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International audience; Hyperspectral imaging is a versatile tool, with a promising prospective for the quick identification of foodborne microbial organisms. One of the main limitations for the application of Near Infrared (NIR) Spectral imaging for the identification of microorganisms in culture media is sample presentation. Polystyrene plastic petri dishes are one the most common sample holder used in microbiological studies. They are cheap, disposable, easy to handle and well standardized. Nevertheless, their use is generally proscribed for NIR spectral imaging, as the spectral signature of Polystyrene dishes can interfere with the spectral information of interest from the sample. The aim of this study is to characterize the effect different types of sample presentation, glass and polystyrene petri dishes, and to test orthogonal projection as a pretreatment to reduce the interference of polystyrene dishes for NIR imaging of Luria Bertani Agar. The combination of Savitzky Golay smoothing, SNV and orthogonal projection allowed to attenuate the spectral differences between agar samples presented on polystyrene and glass dishes. This same approach allowed to reduce the effect of the polystyrene dishes on samples with different agar thicknesses. Future research will test this approach as a method to facilitate the identification of bacteria on polystyrene petri dishes through NIR hyperspectral imaging.

This study aims to investigate the potential of an original polarized hyperspectral imaging (HSI) setup in the spectral domain of 400–1000 nm for sunflower leaves in real-world. Dataset 1 includes hypercubes of sunflower leaves in two varieties with different life growth stages, while Dataset 2 is comprised of healthy and contaminated sunflower leaves suffering from powdery mildew (PM) and/or septoria leaf spot (SLS). Cross polarised ( R ⊥ ), parallel polarised ( R | | ) reflectance signals, R BS ( R | | + R ⊥ ) and R SS ( R | | - R ⊥ ) spectra were obtained and used to develop partial least squares-discriminant analysis (PLS-DA) models. Surface information played an important role in separating two varieties of leaves due to the fact that the best model performance was achieved by using R SS mean spectra, while both surface and subsurface were equally important in classifying leaves between two major growth stages because model of R BS mean spectra outperformed other models. The best classification model for disease detection was achieved by using pixel R ⊥ spectra with the correct classification rate (CCR) of 0.963 for both cross validation and prediction, meaning that subsurface spectral features were the most important to detect infected leaves. The resulting classification maps were also displayed to visualize the distribution of the infected regions on the leaf samples. The overall results obtained in this research showed that the developed polarized-HSI system coupled with multivariate analysis has considerable promise in agricultural real-world applications.

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 ([Formula: see text]) and parallel ([Formula: see text]) reflectance signals, along with the back-scattered reflectance, [Formula: see text]([Formula: see text]+[Formula: see text]) and the weakly scattered reflectance [Formula: see text] ([Formula: see text]−[Formula: see text]) 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 ([Formula: see text]) 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 [Formula: see text] spectra were also obtained from the prepared model matrix. The visual inspection of spectral curves revealed that [Formula: see text] 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, [Formula: see text] and PoLiS absorbance presented the best model performances with determination of coefficients of prediction ([Formula: see text]) equal to 0.96 and 0.95, respectively. The resulting distribution maps of S1/S2 sand sample again confirmed the superior performance of [Formula: see text] 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.

International audience; The paper proposes a methodology based on near-infrared (NIR) spectrometry for studying stratigraphy and depth profiles in archaeological excavations. The NIR spectra can be used to describe and complement the wet chemical analysis. Soil samples were collected from a 0.8 m deep stratigraphy of a Neolithic site that were analyzed by three different NIR instrumentations. Phosphate- and magnetic susceptibility and inductively-coupled plasma mass spectrometry measurements were also conducted as reference analysis. Principal component analysis on the data from three different NIR instrumentations gave useful score plots that allowed grouping of the samples. The results from the lab spectrometer were most useful, although the hyperspectral NIR camera was the fastest method to obtain spectra of many samples from one image. The paper shows how the NIR spectral data can be used for multivariate analysis to get meaningful conclusions on archaeological soils and sediments, especially in terms of understanding site development/phases and soil formation.

The detection of plant diseases, including fungi, is a major challenge for reducing yield gaps of crops across the world. We explored the potential of the PROCOSINE radiative transfer model to assess the effect of the fungus Pseudocercospora fijiensis on leaf tissues using laboratory-acquired submillimetre-scale hyperspectral images in the visible and near-infrared spectral range. The objectives were (i) to assess the dynamics of leaf biochemical and biophysical parameters estimated using PROCOSINE inversion as a function of the disease stages, and (ii) to discriminate the disease stages by using a Linear Discriminant Analysis model built from the inversion results. The inversion results show that most of the parameter dynamics are consistent with expectations: for example, the chlorophyll content progressively decreased as the disease spreads, and the brown pigments content increased. An overall accuracy of 78.7% was obtained for the discrimination of the six disease stages, with errors mainly occurring between asymptomatic samples and first visible disease stages. PROCOSINE inversion provides relevant ecophysiological information to better understand how P. fijiensis affects the leaf at each disease stage. More particularly, the results suggest that monitoring anthocyanins may be critical for the early detection of this disease.

International audience; This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information. It first consists of a supervised spectral dimension reduction step that transforms the HS image into a score image that has fewer channels. These channels are chosen so as to enhance distances between classes to be discriminated and to reduce background variability, thus leading to edges that correspond to actual class borders. In the second step, applying an edge-preserving spatial regularization on this score image leads to a lowered background variability. Therefore, in the third step, the pixel-wise classification of the regularized score image is greatly improved. We implement this approach using the partial least squares (PLS) method for spectral dimension reduction and the anisotropic diffusion for spatial regularization. We then compare linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) for the class decision. The effectiveness of our method was evaluated with three remotely sensed HS images. Its robustness was also assessed for different training sets, since the latter has a crucial influence on classification performance. On average, our method gave better results in terms of classification accuracy and was more robust than other classification methods tested with the same images.

International audience; This dataset presents two series of hyperspectral images of healthy and infected apple tree leaves acquired daily, from two days after inoculation until an advanced stage of infection (11 days after inoculation). The hyperspectral images were calibrated by reflection correction and registered to match the geometry of one reference image. On the last experiment day, scab positions are provided.

We propose a phenotyping method to characterize the crop row structure.A crop row 3D model is built using a single camera and Structure from Motion.Structural parameters such as height and leaf area are estimated from this 3D model.Our method was tested with various plant structures and outdoor conditions.Strong agreements were obtained between estimated and actual heights and leaf areas. This article presents a method for crop row structure characterization that is adapted to phenotyping-related issues. In the proposed method, a crop row 3D model is built and serves as a basis for retrieving plant structural parameters. This model is computed using Structure from Motion with RGB images acquired by translating a single camera along the row. Then, to estimate plant height and leaf area, plant and background are discriminated by a robust method that uses both color and height information in order to handle low-contrasted regions. The 3D model is scaled and the plant surface is finally approximated using a triangular mesh.The efficacy of our method was assessed with two data sets collected under outdoor conditions. We also evaluated its robustness against various plant structures, sensors, acquisition techniques and lighting conditions. The crop row 3D models were accurate and led to satisfactory height estimation results, since both the average error and reference measurement error were similar. Strong correlations and low errors were also obtained for leaf area estimation. Thanks to its ease of use, estimation accuracy and robustness under outdoor conditions, our method provides an operational tool for phenotyping applications.

International audience; Raman chemical imaging provides both spectral and spatial information on a pharmaceutical drug product. Even if the main objective of chemical imaging is to obtain distribution maps of each formulation compound, identification of pure signals in a mixture dataset remains of huge interest. In this work, an iterative approach is proposed to identify the compounds in a pharmaceutical drug product, assuming that the chemical composition of the product is not known by the analyst and that a low dose compound can be present in the studied medicine. The proposed approach uses a spectral library, spectral distances and orthogonal projections to iteratively detect pure compounds of a tablet. Since the proposed method is not based on variance decomposition, it should be well adapted for a drug product which contains a low dose product, interpreted as a compound located in few pixels and with low spectral contributions. The method is tested on a tablet specifically manufactured for this study with one active pharmaceutical ingredient and five excipients. A spectral library, constituted of 24 pure pharmaceutical compounds, is used as a reference spectral database. Pure spectra of active and excipients, including a modification of the crystalline form and a low dose compound, are iteratively detected. Once the pure spectra are identified, multivariate curve resolution-alternating least squares process is performed on the data to provide distribution maps of each compound in the studied sample. Distributions of the two crystalline forms of active and the five excipients were in accordance with the theoretical formulation.

International audience; Visible and near-infrared (Vis-NIR) spectra are generated by the combination of numerous low resolution features. Spectral variables are thus highly correlated, which can cause problems for selecting the most appropriate ones for a given application. Some decomposition bases such as Fourier or wavelet generally help highlighting spectral features that are important, but are by nature constraint to have both positive and negative components. Thus, in addition to complicating the selected features interpretability, it impedes their use for application-dedicated sensors.In this paper we have proposed a new method for feature selection: Application-Dedicated Selection of Filters (ADSF). This method relaxes the shape constraint by enabling the selection of any type of user defined custom features. By considering only relevant features, based on the underlying nature of the data, high regularization of the final model can be obtained, even in the small sample size context often encountered in spectroscopic applications. For larger scale deployment of application-dedicated sensors, these predefined feature constraints can lead to application specific optical filters, e.g., lowpass, highpass, bandpass or bandstop filters with positive only coefficients.In a similar fashion to Partial Least Squares, ADSF successively selects features using covariance maximization and deflates their influences using orthogonal projection in order to optimally tune the selection to the data with limited redundancy. ADSF is well suited for spectroscopic data as it can deal with large numbers of highly correlated variables in supervised learning, even with many correlated responses. © 2016 Elsevier B.V.

International audience; Radiative transfer models have long been used to characterize the foliar content at the leaf and canopy levels. However, they still do not apply well to close-range imaging spectroscopy, especially because directional effects are usually not taken into account. For this purpose, we introduce a physical approach to describe and simulate the variation in leaf reflectance observed at this scale. Two parameters are thus introduced to represent (1) specular reflection at the leaf surface and (2) local leaf orientation. The model, called COSINE (ClOse-range Spectral ImagiNg of lEaves), can be coupled with a directional-hemispherical reflectance model of leaf optical properties to relate the measured reflectance to the foliar content. In this study, we show that, when combining COSINE with the PROSPECT model, the overall PROCOSINE model allows for a robust sub-millimeter retrieval of foliar content based on numerical inversion and pseudo bidirectional reflectance factor hyperspectral measurements.The relevance of the added parameters is first shown through a sensitivity analysis performed in the visible and near-infrared (VNIR) and shortwave infrared (SWIR) ranges. PROCOSINE is then validated based on VNIR and SWIR hyperspectral images of various leaf species exhibiting different surface properties. Introducing these two parameters within the inversion allows us to obtain accurate maps of PROSPECT parameters, e.g., the chlorophyll content in the VNIR range, and the equivalent water thickness and leaf mass per area in the SWIR range. Through the estimation of light incident angle, the PROCOSINE inversion also provides information on leaf orientation, which is a critical parameter in vegetation remote sensing.

International audience; In order to be independent from light source and atmospheric conditions, radiance values extracted from a remote hyperspectral image have to be converted into reflectance values before data processing. Several methods have been proposed in the literature but they require that the lighting/and or atmospheric conditions to be estimated. In the framework of supervised classification, we propose an approach to deal with such lighting and atmospheric temporal fluctuations without reference measurement. Assuming that materials to the surface objects to be discriminated is Lambertian, we show that the difference in lighting conditions after a log-transformation of both reflectance and radiance signals can be expressed as an additive effect. This effect remains additive after the use of a linear dimension reduction method and can be efficiently estimated in a low dimensional feature space. In the feature space, this difference in ligthing can be estimated and thus corrected by finding the translation for which the class densities obtained for each image best overlaps (using cross correlation). This novel approach was applied on a remote sensing data set over the Quiberon peninsula France. For the tested images, classification results obtained with this approach were comparable to those obtained using a classical reflectance correction technique.

International audience; Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used. In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined.

International audience; In this work, Raman hyperspectral images and Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) are used to study the distribution of actives and excipients within a pharmaceutical drug product. This article is mainly focused on the distribution of a low dose constituent. Different approaches are compared, using initially filtered or non-filtered data, or using a column-wise augmented dataset before starting the MCR-ALS iterative process including appended information on the low dose component. In the studied formulation, magnesium stearate is used as a lubricant to improve powder flowability. With a theoretical concentration of 0.5% w/w in the drug product, the spectral variance contained in the data is weak. By using a Principal Component Analysis (PCA) filtered dataset as a first step of the MCR-ALS approach, the lubricant information is lost in the non-explained variance and its associated distribution in the tablet cannot be highlighted. A sufficient number of components to generate the PCA noise-filtered matrix has to be used in order to keep the lubricant variability within the data set analyzed or, otherwise, work with the raw non-filtered data. Different models are built using an increasing number of components to perform the PCA reduction. It is shown that the magnesium stearate information can be extracted from a PCA model using a minimum of 20 components. In the last part, a column-wise augmented matrix, including a reference spectrum of the lubricant, is used before starting MCR-ALS process. PCA reduction is performed on the augmented matrix, so the magnesium stearate contribution is included within the MCR-ALS calculations. By using an appropriate PCA reduction, with a sufficient number of components, or by using an augmented dataset including appended information on the low dose component, the distribution of the two actives, the two main excipients and the low dose lubricant are correctly recovered.

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination ( r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.

Independent component analysis (ICA) was used as a blind source separation method on a Raman image of a pharmaceutical tablet. Calculations were performed without a priori knowledge concerning the formulation. The aim was to extract the pure signals from the initial data set in order to examine the distribution of actives and major excipients within the tablet. As a method based on the decomposition of a matrix of mixtures of several components, the number of independent component to choose is a critical step of the analysis. The ICA_by_blocks method, based on the calculation of several models using an increasing number of independent components on initial matrix blocks, was used. The calculated ICA signals were compared with the pure spectra of the formulation compounds. High correlations between the two active principal ingredient spectra and their corresponding calculated signals were observed giving a good overview of the distributions of these compounds within the tablet. Information from the major excipients (lactose and avicel) was found in several independent components but the ICA approach provides high level of information concerning their distribution within the tablet. However, the results could vary considerably by changing the number of independent components or the preprocessing method. Indeed, it was shown that under-decomposition of the matrix could lead to better signal quality (compared to the pure spectra) but in that case the contributions due to minor components or effects were not correctly identified and extracted. On the contrary, over-decomposition of the original dataset could provide information about low concentration compounds at the expense of some loss of signal interpretability for the other compounds.

International audience; Visible-near-infrared hyperspectral imaging was tested for its suitability for monitoring the moisture content (MC) of wood samples during natural drying. Partial least-squares regression (PLSR) prediction of MC was performed on the basis of average reflectance spectra obtained from hyperspectral images. The validation showed high prediction accuracy. The results were compared concerning the PLSR prediction of MC mapping from raw spectra and standard normal variate (SNV) treatment. SNV pretreatment leads to the best results for visualizing the MC distribution in wood. Hyperspectral imaging has a high potential for monitoring the water distribution of wood.; Cet article s’intéresse à l’imagerie hyperspectrale visible-proche infrarouge en tant qu’outil de suivi de la teneur en eau d’échantillons de bois durant la phase de séchage naturel. Des prédictions par régression PLS ont été conduites à partir de spectres moyens de réflectance issus d’images hyperspectrales. La validation met en évidence une bonne précision de prédiction. Les résultats obtenus à partir de spectres bruts ou prétraités par SNV ont été comparés. Le prétraitement SNV conduit a un meilleur résultat. Il permet de visualiser la distribution spatiale de la teneur en eau dans l’échantillon, confirmant l’intérêt de l’imagerie hyperspectrale pour ce type de suivi.

Le concept d’agriculture de précision consiste à gérer spatialement les pratiques culturales en fonction des variabilités intra-parcellaires. La plupart des outils associés utilisent des capteurs optiques ou imageurs. L’imagerie aérienne est notamment souvent utilisée pour compléter ou remplacer des capteurs embarqués sur machine agricole, mais elle présente des inconvénients comparée aux capteurs optiques de " proxi-détection " : résolution, accessibilité, coût, temporalité, etc. Nous proposons dans ce chapitre d’explorer le domaine de la proxi-détection en s’intéressant d’abord au développement de systèmes d’acquisition d’image robustes, puis aux méthodes de traitement d’image à travers divers exemples d’applications : caractérisation de cultures de blé d’une part (détection/comptage d’épis, détection d’adventices), évolution de la germination de graines (chicorée) d’autre part. / The concept of precision agriculture consists to spatially manage crop management practices according to in-field variability. Most of the tools used for Precision Farming utilize optical and/or imaging sensors. Remote sensing imaging devices are often used to complete or replace embedded sensors onboard the agricultural machinery, however they get some drawbacks compared to “proxy-detection” optical sensors: resolution, accessibility, cost, temporality, etc. We propose in this chapter to explore the proxi-detection domain by focusing first on the development of robust image acquisition systems, and secondly on the use of image processing through different application examples dealing with wheat crop characterization (detection and counting of wheat ears, weed detection), and evaluation of seed development/germination performance of chicory achenes.

International audience; Les images multispectrales incluant des bandes rouge et proche infrarouge ont prouvé leur efficacité pour la discrimination entre sol et végétation et pour le suivi agricole en télédétection. Mais elles restent très peu utilisées en vision au sol ou par drone, par manque de disponibilité d'outils de prise d'image adéquats. Dans ce papier, une méthode générique est proposée pour obtenir simultanément les bandes rouge et proche infrarouge à partir d'un appareil photographique standard, après avoir ôté le filtre interne bloquant l'infrarouge. Cette méthode a été appliquée à deux appareils de nouvelle génération (Canon 500D et Sigma SD14). Les valeurs NDVI obtenues ont été comparées avec des valeurs de référence pour un jeu de spectres de sol et de végétation. La qualité des résultats montre que les bandes NDVI peuvent maintenant être acquises avec des imageurs à haute résolution spatiale, offrant ainsi de nouvelles opportunités pour des applications de suivi des cultures. / Multispectral images including red and near-infrared bands have proved their efficiency for vegetation-soil discrimination and agricultural monitoring in remote sensing applications. But they remain rarely used in ground and UAV imagery, due to a limited availibility of adequate 2D imaging devices. In this paper, a generic methodology is proposed to obtain simultaneously the near-infrared and red bands from a standard RGB camera, after having removed the near-infrared blocking filter inside. This method has been applied with two new generation SLR cameras (Canon 500D and Sigma SD14). NDVI values obtained from these devices have been compared with reference values for a set of soil and vegetation luminance spectra. The quality of the results shows that NDVI bands can now be acquired with high spatial resolution 2D imaging devices, opening new opportunities for crop monitoring applications.

International audience; Calibrating consists in predicting Y, a quantitative variable of interest, using P explaining variables. PLSR-Projection to Latent Structures Regression (Wold,[1]) is the most popular method, very powerfull when a calibration dataset is available. Other methods don't need a calibration dataset, they are called direct calibrations. Two of them have been proposed previously: DC-Direct Calibration (as described in Martens and Naes, [2]) and SBC-Science Based Calibration (Marbach([3])). New method called IDC-Improved Direct Calibration is proposed. As for DC, this approach is based on an orthogonal projection. IDC projector is obtained by merging DC projector (consisting only in pure spectra of chemical compounds), and vectors characterising physical influence factors (consisting in PCA loadings onto a design dataset). Indeed, hyperspectral image analysis is a case where calibration data are not available. Thus, it's interesting to use direct calibration methods instead of PLSR. With the prior knowledge of a few reference spectra, and modelling some noise from the hyperspectral image itself, it's possible to identify objects of interest from the background. This method is also simple and understandable, very quick and easy to compute.

International audience; Few methods are proposed in the litterature for coupling the spectral and the spatial dimension available on hyperspectral images. This paper proposes a generic segmentation scheme named butterfly based on an iterative process and a cross analysis of spectral and spatial information. Indeed, spatial and spatial structures are extracted in spatial and spectral space respectively both taking into account the other one. To apply this layout on hyperspectral imgages, we focus particulary on spatial and spectral structures i.e. topologic concepts and latent variable for the spatial and the spectral space respectively. Moreover, a cooperation scheme with these structures is proposed. Finally, results obtained on real hyperspectral images using this specific implementation of the butterfly approach are presented and discussed.