Identification Using Classification Analysis of Flunitrazepam in Necrophagous Larvae via Differential Pulse Voltammetry and Fluorescence Excitation-Emission Matrix (EEM) Spectroscopy

Lima, Leomir A. S. de; Morais, Camilo L. M.; Jales, Jéssica T.; Gama, Renata A.; Lemos, Sherlan G.; Lima, Kássio M. G.

doi:10.21577/0103-5053.20180139

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Journal of the Brazilian Chemical Society

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Articles • J. Braz. Chem. Soc. 29 (12) • Dec 2018 • https://doi.org/10.21577/0103-5053.20180139 copy

Identification Using Classification Analysis of Flunitrazepam in Necrophagous Larvae via Differential Pulse Voltammetry and Fluorescence Excitation-Emission Matrix (EEM) Spectroscopy

Authorship SCIMAGO INSTITUTIONS RANKINGS

The use of insects to identify drugs in a cadaver has often been explored in the field of entomotoxicology. There are accurate methods; however, they require a high cost and are very time-consuming. The objective of this study was to develop two methods based on differential pulse voltammetry (DPV) and fluorescence excitation-emission matrix (EEM) spectroscopy to classify necrophagous larvae (Chrysomya megacephala, C. albiceps, Lucilia sp. and Cochliomyia macellaria) containing flunitrazepam. The voltammograms of larval extract samples were analyzed by principal component analysis (PCA), successive projection algorithm (SPA) and genetic algorithm (GA): linear and quadratic discriminant analysis (LDA and QDA). The EEM fluorescence data from larval extract samples were analyzed by 2D and parallel factor analysis (PARAFAC) with LDA. These results suggest that DPV and EEM combined with chemometrics can be used as tools for the classification of flunitrazepam in fly larvae presenting innovative applications.

Keywords:
differential pulse voltammetry; fluorescence spectroscopy; flunitrazepam; entomotoxicology; necrophagous larvae

Algorithm	Control		Flunitrazepam		Algorithm	Control		Flunitrazepam
PCA-LDA	SENS	100	SENS	78.6	PCA-QDA	SENS	100	SENS	92.9
	SPEC	100	SPEC	78.6		SPEC	100	SPEC	100
	PPV	100	PPV	78.6		PPV	100	PPV	100
	NPV	100	NPV	78.6		NPV	100	NPV	93.3
	YOU	100	YOU	57.1		YOU	100	YOU	92.9
	LR(+)	0.0	LR(+)	0.0		LR(+)	0.0	LR(+)	0.0
	LR(-)	0.0	LR(-)	0.3		LR(-)	0.0	LR(-)	0.1
SPA-LDA	SENS	100	SENS	85.7	SPA-QDA	SENS	100	SENS	92.9
	SPEC	100	SPEC	85.7		SPEC	100	SPEC	100
	PPV	100	PPV	85.7		PPV	100	PPV	100
	NPV	100	NPV	85.7		NPV	100	NPV	93.3
	YOU	100	YOU	71.4		YOU	100	YOU	92.9
	LR(+)	0.0	LR(+)	0.0		LR(+)	0.0	LR(+)	0.0
	LR(-)	0.0	LR(-)	0.2		LR(-)	0.0	LR(-)	0.1
GA-LDA	SENS	100	SENS	78.6	GA-QDA	SENS	100	SENS	100
	SPEC	100	SPEC	100		SPEC	100	SPEC	100
	PPV	100	PPV	100		PPV	100	PPV	100
	NPV	100	NPV	82.4		NPV	100	NPV	100
	YOU	100	YOU	78.6		YOU	100	YOU	100
	LR(+)	0.0	LR(+)	0.0		LR(+)	0.0	LR(+)	0.0
	LR(-)	0.0	LR(-)	0.2		LR(-)	0.0	LR(-)	0.0

Classifier	Accuracy / %	Sensitivity / %	Specificity / %
LDA	60.0	60.0	60.0
QDA	50.0	20.0	80.0
SVM	50.0	40.0	60.0

Classifier	Accuracy / %	Sensitivity / %	Specificity / %
PARAFAC-LDA	50.0	40.0	60.0
2D-LDA	100	100	100

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[1] *e-mail: kassiolima@gmail.com