**Abstract**

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.

**Keywords:** diffuse scattering, remote sensing, spectroscopy, surfaces, detection, mid wave infra-red, short wave infra-red, classification

#### **1. Introduction**

Spectroscopy is one of the foremost and main methods of characterizing materials of various states of matter—gas, liquid, solid, vapor, and aerosol. The need to remotely detect and identify residues, traces, contaminations, and small amounts of chemicals plays an important role in many fields, such as forensics and security [1], agriculture [2], food quality pharmaceuticals industry, climate research, and others.

Detection of surface contaminations and residues in a standoff manner enables scanning surface from a safe distance and with no physical contact with the sample, which may be hazardous or too sparse to necessitate wiping large area. Therefore, optical sensing can provide an immediate result, and it is favored over surface sampling techniques such as mass spectrometry techniques. Several studies and efforts utilizing probing techniques for ion-mobility spectrometry of explosives form surfaces through surface wiping [3] plasma ionization [4], desorption electrospray ionization from skin [5], and more. Non-contact preconcentration and ionization sampling methods, such as airflow-assisted ionization [6] and laserinduced breakdown spectroscopy [7], enable standoff detection of surface absorbed chemicals. For further reading on sampling techniques and explosive detection, refer to Tourné's review [8].

Fourier-transform infrared (FTIR) spectroscopy, coupled with attenuated total reflection (ATR), can be used to probe trace amounts of spores [9] or other particles on solid surfaces [10], or remotely by imaging [11, 12]. Active methods for powders detection, in which the investigated sample is artificially illuminated rather than using ambient light, were also applied in various spectral regions, such as THz [13, 14], but more commonly in lower wavelengths. The mid-IR region, the most common for chemical spectroscopy, was pushed forward by developing the quantum cascade laser (QCL) [15, 16]. Laser-assisted spectroscopy for explosive detection, and other particulated matters, was also employed by various optical techniques, such as photo-acoustics [1, 17, 18]. Using near-field optical microscopy, Craig et al. scanned surfaces with condensed residues by scanning QCL with a rapid acquisition time of 90 s per spectrum [19]. Explosive's particles and residues using scanning QCL microscopic hyperspectral imaging enabled a four-second acquisition time [20]. Advancing technology provides the means for an even shorter detection time in the range of <0.1 s per spectral cube of trace surface contaminants [21]. A similar method, i.e., imaging the diffusely scattered radiation from a light source, was also demonstrated using a CO2 laser [22]. The short wavelength of the IR region was also used for screening envelopes for traces of hazardous powders by hyperspectral imaging [23]. Multispectral imaging in the visible range was demonstrated for the detection and discrimination of bloodstains on cotton [24].

IR spectroscopy in the 7–14 μm band, i.e., mid-wave IR (MWIR, also termed long-wave IR; LWIR), is discussed in the first section of this chapter. This spectral region is used for inspecting molecular chemical information for gaseous and condensed matter. In condensed samples, MWIR spectroscopy is an analytical technique fitted for trace analysis, but it is non-penetrating, which can be affected by the surface morphology that scatters the light and affects the resulted spectrum. Short IR wavelengths (SWIR, 1–2.5 μm) spectroscopy, addressed in the second section of this chapter, is characterized by weaker absorption and larger penetration depth, resulting in diffuse reflection, allowing deeper investigation of the sample and revealing its physical and chemical structure. This is not an analytical method but provides information not only on the sample's surface but also on the sample's core compound and structure.

This chapter is divided into two main sections. The first section elaborates the issues of MWIR spectroscopy of surfaces and suggests a practical solution based on a physical model. The second part confronts the challenges of using SWIR for similar purposes and proposes a solution based on statistical learning.

#### **2. Reflection and scattering: MWIR**

The MWIR region, known as the 'molecular fingerprint region', reflects the molecular composition of most chemicals vib-rotational transitions, hence reviling the molecular structure by its unique spectral fingerprint. Due to its analytical capabilities, it was favored by spectroscopists for the investigation of the chemical properties of organic materials, and detection and identification needs. Acquiring spectrum for gaseous samples is quite straightforward—launching an optical beam through the sample results in spectral attenuation, i.e., absorbance, caused by the molecular transitions. The absence of back reflection is due to the lack of specific boundaries between materials with different refractive indices (n1 and n2). However, in a condensed matter, where the boundaries between materials are well defined such as in layers and particles, interference scattering and diffraction effect *Reflectance Spectra Analysis Algorithms for the Characterization of Deposits… DOI: http://dx.doi.org/10.5772/intechopen.101301*

the spectral scattering. Each of the following phenomena: interference, diffraction, and scattering, has its theory models and approximations that describe an interaction of light and matter. In fact, these three phenomena are manifestations of maxwell's equations, differ only by mathematical approximations, and therefore provide ambiguous results at certain conditions.

The simplest case is the reflection from a uniform layer, explained and demonstrated in the following sub-section, which has an exact solution and can be expanded to cases of non-uniform layers, such as traces and residues, as depicted in **Figure 1**. The upper figure illustrates a light beam that is specularly reflected from a flat layer. The lower figure presents a surface with residual contamination, which results in diffuse reflection. The lower figure presents the illumination of a contaminated surface. The surface is tilted such as the specular reflection component (in red), which can be orders of magnitude stronger than the diffuse reflection component, is directed away from the detector. Part of the diffuse reflection lobe, which originates mainly from residual traces, is directed to the detector (blue). This section explores the physics of MWIR reflection from a uniform and non-uniform coverage of surfaces and suggests methods for detecting and identifying traces and residues.

#### **2.1 Uniform layers: general**

As mentioned, the reflection of light from surfaces can be generally divided into specular reflection and diffuse reflection components, both illustrated in **Figure 1**.

#### **Figure 1.**

*Reflected light from residuals on a surface. The upper panel illustrates a uniform layer deposited on a surface and its specular reflection. The lower panel illustrates drops, particles and residuals on a surface and the diffuse reflection measurement. The black arrows represent the incident light direction.*

The upper panel of the figure presents specular reflection from a finite layer, where the reflected light is directed to a detector. In thick and highly absorbing layers, the measured spectral reflection is comprised exclusively by the layer's molecular properties, i.e., the absorption coefficient and refractive index as described by Fresnel equation [25]. When the absorption coefficient and the thickness (and the light coherence length) allow the substantial optical intensity to be back-reflected from the carrying surface and complete at least one round trip, interference effects are observed, as illustrated in **Figure 2**. The figure illustrates the tracing of an optical field hitting a finite smooth layer with incidence angle θ. The multiple reflections and refractions amplitudes are summed up and then squared to get the light intensity. Almost no surface is smooth enough to avoid the scattered diffuse reflection lobe (illustrated in **Figure 1** lower panel). This component, which obeys the Lambertian reflection law with highly rough surfaces, is much weaker and negligible for relatively smooth surfaces. In cases of a partial cover of a surface by an analyte, it is desired to avoid the specular reflection, which might be orders of magnitude stronger than the diffuse reflection of the analyte traces and therefore mask it, as illustrated in **Figure 1b** and discussed in Section 2.3.

**Figure 2** refers to the scenario illustrated in **Figure 1a**, and presents detailed optical ray tracing through a finite and uniform thickness layer. An optical beam is hitting a uniform interface of a transparent layer with parallel facets at incidence angle Ɵ. Inside the layer, the optical field suffers multiple internal reflections, which give rise to interference. The reflection of a non-absorbing layer is:

$$\mathbf{R} = \frac{\left(\sqrt{\mathbf{R}\_1} - \sqrt{\mathbf{R}\_2}\right)^2 + 4\sqrt{\mathbf{R}\_1}\sqrt{\mathbf{R}\_2}\sin^2(\mathbb{A}\_2)}{\left(1 - \sqrt{\mathbf{R}\_1}\sqrt{\mathbf{R}\_2}\right)^2 + 4\sqrt{\mathbf{R}\_1}\sqrt{\mathbf{R}\_2}\sin^2(\mathbb{A}\_2)}\tag{1}$$

where R1 and R2 are the intensity reflection coefficient from the facets (calculated by Fresnel relations) and the optical phase is δ = 2πn(λ)L cos(Ɵ)/λ for a layer with thickness L. It is clear that unlike absorption spectroscopy, which measures the attenuation directly, the interference described in Eq. (1) has a crucial impact on the reflected spectra. Generalizing Eq. (1) to the case of semi-absorbing material, for example, a nonvolatile liquid over a glass window, ceramic tile, metallic surface, or other casual flat surfaces, results in the following equation:

#### **Figure 2.**

*Ray tracing of a finite uniform layer. E0, ERef, ETrans are the incident, reflected, and transmitted fields, respectively.*

*Reflectance Spectra Analysis Algorithms for the Characterization of Deposits… DOI: http://dx.doi.org/10.5772/intechopen.101301*

$$\mathbf{R} = \frac{\left(\sqrt{\mathbf{R}\_1} - \sqrt{\mathbf{R}\_2} \mathbf{e}^{\mathrm{aL}\cos(\theta)}\right)^2 + 4\sqrt{\mathbf{R}\_1}\sqrt{\mathbf{R}\_2}\mathbf{e}^{\mathrm{aL}\cos(\theta)}\sin^2(\theta\_2)}{\left(1 - \sqrt{\mathbf{R}\_1}\sqrt{\mathbf{R}\_2}\mathbf{e}^{\mathrm{aL}\cos(\theta)}\right)^2 + 4\sqrt{\mathbf{R}\_1}\sqrt{\mathbf{R}\_2}\mathbf{e}^{\mathrm{aL}\cos(\theta)}\sin^2(\theta\_2)}\tag{2}$$

where α is the attenuation coefficient defined as 4πk(λ)/λ (λ is the wavelength, and k(λ) is the imaginary part of the refractive index), thus, the reflected spectrum is affected heavily by the layer thickness, as seen in **Figure 3**, showing the absorption of Polymethyl Methacrylate (PMMA) layer, and reflected spectrum from different layers of it. The inset of the figure shows the absorption coefficient, measured with ATR. The figure shows reflectance measurements of three different spin-coated layers of PMMA, measured with a spectrally scanning laser, according to the set-up depicted in **Figure 1**. The measured spectra cannot be precisely associated with the absorption coefficient, from the reasons described above (Eq. (2)). Significant differences between the three layers can be seen as the peak's location are shifts and change their shape. This figure accentuates the resulted differences of reflected spectra from the same material with different morphology (i.e., layer thickness). The thickness differences are just a few microns (layers thicknesses are 16, 20, 27 μm), indicated by low correlations of the measured signatures, as shown in the table given in the below figure.

#### **2.2 Uniform layers: experimental implementation**

As explained above, by knowing the optical properties of the layer and the carrying surface, one can calculate the reflected spectrum, using Eq. (2). This is exemplified in **Figure 4**, showing the real and imaginary parts of the refractive index of poly-dimethyl-siloxane (PDMS) on a metallic surface (**Figure 4a**), and the measured spectral reflection from 0.63 μm to 22 μm layers (**Figure 4b** and **c**). **Figure 4a** presents the measured ATR spectra of PDMS, where the refractive index (n) is calculated by Kramers-Kronig relations [26]. **Figure 4b** presents the reflected

#### **Figure 3.**

*Reflected spectra of PMMA layers. The inset depicts the absorption coefficient, and the three curves of the figure represent the reflected intensity from three different uniform layers. The table represents the correlation coefficients between the layers.*

**Figure 4.** *PDMS layers reflection spectrum. (a) Optical properties (n is calculated). (b) Thin layer. (c) Thick layer.*

spectrum of a thin layer that matches the absorption coefficient 'k' from **Figure 4a**. This layer is too thin compared to the wavelength, therefore interference effects are not observed, and the reflected spectrum correlates well with the absorption coefficient. **Figure 4c** presents the reflected spectrum from a layer having thickness more than twice the average wavelength. Consequently, an interference pattern appears at the edges of the acquired spectrum, where the absorption is negligible. Also, some deformation of the absorption around 9.5 μm is noticed. The interference fringes properties can be used for exact evaluation of the layer thickness by Eqs. (1) or (2)—the spacing between adjacent peaks (called 'free spectral range') is affected by the wavelength, thickness, incidence angle, and the refractive index, which are the components of the phase parameter δ from Eqs. (1) and (2). For a more accurate solution, one should account for the variation in the Snell law for absorbing medium interface, which may affect the form of Eq. (2) [27].

#### **2.3 Non-uniform layers: diffraction and scattering**

Disseminating liquid materials more efficiently can be achieved by spraying and drizzling, which cover larger areas with drops or droplets. Such dispersal processes usually result in size distribution similar to lognormal [28], which can be very wide and with standard deviation spread from a few microns to hundreds of microns. The size distribution of sprayed droplets has an important effect on the performance of agrochemical systems [29] and combustion engines [30] etc., mainly characterized by scattering and diffraction measurements of levitating particles. Mostly, the efforts are towards covering large volumes and surfaces with droplets for the highest efficiency of dissemination and surface coverage. The results are

#### *Reflectance Spectra Analysis Algorithms for the Characterization of Deposits… DOI: http://dx.doi.org/10.5772/intechopen.101301*

similar to the lower panel of **Figure 1**, illustrating residuals and traces on a planar surface. The surface is tilted to avoid the specular reflection component that might mask the diffuse reflection component, which can be orders of magnitude weaker.

Light scattering is an extensive and well understood physical phenomenon, originating from Maxwell equations in the form of the wave equation [31], and depends on many factors such as the wavelength of light, incidence angle, material properties of the scatterer (absorption and refraction), and geometric factors of scatterers and illumination. As such, many scattering theories and models were developed, each describes these phenomena under different conditions and assumptions. For example: (1) Mie theory (full name: Lorenz-Mie-Debye theory) describes general scattering by homogeneous, isotropic spheres with no size limits but is more commonly used where the scatterer size is comparable to the light wavelength [32], and have many further approximations for different sizes and shapes of scattering particles. (2) Rayleigh scattering theory (Rayleigh-Gans-Debye) describes scattering from particles smaller than the wavelength [33], and more. In cases where the dissemination process, and hence the resulting size distribution, is unknown, it is unclear how to choose the most suitable approximation. Moreover, in liquid spray sediment over a surface, the interaction between the surface and the droplets may dramatically change the size and geometry of the droplets, and each scattering model might result in a different solution. We should note that most of the interactions of light with matter are fundamentally the same, and all are described by Maxwell's equations. More specifically, interference is a basic outcome of these equations, and it is the cause of diffraction and scattering, which are all different manifestations of light interaction with matter, and the only difference is the approximation of different theories. Therefore, it is expected that scattering by a sphere and reflection by a slab are similar [34, 35]. Accordingly, it will be demonstrated that the above-presented layers model (LM) can be used in many realistic scenarios for the detection and identification of sprayed liquid on a surface. Similarly, it is suggested that unknown condensed residuals on a surface can be detected, identified, and quantified by a simple reflection model instead of a complicated specific scattering model.

**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
