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Papers by Fabien Chauchard

Applied Optics, Nov 20, 2005

By use of time-resolved spectroscopy it is possible to separate light scattering effects from che... more By use of time-resolved spectroscopy it is possible to separate light scattering effects from chemical absorption effects in samples. In the study of propagation of short light pulses in turbid samples the reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data by use of numerical optimization techniques. In this study we propose a prediction model obtained with a semiparametric modeling technique: the least-squares support vector machines. The main advantage of this technique is that it uses theoretical time dispersion curves during the calibration step. Predictions can then be performed by use of data measured on different kinds of sample, such as apples.

Optical Tomography and Spectroscopy of Tissue IX, 2011

ABSTRACT The use of the spectral derivative method in Near Infrared optical spectroscopy and tomo... more ABSTRACT The use of the spectral derivative method in Near Infrared optical spectroscopy and tomographic imaging is presented, whereby instead of using discrete measurements around several wavelengths, the difference between nearest neighboring spectral measurements is used. The proposed technique is shown to be insensitive to the unknown tissue and fiber contact coupling coefficients providing substantially increased accuracy as compared to more conventional techniques. The self-calibrating nature of the spectral derivative techniques increases its robustness in clinical applications, as is demonstrated based on simulated results.

Optics express, Jan 19, 2015

Organic Photo Sensor (OPS) technology allows printing on conformable plastic-like substrates comp... more Organic Photo Sensor (OPS) technology allows printing on conformable plastic-like substrates complex-shaped, arbitrarily-sized and pre-aligned photosensitive elements. This article reports, to the best of our knowledge, the first investigation to implement this emerging technology for Multi-Angle Light Scattering (MALS) characterization of nano- and microparticle suspensions. Monte Carlo and Lorenz-Mie theory calculations as well as preliminary experimental results on latex suspensions clearly demonstrate the potential of the proposed approach.

L'émergence de la technologie des photodétecteurs organiques permet d'envisager la concep... more L'émergence de la technologie des photodétecteurs organiques permet d'envisager la conception de granulomètres optiques au design innovant. Cette technologie permet en effet la conception de zones photosen-sibles de formes complexes et conformables. Des simulations de type Monte Carlo et un modèle analytique ont permis de concevoir et d'optimiser un premier prototype de granulomètre multi-angulaire. Une méthode d'in-version encore très simplifiée est proposée pour déterminer directement le diamètre moyen d'un écoulement particulaire en conduite cylindrique.

Novel methods for particle size measurement in complex environments may be developed thanks to th... more Novel methods for particle size measurement in complex environments may be developed thanks to the emergence of Organic Photo Sensors (OPS). The latter permit creating complex shaped and virtually arbitrary sized photosensitive areas by a simple printing technique, on plastic substrates that can be bent (i.e. conformable sensors). Thanks to these properties, a Multi-Angle Light Scattering based prototype (i.e. MALS, or nephelometer) is designed for the characterization of particles in pipe flow within size range (D  5-50µm). Monte Carlo simulations are used to optimize the response and geometry of the optical system as well as effects that cannot be accounted by Lorenz-Mie theory based light scattering codes. A simple method, based on the scattering intensity in the first rainbow angle region, is introduced to recover the mean particle size.

Use the Organic Photo Sensors (OPS) properties to develop method to characterize particle systems... more Use the Organic Photo Sensors (OPS) properties to develop method to characterize particle systems Model light scattering in a complex environment (confined space-industrial constraints) to predict the OPS response Invert the collected signal to obtain a bi-univocal relationship between the particle size of the sample and the signal Design an optical particle sizing prototype able to operate in such environment MONTE-CARLO LIGHT SCATTERING MODEL Some assumptions Two parameters distribution (Log-Normal)-Spherical particles Parameters : mean diameter and standard deviation (+index) Scattering regime: simple or multiple Remote sensing approximation Numerical model and computer code Principle : Decompose into probabilities the physical behavior and model [1] Allow to use simultaneously various models and theories:-Optic & physic (Snell-Descartes, diffraction, rainbow, critical scattering…) [2]-Electromagnetic (Lorenz-Mie, Debye…) Angular approach chosen: Intensity vs. collection angle (structure factor) [3] Computer code: Fortran, MPI,… Result example : cylindrical projection of the scattered light by droplets cloud (water droplets in air, D= 100µm

Physics in medicine and biology, Jan 21, 2010

The use of the spectral derivative method in visible and near-infrared optical spectroscopy is pr... more The use of the spectral derivative method in visible and near-infrared optical spectroscopy is presented, whereby instead of using discrete measurements around several wavelengths, the difference between nearest neighbouring spectral measurements is utilized. The proposed technique is shown to be insensitive to the unknown tissue and fibre contact coupling coefficients providing substantially increased accuracy as compared to more conventional techniques. The self-calibrating nature of the spectral derivative techniques increases its robustness for both clinical and industrial applications, as is demonstrated based on simulated results as well as experimental data.

Food Engineering Series, 2014

Journal of Near Infrared Spectroscopy, 2004

ABSTRACT

Journal of Near Infrared Spectroscopy, 2008

Chemometrics and Intelligent Laboratory Systems, 2011

Chemometrics and Intelligent Laboratory Systems, 2008

Fluorescence spectroscopy is a useful technique for tissue diagnostics and is also a promising to... more Fluorescence spectroscopy is a useful technique for tissue diagnostics and is also a promising tool in the characterization of embedded structures in tissue. The emitted fluorescence from an embedded inclusion, marked with a fluorescent compound, is affected by several factors as the light propagates through the medium to the tissue boundary, where the fluorescence light is detected. Tissue absorption, scattering and autofluorescence, as well as the size and depth of the inclusion, affect the detected fluorescence light. The aim of this study is to investigate if the size and location of a fluorescent inclusion could be determined using models based a combination of External Parameter Orthogonalisation (EPO) and Least Squares Support Vector Machine (LS-SVM). This can be very useful for data pre-processing before a full fluorescence tomography reconstruction. The data set consisted of simulated multispectral fluorescence, where depth and radius of a spherical fluorescent inclusion were varied as well as the fluorescence contrast and optical properties of the surrounding tissue. The results showed that the non-linear models based on LS-SVM can simultaneously predict both radius and depth. It was observed that EPO acts as a useful pre-processing tool on spectra for this nonlinear model and that it was necessary to perform EPO to be able to predict the depth with the LS-SVM model.

Chemometrics and Intelligent Laboratory Systems, 2003

NIR spectrometry would present a high potential for online measurement if the robustness of multi... more NIR spectrometry would present a high potential for online measurement if the robustness of multivariate calibration was improved. The lack of robustness notably appears when an external parameter varies -e.g. the product temperature. This paper presents a preprocessing method which aims at removing from the X space the part mostly influenced by the external parameter variations. This method estimates this parasitic subspace by computing a PCA on a small set of spectra measured on the same objects, while the external parameter is varying. An application to the influence of the fruit temperature on the sugar content measurement of intact apples is presented. Without any preprocessing, the bias in the sugar content prediction was about 8 o Brix for a temperature variation of 20 o C. After EPO preprocessing the bias is not more than 0.3 o Brix, for the same temperature range. The parasitic subspace is studied by analysing the b-coefficient of a PLS between the temperature and the influence spectra. Further work will be achieved to apply this method to the case of multiple external parameters and to the calibration transfer issue.

Chemometrics and Intelligent Laboratory Systems, 2004

Nowadays, near infrared (NIR)technology is being transferred from the laboratory to the industria... more Nowadays, near infrared (NIR)technology is being transferred from the laboratory to the industrial world for on-line and portable applications. As a result, new issues are arising, such as the need for increased robustness, or the ability to compensate for non-linearities in the calibration or instrument. Semi-parametric modeling has been suggested as a means for adapting to these complications. In this article, Least-Squared Support Vector Machine (LS-SVM) regression, a semi-parametric modeling technique, is used to predict the acidity of three different grape varieties using NIR spectra. The performance and robustness of LS-SVM regression are compared to Partial Least Square Regression (PLSR) and Multivariate Linear Regression (MLR). LS-SVM regression produces more accurate prediction. However SNV pretreatment is required to improve the model robustness.

Applied Spectroscopy, 2005

Time-resolved spectroscopy is a powerful technique permitting the separation of the scattering pr... more Time-resolved spectroscopy is a powerful technique permitting the separation of the scattering properties from the chemical absorption properties of a sample. The reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data using numerical optimization techniques. However, these methods do not take the spectral dimension of the data into account during the evaluation procedure, but evaluate each wavelength separately. A procedure involving multivariate methods may seem more appealing for people used to handle conventional near-infrared data. In this study we present a new method for processing TRS spectra in order to compute the absorption and reduced scattering coefficients. This approach, MADSTRESS, is based on linear regression and a 2-D interpolation procedure. The method has allowed us to calculate absorption and scattering coefficients of apples and fructose powder. The accuracy of the method was good enough to provide the identification of fructose absorption peaks in apple absorption spectra and the construction of a calibration model predicting the sugar content of apples.

Applied Optics, 2005

Time Resolved Spectroscopy is able to separate the light scattering effect from the chemical abso... more Time Resolved Spectroscopy is able to separate the light scattering effect from the chemical absorption effect. This method is based on the time dispersion of light pulses into the scattering medium. The reduced scattering coefficient and the absorption coefficient are usually obtained using numerical optimization technique or Monte Carlo simulation. In this study, we propose to create a prediction model obtained using a semi-parametric modelisation method : the Least-Squares Support Vector Machine. The main advantage of this model is that it uses theorical curve of time dispersion during the calibration step. The prediction can then be performed on different kind of samples such as apples or biological tissue.