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This study uses a novel analysis methodology based on the Hierarchical Clustering Analysis (HCA) to determine the effectiveness of different preprocessing methods in minimizing undesired spectral variability in near infrared spectroscopy due to both the consecutive and repetitive acquisition of the spectrum and the sample temperature. Nine preprocessing methods and different combinations of them were evaluated in four case studies: reproducibility, repeatability, sample temperature, and combination of the before mentioned cases. Eighty-four spectra acquired on seven different hydrocarbon samples from catalytic conversion processes have been selected as the real case study to illustrate the potential of the mentioned methodology. The approach proposed allows a more detailed discriminatory analysis compared to the classical methods for comparing the between-class and the within-class variances, such as the Wilks’ lambda criterion, and hence constitutes a powerful tool to determine adequate spectral preprocessing strategies. This study also proves the potential of the discrimination analysis methodology as a general scheme to identify atypical behaviors either in the spectrum acquisition or in the measured samples.

This study uses a novel analysis methodology based on the Hierarchical Clustering Analysis (HCA) to determine the effectiveness of different preprocessing methods in minimizing undesired spectral variability in near infrared spectroscopy due to both the consecutive and repetitive acquisition of the spectrum and the sample temperature. Nine preprocessing methods and different combinations of them were evaluated in four case studies: reproducibility, repeatability, sample temperature, and combination of the before mentioned cases. Eighty-four spectra acquired on seven different hydrocarbon samples from catalytic conversion processes have been selected as the real case study to illustrate the potential of the mentioned methodology. The approach proposed allows a more detailed discriminatory analysis compared to the classical methods for comparing the between-class and the within-class variances, such as the Wilks’ lambda criterion, and hence constitutes a powerful tool to determine adequate spectral preprocessing strategies. This study also proves the potential of the discrimination analysis methodology as a general scheme to identify atypical behaviors either in the spectrum acquisition or in the measured samples.

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer–Beer–Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer–Beer–Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

Near infrared hyperspectral imaging technique has been used for adulteration detection in food samples for several years. However, the sensor cannot screen beyond a certain material thickness. This work studies a method to determine the penetration depth of near infrared radiations in the context of detection. The case of wheat flour in a polylactic acid sample holder is investigated. A sample holder is specially designed to have the wheat flour thickness vary from 0.5 to 3.5 mm. Hyperspectral images are acquired and the partial least squares regression method is used to determine the amount of polylactic acid in each spectrum. Partial least squares prediction results are interpreted using sensor considerations and the Kubelka–Munk theory. Thereafter, the Kubelka–Munk model is fitted and the penetration depth is determined for each wavelength using the reflectance profiles. Similarities between these results and partial least squares regression coefficients lead to the conclusion that partial least squares, combined with the near infrared spectra, is able to characterize the detection depth. The value is calculated by fitting two linear models on the partial least squares prediction results. As a result, 1.80 mm is found to be the detection depth, defined as the maximum thickness of wheat flour to ensure the detection of polylactic acid through the background. Reflectance profiles also show that the penetration depth is highly dependent on the wavelength. This study aimed at providing a method that could be used to evaluate the penetration depth and the detection depth in other contexts than a polylactic acid target in wheat flour.

Near infrared hyperspectral imaging technique has been used for adulteration detection in food samples for several years. However, the sensor cannot screen beyond a certain material thickness. This work studies a method to determine the penetration depth of near infrared radiations in the context of detection. The case of wheat flour in a polylactic acid sample holder is investigated. A sample holder is specially designed to have the wheat flour thickness vary from 0.5 to 3.5 mm. Hyperspectral images are acquired and the partial least squares regression method is used to determine the amount of polylactic acid in each spectrum. Partial least squares prediction results are interpreted using sensor considerations and the Kubelka–Munk theory. Thereafter, the Kubelka–Munk model is fitted and the penetration depth is determined for each wavelength using the reflectance profiles. Similarities between these results and partial least squares regression coefficients lead to the conclusion that partial least squares, combined with the near infrared spectra, is able to characterize the detection depth. The value is calculated by fitting two linear models on the partial least squares prediction results. As a result, 1.80 mm is found to be the detection depth, defined as the maximum thickness of wheat flour to ensure the detection of polylactic acid through the background. Reflectance profiles also show that the penetration depth is highly dependent on the wavelength. This study aimed at providing a method that could be used to evaluate the penetration depth and the detection depth in other contexts than a polylactic acid target in wheat flour.

This study aims to investigate the combination of speckle pattern analysis, polarization parameters, and chemometric tools to predict the optical absorption and scattering properties of materials. For this purpose, an optical setup based on light polarization and speckle measurements was developed, and turbid samples were measured at 405 and 660 nm. First, a backscattered polarized speckle acquisition was performed on a set of 41 samples with various scattering (${\mu}_s$μ_s) and absorbing (${{\mu}_a}$μ_a) coefficients. Then, several parameters were computed from the polarized speckle images, and prediction models were built using stepwise multiple linear regression. For scattering media, ${{\mu}_s}$μ_s was predicted with ${R^{2} = 0.9}$R^2=0.9 using two parameters. In the case of scattering and absorbing media, prediction results using two parameters were ${R^{2} = 0.62}$R^2=0.62 for ${{\mu}_s}$μ_s and ${R^{2} = 0.8}$R^2=0.8 for ${{\mu}_a}$μ_a. The overall results obtained in this research showed that the combination of speckle pattern analysis, polarization parameters, and chemometric tools to predict the optical bulk properties of materials show interesting promise.

This study was carried out to investigate the feasibility of an original polarized hyperspectral imaging setup in the spectral range of 400–1100 nm for enhancement of absorbance signal measurement on highly scattering samples. Spatial response and spectral calibration have been verified, indicating the consistency of this system and reliability of the acquired data. Model samples consisting of layered sand were prepared and used to uncover the hidden spectral information. In the model matrix, sand worked as scattering particle and dye E141 as absorbing material. Cross (R⊥) and parallel (R||) reflectance signals, along with the back-scattered reflectance, RBS(R||+R⊥) and the weakly scattered reflectance RSS(R||−R⊥) spectra were computed and compared. Results demonstrated that cross-polarized images showed more subsurface information from the second layer due to the rejection of the superficial reflectance, while weakly scattered reflectance (RSS) preserved only the surface information from the first layer. In addition, polarized light spectroscopy absorbance based on Dahm's equation in the frame of the representative layer theory and standard normal variate preprocessing RBSspectra were also obtained from the prepared model matrix. The visual inspection of spectral curves revealed that RSS/RBSand PoLiS absorbance showed two narrow peaks at 405 nm and 630 nm that were less impacted by multi-scattering effects. Partial least squares regression models were developed to predict dye concentration in the mixture sample. Consistent with the spectral profiles, RSS/RBSand PoLiS absorbance presented the best model performances with determination of coefficients of prediction (r2Pred) equal to 0.96 and 0.95, respectively. The resulting distribution maps of S1/S2 sand sample again confirmed the superior performance of RSS/RBSand PoLiS absorbance, manifesting their better ability to reveal chemically related information. The overall results obtained in this research showed that the developed polarized-hyperspectral imaging system coupled with scattering correction methods has great potential for the analysis of powdered or turbid samples.

This study was carried out to investigate the feasibility of an original polarized hyperspectral imaging setup in the spectral range of 400–1100 nm for enhancement of absorbance signal measurement on highly scattering samples. Spatial response and spectral calibration have been verified, indicating the consistency of this system and reliability of the acquired data. Model samples consisting of layered sand were prepared and used to uncover the hidden spectral information. In the model matrix, sand worked as scattering particle and dye E141 as absorbing material. Cross (R_⊥) and parallel (R_||) reflectance signals, along with the back-scattered reflectance, R_BS(R_||+R_⊥) and the weakly scattered reflectance R_SS (R_||−R_⊥) spectra were computed and compared. Results demonstrated that cross-polarized images showed more subsurface information from the second layer due to the rejection of the superficial reflectance, while weakly scattered reflectance (R_SS) preserved only the surface information from the first layer. In addition, polarized light spectroscopy absorbance based on Dahm's equation in the frame of the representative layer theory and standard normal variate preprocessing R_BS spectra were also obtained from the prepared model matrix. The visual inspection of spectral curves revealed that R_SS/R_BS and PoLiS absorbance showed two narrow peaks at 405 nm and 630 nm that were less impacted by multi-scattering effects. Partial least squares regression models were developed to predict dye concentration in the mixture sample. Consistent with the spectral profiles, R_SS/R_BS and PoLiS absorbance presented the best model performances with determination of coefficients of prediction (r^2_Pred) equal to 0.96 and 0.95, respectively. The resulting distribution maps of S1/S2 sand sample again confirmed the superior performance of R_SS/R_BS and PoLiS absorbance, manifesting their better ability to reveal chemically related information. The overall results obtained in this research showed that the developed polarized-hyperspectral imaging system coupled with scattering correction methods has great potential for the analysis of powdered or turbid samples.

The approach adopted in the present work was to increase the pressure within an integrating sphere system to increase the number density of molecules in the gas cell and hence to obtain a significant absorption in order to improve the sensitivity of the measurement system. This feasibility study has allowed an assessment of the net absorption gain with the rise of pressure and highlights the validity domain of the linear operating regime relative to Beer's law. Experiments were conducted on the oxygen A-band. The absorption peaks of oxygen at 760 nm typically were measured with a 50 mm diameter integrating sphere system under various pressures. Tests were performed up to 200 bar, the pressure for which the linear regime was operative, and analysed from a theoretical and experimental point of view. An experimental net absorption gain of 160 was achieved in this pressure range with the possibility of up to 650 bar while remaining in the linear regime. Finally, the experimental data obtained, in particular the absorption evolution due to the contribution of oxygen gas, seem consistent with the simulated results and are discussed in this paper.

The approach adopted in the present work was to increase the pressure within an integrating sphere system to increase the number density of molecules in the gas cell and hence to obtain a significant absorption in order to improve the sensitivity of the measurement system. This feasibility study has allowed an assessment of the net absorption gain with the rise of pressure and highlights the validity domain of the linear operating regime relative to Beer's law. Experiments were conducted on the oxygen A-band. The absorption peaks of oxygen at 760 nm typically were measured with a 50 mm diameter integrating sphere system under various pressures. Tests were performed up to 200 bar, the pressure for which the linear regime was operative, and analysed from a theoretical and experimental point of view. An experimental net absorption gain of 160 was achieved in this pressure range with the possibility of up to 650 bar while remaining in the linear regime. Finally, the experimental data obtained, in particular the absorption evolution due to the contribution of oxygen gas, seem consistent with the simulated results and are discussed in this paper.

Light carrying orbital angular momentum L⃗, scattered by a rotating object at angular velocity Ω⃗, experiences a rotational Doppler shift Ω⃗·L⃗. We show that this fundamental light–matter interaction can be detected exploiting self-mixing in a vortex laser under Doppler-shifted optical feedback, with quantum noise-limited light detection. We used a low-noise relaxation oscillation-free (class–A) vortex laser, based on III-V semiconductor vertical-external-cavity-surface-emitting laser technology to generate coherent Laguerre–Gauss beams carrying L=ℏl (l=±1,…±4). Linear and rotational Doppler effects were studied experimentally and theoretically. This will allow us to combine a velocity sensor with optical tweezers for micro-manipulation applications, with high performances: compact, powerful ≫10  mW, high-quality beam, auto-aligned, linear response up to >10^8  rad/s or >300  km/h, low back-scattered light detection limit <10^−16/Hz.

We introduce a time-domain model to study the dynamics of optoelectronic oscillators. We show that, due to the interaction between nonlinearity and time delay, the envelope amplitude of ultrapure microwaves generated by optoelectronic oscillators can turn unstable when the gain is increased beyond a given critical value. Our analytical predictions are confirmed by numerical simulations and experiments.

We describe the fabrication of an intrinsic fiber Fabry-Pe´rot interferometer meant to be used in white light interferometry sensing experiments. It requires the elaboration of a cavity exhibiting a finesse of about 2, and therefore, cavity mirrors with a reflection coefficient of about 23&percent;. The different steps involved in the fabrication of the cavity are addressed. We discuss the optical properties of the e-beam deposited TiO2 and SiO2 multilayer mirrors used for the fabrication of the cavity. In this part, a convenient method for measuring the complex refractive index of the materials is proposed, as well as an in situ monitoring technique used to accurately control the film properties. We next study the evolution of the mirror reflectivity with the splicing procedure, and we propose a modeling of the mirror composition after splicing. Then, the fabrication of a 20-mm-long Fabry-Pe´rot cavity exhibiting a visibility of 0.5 is presented. In this section, an efficient signal processing is proposed to reduce any measurement noise.