Publications by authors named "Jean-Michel Roger"

43 Publications

Untargeted analysis of TD-NMR signals using a multivariate curve resolution approach: Application to the water-imbibition kinetics of Arabidopsis seeds.

Talanta 2021 Oct 27;233:122525. Epub 2021 May 27.

INRAE, UR1466 OPAALE, 17 Avenue de Cucillé, CS 64427, F-35044, Rennes, France.

The aim of this study is to investigate the ability of Time-Domain Nuclear Magnetic Resonance (TD-NMR) combined with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis to detect changes in hydration properties of nineteen genotypes of Arabidopsis (Arabidopsis thaliana) seeds during the imbibition process. The Hybrid hard and Soft modelling version of MCR-ALS (HS-MCR) applied to raw TD-NMR data allowed the introduction of kinetic models to elucidate underlying biological mechanisms. The imbibition process of all investigated hydrated Arabidopsis seeds could be described with a kinetic model based on two consecutive first-order reactions related to an initial absorption of water from the bulk around the seed and a posteriori hydration of the internal seed tissues, respectively. Good data fit was achieved (LOF % = 0.98 and r% = 99.9), indicating that the hypothesis of the selected kinetic model was correct. An interpretation of the mucilage characteristics of the studied Arabidopsis seeds was also provided. The presented methodology offers a novel and general strategy to describe in a comprehensive way the kinetic process of plant tissue hydration in a screening objective. This work also proves the potential of the MCR methods to analyse raw TD-NMR signals as alternative to the controversial and time-consuming pre-processing techniques of this kind of data, known to be an ill-conditioned and ill-posed problem.
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http://dx.doi.org/10.1016/j.talanta.2021.122525DOI Listing
October 2021

On-site substrate characterization in the anaerobic digestion context: A dataset of near infrared spectra acquired with four different optical systems on freeze-dried and ground organic waste.

Data Brief 2021 Jun 11;36:107126. Epub 2021 May 11.

INRAE, UMR ITAP, Montpellier University, Montpellier F-34000, France.

The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a micro-spectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R) [1], but could be used more broadly to test new chemometrics methods. For example, the data could be used to evaluate different transfer functions between spectroscopic systems [2]. This dataset enabled the research work reported by Mallet et al. 2021 [3].
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http://dx.doi.org/10.1016/j.dib.2021.107126DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166774PMC
June 2021

Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law.

Anal Chem 2021 05 22;93(17):6817-6823. Epub 2021 Apr 22.

INRAE, UMR ITAP, Montpellier University, 34000 Montpellier, France.

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.
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http://dx.doi.org/10.1021/acs.analchem.1c00811DOI Listing
May 2021

Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations.

Waste Manag 2021 May 16;126:664-673. Epub 2021 Apr 16.

INRAE, UMR ITAP, Montpellier University, Montpellier, France.

Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH).gTS compared to 60 NmL(CH).gTS for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste.
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http://dx.doi.org/10.1016/j.wasman.2021.03.045DOI Listing
May 2021

Chemometric pre-processing can negatively affect the performance of near-infrared spectroscopy models for fruit quality prediction.

Talanta 2021 Jul 11;229:122303. Epub 2021 Mar 11.

Farm Technology Group, Wageningen University & Research, Wageningen, the Netherlands.

Chemometrics pre-processing of spectral data is widely performed to enhance the predictive performance of near-infrared (NIR) models related to fresh fruit quality. Pre-processing approaches in the domain of NIR data analysis are used to remove the scattering effects, thus, enhancing the absorption components related to the chemical properties. However, in the case of fresh fruit, both the scattering and absorption properties are of key interest as they jointly explain the physicochemical state of a fruit. Therefore, pre-processing data that reduces the scattering information in the spectra may lead to poorly performing models. The objectives of this study are to test two hypotheses to explore the effect of pre-processing on NIR spectra of fresh fruit. The first hypothesis is that the pre-processing of NIR spectra with scatter correction techniques can reduce the predictive performance of models as the scatter correction can reduce the useful scattering information correlated to the property of interest. The second hypothesis is that the Deep Learning (DL) can model the raw absorbance data (mix of scattering and absorption) much more efficiently than the Partial Least Squares (PLS) regression analysis. To test the hypotheses, a real NIR data set related to dry matter (DM) prediction in mango fruit was used. The dataset consisted of a total of 11,420 NIR spectra and reference DM measurements for model training and independent testing. The chemometric pre-processing methods explored were standard normal variate (SNV), variable sorting for normalization (VSN), Savitzky-Golay based 2nd derivative and their combinations. Further two modelling approaches i.e., PLS regression and DL were used to evaluate the effect of pre-processing. The results showed that the best root mean squared error of prediction (RMSEP) for both the PLS and DL models were obtained with the raw absorbance data. The spectral pre-processing in general decreased the performance of both the PLS and DL models. Further, the DL model attained the lowest RMSEP of 0.76%, which was 13% lower compared to the PLS regression on the raw absorbance data. Pre-processing approaches should be carefully used while analysing the NIR data related to fresh fruit.
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http://dx.doi.org/10.1016/j.talanta.2021.122303DOI Listing
July 2021

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics.

Waste Manag 2021 Mar 19;122:36-48. Epub 2021 Jan 19.

INRAE, UMR ITAP, Montpellier University, Montpellier, France; ChemHouse Research Group, Montpellier, France.

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.
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http://dx.doi.org/10.1016/j.wasman.2020.12.019DOI Listing
March 2021

Improved prediction of fuel properties with near-infrared spectroscopy using a complementary sequential fusion of scatter correction techniques.

Talanta 2021 Feb 24;223(Pt 1):121693. Epub 2020 Sep 24.

ITAP, INRAE, Institut Agro, University Montpellier, Montpellier, France; ChemHouse Research Group, Montpellier, France.

Near-infrared (NIR) spectroscopy of fuels can suffer from scattering effects which may mask the signals corresponding to key analytes in the spectra. Therefore, scatter correction techniques are often used prior to any modelling so to remove scattering and improve predictive performances. However, different scatter correction techniques may carry complementary information so that, if jointly used, both model stability and performances could be improved. A solution to that is the fusion of complementary information from differently scatter corrected data. In the present work, the use of a preprocessing fusion approach called sequential preprocessing through orthogonalization (SPORT) is demonstrated for predicting key quality parameters in diesel fuels. In particular, the possibility of predicting four different key properties, i.e., boiling point (°C), density (g/mL), aromatic mass (%) and viscosity (cSt), was considered. As a comparison, standard partial least-squares (PLS) regression modelling was performed on data pretreated by SNV and 2nd derivative (which is a widely used preprocessing combination). The results showed that the SPORT models, based on the fusion of scatter correction techniques, outperformed the standard PLS models in the prediction of all the four properties, suggesting that selection and use of a single scatter correction technique is often not sufficient. Up to complete bias removal with 50% reduction in prediction error was obtained. The R was increased by up to 8%. The sequential scatter fusion approach (SPORT) is not limited to NIR data but can be applied to any other spectral data where a preprocessing optimization step is required.
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http://dx.doi.org/10.1016/j.talanta.2020.121693DOI Listing
February 2021

Sequential fusion of information from two portable spectrometers for improved prediction of moisture and soluble solids content in pear fruit.

Talanta 2021 Feb 13;223(Pt 2):121733. Epub 2020 Oct 13.

Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA, Wageningen, the Netherlands.

Near infrared (NIR) spectroscopy allows rapid estimation of quality traits in fresh fruit. Several portable spectrometers are available in the market as a low-cost solution to perform NIR spectroscopy. However, portable spectrometers, being lower in cost than a benchtop counterpart, do not cover the complete near infrared (NIR) spectral range. Often portable sensors either use silicon-based visible and NIR detector to cover 400-1000 nm, or InGaAs-based short wave infrared (SWIR) detector covering the 900-1700 nm. However, these two spectral regions carry complementary information, since the 400-1000 nm interval captures the color and 3rd overtones of most functional group vibrations, while the 1st and the 2nd overtones of the same transitions fall in the 1000-1700 nm range. To exploit such complementarity, sequential data fusion strategies were used to fuse the data from two portable spectrometers, i.e., Felix F750 (~400-1000 nm) and the DLP NIR Scan Nano (~900-1700 nm). In particular, two different sequential fusion approaches were used, namely sequential orthogonalized partial-least squares (SO-PLS) regression and sequential orthogonalized covariate selection (SO-CovSel). SO-PLS improved the prediction of moisture content (MC) and soluble solids content (SSC) in pear fruit, leading to an accuracy which was not obtainable with models built on any of the two spectral data set individually. Instead, SO-CovSel was used to select the key wavelengths from both the spectral ranges mostly correlated to quality parameters of pear fruit. Sequential fusion of the data from the two portable spectrometers led to an improved model prediction (higher R and lower RMSEP) of MC and SSC in pear fruit: compared to the models built with the DLP NIR Scan Nano (the worst individual block) where SO-PLS showed an increase in R up to 56% and a corresponding 47% decrease in RMSEP. Differences were less pronounced to the use of Felix data alone, but still the R was increased by 2.5% and the RMSEP was reduced by 6.5%. Sequential data fusion is not limited to NIR data but it can be considered as a general tool for integrating information from multiple sensors.
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http://dx.doi.org/10.1016/j.talanta.2020.121733DOI Listing
February 2021

Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques.

J Pharm Biomed Anal 2021 Jan 10;192:113684. Epub 2020 Oct 10.

ITAP, INRAE, Montpellier SupAgro, University Montpellier, Montpellier, France; ChemHouse Research Group, Montpellier, France.

Near-infrared (NIR) spectra of pharmaceutical tablets get affected by light scattering phenomena, which mask the underlying peaks related to chemical components. Often the best performing scatter correction technique is selected from a pool of pre-selected techniques. However, the data corrected with different techniques may carry complementary information, hence, use of a single scatter correction technique is sub-optimal. In this study, the aim is to prove that NIR models related to pharmaceuticals can directly benefit from the fusion of complementary information extracted from multiple scatter correction techniques. To perform the fusion, sequential and parallel pre-processing fusion approaches were used. Two different open source NIR data sets were used for the demonstration where the assay uniformity and active ingredient (AI) content prediction was the aim. As a baseline, the fusion approach was compared to partial least-squares regression (PLSR) performed on standard normal variate (SNV) corrected data, which is a commonly used scatter correction technique. The results suggest that multiple scatter correction techniques extract complementary information and their complementary fusion is essential to obtain high-performance predictive models. In this study, the prediction error and bias were reduced by up to 15 % and 57 % respectively, compared to PLSR performed on SNV corrected data.
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http://dx.doi.org/10.1016/j.jpba.2020.113684DOI Listing
January 2021

Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles.

Foods 2020 Sep 8;9(9). Epub 2020 Sep 8.

VetAgro Sup, UMR1213 Herbivores, INRAE, Clermont Université, Université de Lyon, 63122 Saint-Genès-Champanelle, France.

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles ( and ) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d'Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising ( test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited RP = 0.99 and RMSEP = 0.103 g × 100 g DM.
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http://dx.doi.org/10.3390/foods9091254DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555109PMC
September 2020

Multi-block classification of chocolate and cocoa samples into sensory poles.

Food Chem 2021 Mar 25;340:127904. Epub 2020 Aug 25.

ITAP, Inrae, Montpellier SupAgro, University of Montpellier, Montpellier, France; ChemHouse Research Group, Montpellier, France.

The present study aims at developing an analytical methodology which allows correlating sensory poles of chocolate to their chemical characteristics and, eventually, to those of the cocoa beans used for its preparation. Trained panelists investigated several samples of chocolate, and they divided them into four sensorial poles (characterized by 36 different descriptors) attributable to chocolate flavor. The same samples were analyzed by six different techniques: Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), Solid Phase Micro Extraction-Gas Chromatography-Mass Spectroscopy (SPME-GC-MS), High-Performance Liquid Chromatography (HPLC) (for the quantification of eight organic acids), Ultra High Performance Liquid Chromatography coupled to triple-quadrupole Mass Spectrometry (UHPLC-QqQ-MS) for polyphenol quantification, 3D front face fluorescence Spectroscopy and Near Infrared Spectroscopy (NIRS). A multi-block classification approach (Sequential and Orthogonalized-Partial Least Squares - SO-PLS) has been used, in order to exploit the chemical information to predict the sensorial poles of samples. Among thirty-one test samples, only two were misclassified.
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http://dx.doi.org/10.1016/j.foodchem.2020.127904DOI Listing
March 2021

Multiblock Analysis to Relate Polyphenol Targeted Mass Spectrometry and Sensory Properties of Chocolates and Cocoa Beans.

Metabolites 2020 Jul 29;10(8). Epub 2020 Jul 29.

SPO, INRAE, Univ Montpellier, Institut Agro-Montpellier Supagro, 34060 Montpellier, France.

Chocolate quality is largely due to the presence of polyphenols and especially of flavan-3-ols and their derivatives that contribute to bitterness and astringency. The aim of the present work was to assess the potential of a quantitative polyphenol targeted metabolomics analysis based on mass spectrometry for relating cocoa bean polyphenol composition corresponding chocolate polyphenol composition and sensory properties. One-hundred cocoa bean samples were transformed to chocolates using a standard process, and the latter were attributed to four different groups by sensory analysis. Polyphenols were analyzed by an ultra-high-performance liquid chromatography (UPLC) system hyphenated to a triple quadrupole mass spectrometer. A multiblock method called a Common Component and Specific Weights Analysis (CCSWA) was used to study relationships between the three datasets, i.e., cocoa polyphenols, chocolate polyphenols and sensory profiles. The CCSWA multiblock method coupling sensory and chocolate polyphenols differentiated the four sensory poles. It showed that polyphenolic and sensory data both contained information enabling the sensory poles' separation, even if they can be also complementary. A large amount of variance in the cocoa bean and corresponding chocolate polyphenols has been linked. The cocoa bean phenolic composition turned out to be a major factor in explaining the sensory pole separation.
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http://dx.doi.org/10.3390/metabo10080311DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465875PMC
July 2020

Dataset of visible-near infrared handheld and micro-spectrometers - comparison of the prediction accuracy of sugarcane properties.

Data Brief 2020 Aug 12;31:106013. Epub 2020 Jul 12.

HélioSPIR, 361 Rue Jean François Breton, 34196 Montpellier, France.

In the dataset presented in this article, sixty sugarcane samples were analyzed by eight visible / near infrared spectrometers including seven micro-spectrometers. There is one file per spectrometer with sample name, wavelength, absorbance data [calculated as log (1/Reflectance)], and another file for reference data, in order to assess the potential of the micro-spectrometers to predict chemical properties of sugarcane samples and to compare their performance with a LabSpec spectrometer. The Partial Least Square Regression (PLS-R) algorithm was used to build calibration models. This open access dataset could also be used to test new chemometric methods, for training, etc.
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http://dx.doi.org/10.1016/j.dib.2020.106013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372143PMC
August 2020

Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging.

Talanta 2020 Aug 9;216:120993. Epub 2020 Apr 9.

UMR Physiologie de la Nutrition et du Comportement Alimentaire, AgroParisTech, INRAE, Université Paris-Saclay, 75005, Paris, France. Groupe "Chimiométrie pour la Caractérisation de Biomarqueurs - C(2)B"; Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 75005, Paris, France; ChemHouse Research Group, Montpellier, France.

The detection of adulterations in food powder products represents a high interest especially when it concerns the health of the consumers. The food industry is concerned by peanut adulteration since it is a major food allergen often used in transformed food products. Near-infrared hyperspectral imaging is an emerging technology for food inspection. It was used in this work to detect peanut flour adulteration in wheat flour. The detection of peanut particles was challenging for two reasons: the particle size is smaller than the pixel size leading to impure spectral profiles; peanut and wheat flour exhibit similar spectral signatures and variability. A Matched Subspace Detector (MSD) algorithm was designed to take these difficulties into account and detect peanut adulteration at the pixel scale using the associated spectrum. A set of simulated data was generated to overcome the lack of reference values at the pixel scale and to design appropriate MSD algorithms. The best designs were compared by estimating the detection sensitivity. Defatted peanut flour and wheat flour were mixed in eight different proportions (from 0.02% to 20%) to test the detection performances of the algorithm on real hyperspectral measurements. The number and positions of the detected pixels were investigated to show the relevancy of the results and validate the design of the MSD algorithm. The presented work proved that the use of hyperspectral imaging and a fine-tuned MSD algorithm enables to detect a global adulteration of 0.2% of peanut in wheat flour.
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http://dx.doi.org/10.1016/j.talanta.2020.120993DOI Listing
August 2020

Reduction of repeatability error for analysis of variance-Simultaneous Component Analysis (REP-ASCA): Application to NIR spectroscopy on coffee sample.

Anal Chim Acta 2020 Mar 16;1101:23-31. Epub 2019 Dec 16.

ITAP, Univ Montpellier, Irstea, Montpellier SupAgro, Montpellier, France.

A method to reduce repeatability error in multivariate data for Analysis of variance-Simultaneous Component Analysis (REP-ASCA) has been developed. This method proposes to adapt the acquisition protocol by adding a set containing repeated measures for describing repeatability error. Then, an orthogonal projection is performed in the row-space to reduce the repeatability error of the original dataset. Finally, ASCA is performed on the orthogonalized dataset. This method was evaluated on NIR spectral data of coffee beans. This study shows that the repeatability error due to physical variations between measurements can alter results of the analysis of variance. These effects are predominant in factors analysis and can be seen on spectra as constant or non-constant baselines. By reducing repeatability error with REP-ASCA, baselines are removed and factor analysis provides more information about chemical content of the factors of interest.
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http://dx.doi.org/10.1016/j.aca.2019.12.024DOI Listing
March 2020

Using spatial information for evaluating the quality of prediction maps from hyperspectral images: A geostatistical approach.

Anal Chim Acta 2019 Oct 30;1077:116-128. Epub 2019 May 30.

ITAP, Univ Montpellier, Irstea, Montpellier SupAgro, Montpellier, France.

Applying a calibration model onto hyperspectral (HS) images is of great interest because it produces images of chemical or physical properties. HS imaging is widely used in this way in food processing industries for monitoring product quality and process control. In this context, one of the main difficulties in the application of regression models to HS images is to evaluate the error of the obtained predictions, since in a proximal imaging set up, the size of the pixels is usually much smaller than the area required to obtain a wet chemical reference. Moreover, the selection of regression model parameters, such as the number of latent variables (LV) in a partial least squares (PLS) model, can modify the appearance of the prediction maps. The objective of this work is to propose an approach based on geostatistical indices to use spatial information of prediction maps for supporting the evaluation of regression models applied to HS images. This work stablishes a theoretical connection between linear regression model performance estimates and the spatial decomposition of variance in prediction maps, when the ground truth to estimate is spatially structured. This approach was tested in a simulated dataset and two real case studies. Geostatistical indices of the prediction maps were compared to model performance metrics for PLS models with increasing number of LV. The theoretical framework was proven by the results on the simulated dataset. In particular, the evolution of the nugget effect, C0, corresponded with the evolution of the random error of the model. Conversely, the error term of the model related with the slope of the model corresponded with the evolution of the structured variance observed in the prediction maps. On the real case studies, geostatistical indexes, extracted from the prediction maps, allowed to quantitatively evaluate the spatial structure of the estimations and complement the Root Mean Standard Error of Cross Validation (RMSECV) for the choice of optimal number of LV to consider in the model. The main advantage of this approach is that it does not require ground truth values. It could be used as a source of information for supporting the choice of optimum calibration parameters, such as the number of latent variables, or the choice of pre-treatments, complementing the traditional visual inspection of prediction maps with quantitative and objective metrics.
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http://dx.doi.org/10.1016/j.aca.2019.05.067DOI Listing
October 2019

Effect of the Architecture of Fiber-Optic Probes Designed for Soluble Solid Content Prediction in Intact Sugar Beet Slices.

Sensors (Basel) 2019 Jul 7;19(13). Epub 2019 Jul 7.

IRSTEA, UMR ITAP, Montpellier University, 361 Rue J.F. Breton, F-34196 Montpellier CEDEX 5, France.

Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. One of the main limitations for its competitiveness is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots, especially in breeding programs. In this context, a dedicated near infrared (NIR) fiber-optic probe based approach is proposed. NIR technology is widely used for the estimation of sugar content in vegetable products, while optic fibers allow a wide choice of technical properties and configurations. The objective of this research was to study the best architecture through different technical choices for the estimation of sugar content in intact sugar beet roots. NIR spectral measurements were taken on unprocessed sugar beet samples using two types of geometries, single and multiple fiber-probes. Sugar content estimates were more accurate when using multiple fiber-probes (up to R = 0.93) due to a lesser disruption of light specular reflection. In turn, on this configuration, the best estimations were observed for the smallest distances between emitting and collecting fibers, reducing the proportion of multiply scattered light in the spectra. Error of prediction (RPD) values of 3.95, 3.27 and 3.09 were obtained for distances between emitting and collecting fibers of 0.6, 1.2 and 1.8 µm respectively. These high RPD values highlight the good predictions capacities of the multi-fiber probes. Finally, this study contributes to a better understanding of the effects of the technical properties of optical fiber-probes on the quality of spectral models. In addition, and beyond this specificity related to sugar beet, these findings could be extended to other turbid media for quantitative optical spectroscopy and eventually to validate considered fiber-optic probe design obtained in this experimental study.
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http://dx.doi.org/10.3390/s19132995DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651724PMC
July 2019

Volatile compounds profiling by using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS). The case study of dark chocolates organoleptic differences.

J Mass Spectrom 2019 Jan;54(1):92-119

Centre des Sciences du Goût et de l'Alimentation (CSGA), AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, F-21000, Dijon, France.

Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.
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http://dx.doi.org/10.1002/jms.4317DOI Listing
January 2019

Chemometrics in analytical chemistry-part II: modeling, validation, and applications.

Anal Bioanal Chem 2018 Oct 2;410(26):6691-6704. Epub 2018 Aug 2.

IDAEA-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain.

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial "Chemometrics in analytical chemistry." This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields. This tutorial provides an overview of the popularity of chemometrics in analytical chemistry.
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http://dx.doi.org/10.1007/s00216-018-1283-4DOI Listing
October 2018

Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the early detection of apple scab disease.

Data Brief 2018 Feb 21;16:967-971. Epub 2017 Dec 21.

ITAP, Irstea, Montpellier SupAgro, Univ Montpellier, Montpellier, France.

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.
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http://dx.doi.org/10.1016/j.dib.2017.12.043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752091PMC
February 2018

Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools.

Anal Bioanal Chem 2017 Oct 3;409(25):5891-5899. Epub 2017 Aug 3.

IDAEA-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain.

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.
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http://dx.doi.org/10.1007/s00216-017-0517-1DOI Listing
October 2017

Potential of vis-NIR spectroscopy to monitor the silica precipitation reaction.

Anal Bioanal Chem 2017 Jan 28;409(3):785-796. Epub 2016 Nov 28.

Irstea, UMR ITAP, rue Jean-François Breton, 34196, Montpellier, France.

Controlling production online is an important issue for chemical companies. Visible and near-infrared (NIR) spectroscopy offers a number of important advantages for process monitoring, and has been used since the 1980s. For complex media such as silica precipitation samples, it is interesting to be able to study independently the scattering and absorption effects. From the scattering coefficient it is possible to extract information on the physical structure of the medium. In this work, the physical changes were monitored during a silica precipitation reaction by simple measurement of collimated transmittance NIR spectra. It is shown that it is possible to differentiate samples before and after the gel point, which is a key parameter for monitoring the process. From these NIR spectra the scattering coefficients were simply extracted, allowing a global vision of the physical changes in the medium. Then principal component analysis of the spectra allowed refinement of the understanding of the scattering effects, in combination with particle size monitoring.
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http://dx.doi.org/10.1007/s00216-016-0064-1DOI Listing
January 2017

Application-Dedicated Selection of Filters (ADSF) using covariance maximization and orthogonal projection.

Anal Chim Acta 2016 05 8;921:1-12. Epub 2016 Apr 8.

Irstea, UMR ITAP, 361 rue J-F Breton BP 5095, 34196 Montpellier Cedex 5, France.

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.
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http://dx.doi.org/10.1016/j.aca.2016.04.004DOI Listing
May 2016

Simulation Method Linking Dense Microalgal Culture Spectral Properties in the 400-750 nm Range to the Physiology of the Cells.

Appl Spectrosc 2016 06 18;70(6):1018-33. Epub 2016 Apr 18.

IRSTEA UMR ITAP, Montpellier, France.

This work describes a method to model the optical properties over the (400-750 nm) spectral range of a dense microalgal culture using the chemical and physical properties of the algal cells. The method was based on a specific program called AlgaSim coupled with the adding-doubling method: at the individual cell scale, AlgaSim simulates the spectral properties of one model, three-layer spherical algal cell from its size and chemical composition. As a second step, the adding-doubling method makes it possible to retrieve the total transmittance of the algal medium from the optical properties of the individual algal cells. The method was tested by comparing the simulated total transmittance spectra for dense marine microalgal cultures of Isochrysis galbana (small flagellates) and Phaeodactylum tricornutum (diatoms) to spectra measured using an experimental spectrophotometric setup. Our study revealed that the total transmittance spectra simulated for the quasi-spherical cells of Isochrysis galbana were in good agreement with the measured spectra over the whole spectral range. For Phaeodactylum tricornutum, large differences between simulated and measured spectra were observed over the blue part of the transmittance spectra, probably due to non-spherical shape of the algal cells. Prediction of the algal cell density, mean size and pigment composition from the total transmittance spectra measured on algal samples was also investigated using the reversal of the method. Mean cell size was successfully predicted for both species. The cell density was also successfully predicted for spherical Isochrysis galbana, with a relative error below 7%, but not for elongated Phaeodactylum tricornutum with a relative error up to 26%. The pigments total quantity and composition, the carotenoids:chlorophyll ratio in particular, were also successfully predicted for Isochrysis galbana with a relative error below 8%. However, the pigment predictions and measurements for Phaeodactylum tricornutum showed large discrepancies, with a relative error up to 88%. These results give strong support for the development of a promising tool providing rapid and accurate estimations of biomass and physiological status of a dense microalgal culture based on only light transmittance properties.
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http://dx.doi.org/10.1177/0003702816641270DOI Listing
June 2016

An iterative approach for compound detection in an unknown pharmaceutical drug product: Application on Raman microscopy.

J Pharm Biomed Anal 2016 Feb 28;120:342-51. Epub 2015 Dec 28.

IRSTEA, UMR ITAP 361, Montpellier, France.

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.
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http://dx.doi.org/10.1016/j.jpba.2015.12.038DOI Listing
February 2016

Setting local rank constraints by orthogonal projections for image resolution analysis: application to the determination of a low dose pharmaceutical compound.

Anal Chim Acta 2015 Sep 25;892:49-58. Epub 2015 Aug 25.

IRSTEA, UMR ITAP, 361, Montpellier, France.

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.
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http://dx.doi.org/10.1016/j.aca.2015.08.031DOI Listing
September 2015

Improvement of the chemical content prediction of a model powder system by reducing multiple scattering using polarized light spectroscopy.

Appl Spectrosc 2015 Jan 1;69(1):95-102. Epub 2014 Dec 1.

Irstea, IRSTEA UMR ITAP 361 rue Jean-François Breton - BP5095 F-34093 Montpellier, France.

Near-infrared spectroscopy (NIRS) is a powerful non-destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in line in industries, in vivo with biomedical applications, or in field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentration. Instead of spectral pre-processing, which is commonly used by NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, i.e., coupling polarized light with NIR spectrometry, to free spectra from scattering effect. This should allow us to retrieve linear and steady conditions for spectral analysis. When tested in visible-NIR (Vis-NIR) range (400-800 nm) on model media, mixtures of scattering and absorbing particles, the setup provided significant improvements in absorber concentration estimation precision as well as in the quality and robustness of the calibration model.
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http://dx.doi.org/10.1366/14-07539DOI Listing
January 2015

Combining linear polarization spectroscopy and the Representative Layer Theory to measure the Beer-Lambert law absorbance of highly scattering materials.

Anal Chim Acta 2015 Jan 14;853:486-494. Epub 2014 Oct 14.

Irstea, UMR ITAP, 361 Rue Jean-François Breton, 34000 Montpellier, France.

Visible and Near Infrared (Vis-NIR) Spectroscopy is a powerful non destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in-line in industries, in-vivo with biomedical applications or in-field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentrations. Instead of spectral pre-processing, which is commonly used by Vis-NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, based on Polarized Light Spectroscopy to improve the absorbance signal measurement on highly scattering samples. This method selects part of the signal which is less impacted by scattering. The resulted signal is combined in the Absorption/Remission function defined in Dahm's Representative Layer Theory to compute an absorbance signal fulfilling Beer-Lambert's law, i.e. being linearly related to concentration of the chemicals composing the sample. The underpinning theories have been experimentally evaluated on scattering samples in liquid form and in powdered form. The method produced more accurate spectra and the Pearson's coefficient assessing the linearity between the absorbance spectra and the concentration of the added dye improved from 0.94 to 0.99 for liquid samples and 0.84-0.97 for powdered samples.
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http://dx.doi.org/10.1016/j.aca.2014.10.014DOI Listing
January 2015

Distribution of a low dose compound within pharmaceutical tablet by using multivariate curve resolution on Raman hyperspectral images.

J Pharm Biomed Anal 2015 Jan 31;103:35-43. Epub 2014 Oct 31.

IRSTEA, UMR ITAP 361, Montpellier, France.

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.
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http://dx.doi.org/10.1016/j.jpba.2014.10.024DOI Listing
January 2015

Potential of a spectroscopic measurement method using adding-doubling to retrieve the bulk optical properties of dense microalgal media.

Appl Spectrosc 2014 1;68(10):1154-67. Epub 2014 Oct 1.

IRSTEA, UMR ITAP, 361 rue Jean-François Breton-BP5095, F-34093 Montpellier, France.

In the context of algal mass cultivation, current techniques used for the characterization of algal cells require time-consuming sample preparation and a large amount of costly, standard instrumentation. As the physical and chemical properties of the algal cells strongly affect their optical properties, the optical characterization is seen as a promising method to provide an early diagnosis in the context of mass cultivation monitoring. This article explores the potential of a spectroscopic measurement method coupled with the inversion of the radiative transfer theory for the retrieval of the bulk optical properties of dense algal samples. Total transmittance and total reflectance measurements were performed over the 380-1020 nm range on dense algal samples with a double integrating sphere setup. The bulk absorption and scattering coefficients were thus extracted over the 380-1020 nm range by inverting the radiative transfer theory using inverse-adding-doubling computations. The experimental results are presented and discussed; the configuration of the optical setup remains a critical point. The absorption coefficients obtained for the four samples of this study appear not to be more informative about pigment composition than would be classical methods in analytical spectroscopy; however, there is a real added value in measuring the reduced scattering coefficient, as it appears to be strongly correlated to the size distribution of the algal cells.
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http://dx.doi.org/10.1366/13-07308DOI Listing
May 2015
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