Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk ...management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-infrared (NIR) and shortwave infrared (SWIR) domains: (1) physically-based approaches estimate EWT accurately and LMA poorly, while (2) statistically-based and machine learning (ML) methods provide accurate estimates of both LMA and EWT.
Using six experimental datasets including broadleaf species samples of >150 species collected over tropical, temperate and boreal ecosystems, we compared the performances of a physically-based method (PROSPECT model inversion) and a ML algorithm (support vector machine regression, SVM) to infer EWT and LMA based on leaf reflectance and transmittance. We assessed several merit functions to invert PROSPECT based on iterative optimization and investigated the spectral domain to be used for optimal estimation of LMA and EWT. We also tested several strategies to select the training samples used by the SVM, in order to investigate the generalization ability of the derived regression models.
We evidenced that using spectral information from 1700 to 2400 nm leads to strong improvement in the estimation of EWT and LMA when performing a PROSPECT inversion, decreasing the LMA and EWT estimation errors by 55% and 33%, respectively.
The comparison of various sampling strategies for the training set used with SVM suggests that regression models show limited generalization ability, particularly when the regression model is applied on data fully independent from the training set. Finally, our results demonstrate that, when using an appropriate spectral domain, the PROSPECT inversion outperforms SVM trained with experimental data for the estimation of EWT and LMA. Thus we recommend that estimation of LMA and EWT based on leaf optical properties should be physically-based using inversion of reflectance and transmittance measurements on the 1700 to 2400 nm spectral range.
•Limitations of physical modeling for the estimation of LMA need to be understood.•Species samples of >150 boreal, temperate and tropical species are studied.•Performance of PROSPECT inversion is reduced when near infrared is used.•Machine learning trained with experimental data shows poor generalization ability.•LMA and EWT can be accurately estimated with PROSPECT inverted from 1700 to 2400 nm.
The aim of this study was to investigate the use of biogas production rate kinetics for the monitoring of anaerobic co-digestion. Recent extensive studies of degradation pathways showed that ...acetoclastic methanogenesis is not always the main pathway. Hydrogenotrophic methanogenesis and syntrophic acetate oxidation can also dominate, mostly for operating conditions with high concentrations of ammonia or volatile fatty acids … These conditions are also known to cause instability in the digester’s operation especially in co-digestion due to substrate variability. Therefore, co-digestion experiments were conducted with several co-substrates using a continuously stirred 35-L tank reactor. Degradation pathways and their potential shifts were identified by monitoring variations in biogas production rate kinetics using a principal component analysis model. The shifts in the degradation pathways were used to monitor the process. These shift points were found to provide early warnings of instabilities in the anaerobic co-digestion process.
•LCFA, VFA & NH3 accumulations induce methanogenic pathway shifts in digesters.•Biogas production rate kinetics were used to monitor several AcoD experiments.•Start of pathway shift corresponded to early sign of imbalances in the process.•PCA performed on BPR kinetics, allowed methanogenic pathway shifts determination.•Warnings were given at low LCFA (0.52 g/l), VFA (0.19 g/l) & NH3 (0.16 g/l) levels.
Principal component analysis (PCA) is a popular method for process monitoring. However, most processes are time-varying, thus older samples are not representative of the current process status. This ...led to the introduction of adaptive-PCA based monitoring, such as moving window PCA (MWPCA). In this study, near-infrared spectroscopy (NIRS) responses to digester failures were evaluated to develop a spectral data processing tool. Tests were performed with a spectroscopic probe (350-2,500 nm), using a 35 L mesophilic continuously stirred tank reactor. Co-digestion experiments were performed with pig slurry mixed with several co-substrates. Different stresses were induced by abruptly increasing the organic load rate, changing the feedstock or stopping the stirring. Physicochemical parameters as well as NIRS spectra were acquired for lipid, organic and protein overloads experiments. MWPCA was then applied to the collected spectra for a multivariate statistical process control. MWPCA outputs, Hotelling T2 and residuals Q statistics showed that most of the induced dysfunctions can be detected with variations in these statistics according to a defined criterion based on spectroscopic principles and the process. MWPCA appears to be a multivariate statistical method that could help in decision support in industrial biogas plants.
Near infrared spectroscopy offers a number of important advantages for process monitoring. In addition to its numerous practical advantages, an important reason to use near infrared spectroscopy for ...process monitoring is its ability to supply versatile and multivariate information. However, in heterogeneous samples the interaction of light is complex and includes transmission, absorption, and scattering simultaneously which all affect spectra. The measurement of the signal at one point may be insufficient. A solution is to measure the medium at several points and to use specific multivariate analysis. In our study we propose to associate multipoint measurements with a common components and specific weight analysis. We monitored two media online by angular multipoint near infrared spectroscopy. For the first medium, in which only the scattering varies over time, the precipitation of silica was chosen to illustrate such a medium. For the second medium, both scattering and absorption vary, whereby microemulsions implemented for enhanced oil recovery illustrate this medium. The results showed, by combining multiangle measurements to common components and specific weight analysis, the interest of measuring at different angles. In the first case, two scattering regimes have been identified and it was possible to access the anisotropy coefficient during the silica precipitation reaction. In the second case study, on microemulsions, it was possible to identify the different phases and to separate the phenomena related to absorption and those related to diffusion. These encouraging results validate the interest of coupling multiangle measurements with multivariate multiblock analysis tools.
In this article, a set of 50 turbid liquid samples with different levels of absorption and scattering properties were prepared and measured at various orientations of polarizers and analyzers ...to obtain the 16 elements of the complete Muller matrix. Partial Least Square (PLS) was used to build calibration models in order to assess the potential of polarization spectroscopy through the elements of Muller matrix to predict chemical and physical parameters.
Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few ...dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.
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 submillimeter 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.
•We propose a model for ClOse-range Spectral ImagiNg of lEaves (COSINE).•COSINE models the variability due to bidirectional effects and leaf orientation.•COSINE must be combined with a leaf directional–hemispherical model such as PROSPECT.•The overall PROCOSINE model is validated based on VNIR and SWIR close-range images.•Model inversion allows a submillimeter retrieval of leaf biochemistry and orientation.
In the dataset presented in this article, 36 sludge samples were characterized. Rheological parameters were determined and near infrared spectroscopy measurements were realized. In order to assess ...the potential of near infrared spectroscopy to predict rheological parameters of sludge, Partial Least Square algorithm was used to build calibration models.
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•Regression models reduce response time and sample volume required for fuel characterization.•Data fusion modelling between two heterogenous data blocks improves models’ ...performance.•Identifying relevant descriptors when modelling results in further models’ accuracy.•Variable selection enables better understanding of the influence that process variables have on fuel properties.
This study evaluates the potential of variable selection to improve the performance of data fusion modelling to estimate diesel cetane number from NIR spectroscopy information acquired on total effluent samples obtained from the hydrocracking process and their operating variables. The evaluation conducted in this research was divided into four steps. First, predictive models were developed using each data block separately. Next, seven variable selection methods were applied on the NIR block, and eleven methods were applied on the process variable block. Then, with each data set generated from the variable selection analysis, single prediction models were generated and compared with those developed in the first step. Finally, data fusion was performed once the best variable selection method was defined for each data block. Two data fusion models were generated, a first using all the variables in the two blocks and a second using only the previously selected variables. In addition, the potential of the sequential and orthogonalized covariance selection (SO-CovSel) method was also analyzed. The results showed that the data fusion modelling using all variables from each data block improves the estimation of the diesel cetane number compared to single models (about 20% reduction of the RMSEP). However, using variable selection analysis before data fusion significantly improves the estimation of this property and leads to greater model stability regarding the RMSE's and r′s (about 47% of the RMSEP). The Covariance Selection (CovSel) method was the most efficient in the NIR data block, while for the process variable data block, it was the sequential backward floating feature selection method (SBFFS) that gave the best performance. The advantages offered by the variable selection resulted not only in having a more accurate prediction of the property but also in improving the analysis and understanding of the process by determining the variables that significantly impact the property studied.
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•Chemometric models optimize time and sample volume required for fuel characterization.•Diesel properties estimation from the total effluent NIR spectra is feasible.•Chemometrics ...enables better understanding of hydrocarbon compounds influence on diesel properties.
The work shown in this paper offers a fast and efficient alternative for estimating the cetane number of the diesel obtained from the distillation of the hydrocracking total effluent. In this study, the estimation of this diesel property was achieved through a partial least squares regression (PLSR) model using only the NIR spectrum of the hydrocracking total effluent. For calibrating and validating the PLS model, it was used a database containing the NIR spectra acquired on 98 total effluent samples and the cetane number measured on the 98 diesel fractions recovered from each total effluent sample distillation. The database was divided into the calibration and test data sets using the Kennard-Stone algorithm. The regression model developed exhibited good performance in estimating the studied property with errors of calibration (1.3), cross-validation (2.2), and prediction (2.0), close to the reproducibility of the reference method (±3.6). The alternative method for diesel cetane number estimation discussed in this article evidences its feasibility in optimizing diesel fuel characterization by reducing the necessity of the total effluent distillation. Furthermore, the results also show the potential of the alternative proposed to be applied in predicting other properties of fuels obtained from the hydrocracking process.
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