Liquid trace detection and identification by spectral reflectance model

Aharoni, Ran; Elisha, Shmuel; Peri, David; Kendler, Shai

doi:10.1117/1.oe.58.7.074104

Cited by 1 publication

(4 citation statements)

References 22 publications

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“…Figure 5 and Ref. [36] exemplify that fitting the best correlated LM result to unknown dissemination results in high certainty identification [36]. Using this approach of forcing the LM over non-uniform coverage, we can circumvent the requirement of the different scattering models to characterize the morphological properties of the analyte and provide identification and also a good estimation of its coverage features.…”

Section: Non-uniform Layers: Diffraction and Scatteringmentioning

confidence: 91%

“…As demonstrated in Figures 3 and 4 for layers, and in Figure 5 and Ref. [36] for sprayed traces, the spectral scattering of different ET expresses in non-identical spectral responses, i.e., different peaks heights and locations. Figure 5 and Ref.…”

Section: Non-uniform Layers: Diffraction and Scatteringmentioning

confidence: 92%

“…Figure 5 presents measured and calculated diffuse reflection normalized spectra of PDMS, the solid black spectra were calculated using the LM presented in the previous section, and the red dashed curves represent measure spectra of laboratory Diffuse reflection measurement (solid black) of PDMS spray-on metalic surface, vs. LM (dashed red) (from [36]).…”

Section: Non-uniform Layers: Diffraction and Scatteringmentioning

confidence: 99%

“…Figure 5.Diffuse reflection measurement (solid black) of PDMS spray-on metalic surface, vs. LM (dashed red) (from[36]). …”

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confidence: 99%

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Reflectance Spectra Analysis Algorithms for the Characterization of Deposits and Condensed Traces on Surfaces

Aharoni¹,

Zuck²,

Peri³

et al. 2022

Colorimetry

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Identification of particulate matter and liquid spills contaminations is essential for many applications, such as forensics, agriculture, security, and environmental protection. For example, toxic industrial compounds deposition in the form of aerosols, or other residual contaminations, pose a secondary, long-lasting health concern due to resuspension and secondary evaporation. This chapter explores several approaches for employing diffuse reflectance spectroscopy in the mid-IR and SWIR to identify particles and films of materials in field conditions. Since the behavior of thin films and particles is more complex compared to absorption spectroscopy of pure compounds, due to the interactions with background materials, the use of physical models combined with statistically-based algorithms for material classification, provides a reliable and practical solution and will be presented.

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Section: Non-uniform Layers: Diffraction and Scatteringmentioning

confidence: 91%

Section: Non-uniform Layers: Diffraction and Scatteringmentioning

confidence: 92%

Section: Non-uniform Layers: Diffraction and Scatteringmentioning

confidence: 99%

“…Figure 5.Diffuse reflection measurement (solid black) of PDMS spray-on metalic surface, vs. LM (dashed red) (from[36]). …”

mentioning

confidence: 99%

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