univ, BR-50740540 Recife, PE - Brazil

^[3] Basque Fdn Sci, Ikerbasque, Bilbao 48011 - Spain

^[4] Univ Copenhagen, Dept Food Sci, Rolighedsvej 26, Frederiksberg - Denmark

^[5] Univ Basque Country, Dept Analyt Chem, UPV EHU, POB 644, Bilbao 48080 - Spain

^[6] Fed Police, Recife, PE - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY; v. 237, AUG 15 2020.
• Citações Web of Science: 6

Resumo

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 14/50951-4 - INCT 2014: Tecnologias Analíticas Avançadas
• Beneficiário: Celio Pasquini
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 08/57808-1 - Instituto Nacional de Ciências e Tecnologias Analíticas Avançadas - INCTAA
• Beneficiário: Celio Pasquini
• Modalidade de apoio: Auxílio à Pesquisa - Temático