TY - JOUR

T1 - Hierarchical method and hyperspectral images for classification of blood stains on colored and printed fabrics

AU - Pereira, José F. Q.

AU - Pimentel, Maria Fernanda

AU - Honorato, Ricardo S.

AU - Bro, Rasmus

PY - 2021

Y1 - 2021

N2 - This work describes the development of methodology based on the hierarchical soft classification method by combining multivariate analysis techniques and Hyperspectral Near Infrared Images (HSI-NIR) to confirm identification of bloodstains on colored and printed fabrics. The term hierarchical is used to designate that the classification is done sequentially on smaller parts of the data such as first splitting the data into human and non-human etc. Human Blood (HB) and Animal Blood (AB) stains and stains from different commercial products (Common False Positives-CFP) were deposited on ten different fabrics of two types (five synthetic and five cotton-based) and hyperspectral imagens were acquired. The best pre-processing techniques were spectral smoothing (Savitzky-Golay filter, 11 point), Standard Normal Variate (SNV), and Generalized Least Squared Weighted (GLSW) performed in that sequence, to reduce the influence of the fabric on the model. Principal Component Analysis (PCA) models for samples prepared on synthetic fabrics demonstrated a great potential as a filter in discriminating blood samples from common false positives than the models built for samples prepared on cotton fabrics. This was done in a SIMCA-like fashion. The Partial Least Squared Discriminant Analysis (PLS-DA) model was used only to separate HB from AB samples for samples prepared on synthetic fabrics. For the samples prepared on cotton fabric, PLS-DA was also needed to discriminate some CFP from blood samples. PCA and PLS-DA were combined in a hierarchical structure to result in a single aggregated soft classification model. The hierarchical classification model built by a fusion of PCA and PLS-DA showed 100% sensitivity and 98% specificity in distinguishing between HB and Animal Blood and False Positive Samples in synthetic fabric. For stains prepared on cotton fabrics, the hierarchical model showed 95% sensitivity and 98% specificity.

AB - This work describes the development of methodology based on the hierarchical soft classification method by combining multivariate analysis techniques and Hyperspectral Near Infrared Images (HSI-NIR) to confirm identification of bloodstains on colored and printed fabrics. The term hierarchical is used to designate that the classification is done sequentially on smaller parts of the data such as first splitting the data into human and non-human etc. Human Blood (HB) and Animal Blood (AB) stains and stains from different commercial products (Common False Positives-CFP) were deposited on ten different fabrics of two types (five synthetic and five cotton-based) and hyperspectral imagens were acquired. The best pre-processing techniques were spectral smoothing (Savitzky-Golay filter, 11 point), Standard Normal Variate (SNV), and Generalized Least Squared Weighted (GLSW) performed in that sequence, to reduce the influence of the fabric on the model. Principal Component Analysis (PCA) models for samples prepared on synthetic fabrics demonstrated a great potential as a filter in discriminating blood samples from common false positives than the models built for samples prepared on cotton fabrics. This was done in a SIMCA-like fashion. The Partial Least Squared Discriminant Analysis (PLS-DA) model was used only to separate HB from AB samples for samples prepared on synthetic fabrics. For the samples prepared on cotton fabric, PLS-DA was also needed to discriminate some CFP from blood samples. PCA and PLS-DA were combined in a hierarchical structure to result in a single aggregated soft classification model. The hierarchical classification model built by a fusion of PCA and PLS-DA showed 100% sensitivity and 98% specificity in distinguishing between HB and Animal Blood and False Positive Samples in synthetic fabric. For stains prepared on cotton fabrics, the hierarchical model showed 95% sensitivity and 98% specificity.

KW - Blood stains

KW - Fabric

KW - Hierarchical model

KW - Hyperspectral image

KW - Near infrared

U2 - 10.1016/j.chemolab.2021.104253

DO - 10.1016/j.chemolab.2021.104253

M3 - Journal article

AN - SCOPUS:85100736154

VL - 210

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

SN - 0169-7439

M1 - 104253

ER -