An investigation into the use of calculating the first derivative of absorbance spectra as a tool for forensic fibre analysis

Wiggins, Kenneth G.; Palmer, Ray; Hutchinson, W. B.; Drummond, P.

doi:10.1016/j.scijus.2006.11.001

Cited by 57 publications

(24 citation statements)

References 6 publications

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“…Generally, 1-order derivative stands for the slope of the reflectance spectra, and 2-order derivative stands for the curvature of the reflectance spectra [56]. Nonetheless, although 1-order derivative and 2-order derivative can increase the spectral difference, a very large difference still exists between the original spectral curve and 1-order derivative/2-order derivative, which may result to the omission of useful information.…”

Section: Discussionmentioning

confidence: 99%

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

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Visible and near-infrared (VIS-NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R 2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R 2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

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Section: Discussionmentioning

confidence: 99%

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

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“…In addition, improvements in computer hardware and software used in MSP control and data acquisition have allowed the application of the first derivative to spectral data, which provided it is used with caution, can assist in discriminating between fibres with very similar, but different dyes [28]. Grieve and Biermann, [8] also pointed out that even with improvements in MSP technology, further discrimination between fibres is still possible using thin layer chromatography (TLC).…”

Section: Discussionmentioning

confidence: 99%

The prevalence of two ‘commonly’ encountered synthetic target fibres within a large urban environment

et al. 2015

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“…Comparison of spectra routinely involves overlaying these to determine whether they match with respect to peak positions and general spectral shape. Calculating the first derivative of absorbance spectra could also be used [22]. Nevertheless, these approaches do not provide information about the value of evidence.…”

Section: Comparison Of Fibresmentioning

confidence: 99%

Evaluation of the evidential value of physicochemical data by a Bayesian network approach

Zadora

2010

Journal of Chemometrics

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The growing interest in applications of Bayesian networks (BNs) in forensic science raises the question of whether BN could be used in forensic practice for the evaluation of results from physicochemical analysis of a limited number of observations from flammable liquids (weathered kerosene and diesel fuel) by automated thermal desorption gas chromatography mass spectrometry (ATD-GC/MS), car paints by pyrolysis gas chromatography mass spectrometry (Py-GC/MS) and fibres by microspectrophotometry (MSP) in the visible (VIS) range. Therefore, various simple BN models, which allow the evaluation of both discrete and continuous types of data, were studied in order to address questions raised by the representatives of the administration of justice, concerning the identification and classification of objects into certain categories and/or the association between two items. The results of the evaluation performed by BN models were expressed in the form of a likelihood ratio, which is a well-documented measure of evidential value in the forensic field. From the results obtained, it can be concluded that BN models seem to be promising tool for evaluating physicochemical data.

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An investigation into the use of calculating the first derivative of absorbance spectra as a tool for forensic fibre analysis

Cited by 57 publications

References 6 publications

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

The prevalence of two ‘commonly’ encountered synthetic target fibres within a large urban environment

Evaluation of the evidential value of physicochemical data by a Bayesian network approach

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