Véronique Bellon-Maurel

EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits

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

Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry

Abstract This paper aims at assessing the capability of high-speed analytical devices, such as aroma sensors (“electronic noses”), Fourier Transform InfraRed (FT-IR) and ultraviolet spectrometers to classify white grape musts (grape juices before fermentation) in variety categories. Due to the complexity of the signal generated, specific data processing techniques have been developed and are described here. First, a pre-processing technique, based on Genetic Algorithms , is applied to spectra to improve spectrometer efficiency without expert knowledge in spectrometry; by selecting the most discriminant subsets of wavelengths, this stochastic method tends to reduce over-fitting and improves classification results. Secondly, the Partial Least Squares Regression technique is adapted to a pattern recognition problem, using Partial Least Squares-Discriminant Analysis , a multivariate classification technique. These devices and data processing techniques are applied to more than 100 must samples. FT-IR spectrometry is the most satisfactory technique with a 9.6% classification error level. Finally, outputs of the three individual sensors are combined in a “low-level” fusion method, by concatenating the individual sensor signals. This straightforward fusion method does not significantly improve results.

Life cycle assessments of urban water systems: a comparative analysis of selected peer-reviewed literature.

Water is a growing concern in cities, and its sustainable management is very complex. Life cycle assessment (LCA) has been increasingly used to assess the environmental impacts of water technologies during the last 20 years. This review aims at compiling all LCA papers related to water technologies, out of which 18 LCA studies deals with whole urban water systems (UWS). A focus is carried out on these 18 case studies which are analyzed according to criteria derived from the four phases of LCA international standards. The results show that whereas the case studies share a common goal, i.e., providing quantitative information to policy makers on the environmental impacts of urban water systems and their forecasting scenarios, they are based on different scopes, resulting in the selection of different functional units and system boundaries. A quantitative comparison of life cycle inventory and life cycle impact assessment data is provided, and the results are discussed. It shows the superiority of information offered by multi-criteria approaches for decision making compared to that derived from mono-criterion. From this review, recommendations on the way to conduct the environmental assessment of urban water systems are given, e.g., the need to provide consistent mass balances in terms of emissions and water flows. Remaining challenges for urban water system LCAs are identified, such as a better consideration of water users and resources and the inclusion of recent LCA developments (territorial approaches and water-related impacts).

Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties

Abstract The objective of this study is to present a fusion method based on the Bayesian inference to combine the outputs of various sensors. The sensors studied here are aroma sensors, FT-IR and UV spectrometers. The application deals with classifying musts of white grapes according to their variety. The fusion procedure is not based on the combination of the signals, but of the class assignments provided individually by each sensor. Two methods have been developed based on the Bayesian inference: the Bayesian minimum error fusion rule and the minimum risk rule. The latter involves both experimental knowledge, in computing error probability values, and expert knowledge, through the level of error costs. The paper presents the mathematical theory concerning the Bayesian approach and the results obtained on white grape classification. This effective fusion method leads to a significant improvement in the grape variety discrimination: the final misclassification error is 4.7%, whereas the best individual sensor (FT-IR) gave a misclassification error twice as high, i.e. 9.6%. Bayesian fusion proved to be very well suited to the combination of all kinds of analytical measurements or sensors (curves or single value outputs), as long as they provide individual classification outputs. Furthermore, Bayesian fusion is able to cope with sensors providing large, noisy and redundant data as well as sensors showing very dissimilar efficiency levels.

Aerobic Biodegradation of Polymers in Solid-State Conditions: A Review of Environmental and Physicochemical Parameter Settings in Laboratory Simulations

During the last few years, biodegradable polymers have been developed to replace petrochemical polymers. Until now, research devoted to these polymers essentially focused on their biodegradability. There is now a need to bear out their nontoxicity. To verify this, the biodegradation must be carried out in accelerated laboratory tests which allow the metabolites and residues to be recovered. To reproduce the natural conditions (compost, field) as closely as possible, degradation experiments must be run on solid-state substrates. We review studies of aerobic degradation in solid-state substrates. This article focuses in particular on the environmental, physical, and chemical parameters (such as substrate nature, moisture, temperature, C/N ratio, and pH) that influence biodegradation kinetics. This study also aims at finding the solid substrate most adapted to residues and metabolite recovery. The most significant parameters would appear to be the substrate type, moisture content, and temperature. Inert substrates such as vermiculite are well suited to residue extraction. This review also opens the field to new research aimed at optimizing conditions for aerobic solid-state biodegradation and at recovering the metabolites and residues of this degradation process.

An automated test for measuring polymer biodegradation

The biodegradability of polymer materials as evaluated by the modified Sturm test is labor-intensive, cumbersome and costly and also tends to cumulate errors. An automated system for the measurement of carbon dioxide would overcome many of these disadvantages. We describe here a method in which CO2 was determined by IR spectroscopy. We compared the results with those from trapping CO2 in a solution of barium hydroxide (Ba(OH)2) followed by manual titration. The automated system was more reproducible, less costly and more compact. The automated system could also be employed to measure the biodegradability of other substances such as oils and detergents.

Robustness of models based on NIR spectra for sugar content prediction in apples

The sugar content of Golden Delicious apples is predicted using near infrared (NIR) spectrometry. The study focuses on the metrological characteristics of the sugar content measurement and external parameters involved in the lack of robustness of the NIR-based model. The external parameters were fruit temperature, spectrometer temperature and ambient light. The first two factors influenced the prediction accuracy: (i) a fruit temperature variation altered the prediction, the relationship seems to be described by a non-linear model within the considered temperature range, (ii) a variation of the spectrometer temperature also altered the prediction, the relationship is described by a linear function for a temperature between 4 and 30°C. Ambient light did not show to have any influence on the NIR-based model. The analysis of the metrological parameters showed a satisfactory repeatibility in sugar prediction with a low error, 0.073°Brix. The model reproducibility was good regarding bias-corrected standard error of prediction (SEPc) without significant differences between experiments, on the other hand a bias remained even if the previous parameters were maintained constant. These results will be taken into account in future measurements, in order to improve the robustness of the NIR-based model developed for apples.

Adapting the LCA framework to environmental assessment in land planning

PurposeSince the implementation of the European directive (EC/2001/42) on strategic environmental assessment, an ex ante evaluation has become mandatory for plans and programs. This requirement could have significant consequences for the environment. Local authorities, who are in charge of land planning issues, must therefore conduct such assessments. However, they are faced with lack of uniform methodology. The aim of this paper is thus to propose a methodological framework for the required environmental assessment stages in land planning.MethodsLife cycle assessment (LCA) has been identified as a promising tool to perform environmental assessment at a meso-level (i.e., territories). Yet, the standardized LCA framework has never been used for assessing the environmental impacts of a territory as such, which can be explained by the complexity that its application would involve. Four major methodological bottlenecks have been identified in this paper, i.e., (1) functional unit definition, (2) boundary selection, (3) data collecting, and (4) the refinement of the life cycle impact assessment phase in order to provide useful indicators for land planning. For each of these challenges, recommendations have been made to adapt the analytical framework of LCA.Results and discussionA revised framework is proposed to perform LCA of a territory. One of the major adaptations needed concerns the goal and scope definition phase. Henceforth, the association of a territory and the studied land planning scenario, defined by its geographical boundaries and its interactions with other territories, will be designated as the reference flow in LCA. Consequently, two kinds of indicators will be determined using this approach, i.e., (1) a vector of environmental impacts generated (conventional LCA) and (2) a vector of land use functions provided by the territory for different stakeholders (e.g., provision of work, recreation, culture, etc.). This revised framework has been applied to a theoretical case study in order to highlight its utility in land planning.ConclusionsThis work is a first step in the adaptation of the LCA framework to environmental assessment in land planning. We believe that this revised framework has the potential to provide relevant information in decision-making processes. Nonetheless, further work is still needed to broaden and deepen this approach (i.e., normalization of impacts and functions, coupled application with GIS, uncertainties, etc.).

Optimisation of electronic nose measurements. Part I: Methodology of output feature selection

Abstract Although very often cited in publications dealing with food products, electronic noses still pose many problems. One is the extraction of features from the response curve; in general, only the adsorption maximum is retained and input into a classification system. This paper describes a statistic-based methodology developed to extract the most pertinent features from the outputs of SnO 2 -gas sensor array. Several features are extracted from each sensor curve and also from its primary and secondary derivatives. They are then sorted, taking into account three specific indexes, designed to describe the repeatability, discrimination power and their redundancy. To generalise this approach, the acquisitions are carried out using various operating conditions. This protocol is applied to a set of model mixtures, representing wine with satisfactory and unsatisfactory tart or vinegar flavour. This paper shows that relevant information can be obtained from the curve maximum, but also from features related to derivatives. Moreover, the most efficient features are the same for the five sensors, which would seem to indicate that they should also be the most suitable ones for all SnO 2 sensors.

Evaluation of Material Biodegradability in Real Conditions–Development of a Burial Test and an Analysis Methodology Based on Numerical Vision

This work validated a burial protocol for in situ testing and presents a robust, repeatable and time-saving technique to measure degraded areas in the sample, i.e. an image analysis method. 1440 specimens of degraded samples have been compiled in a data base. To this end, twenty samples presenting different levels of biodegradability (i.e. PHBV/HV, PLA, PCL, PCL-Starch, paper, PE, PE-Starch) were buried at 4 different locations and then disinterred at 4, 6, 9, 12, 18, and 24-month intervals. The biodegradation levels of these samples were determined by computing weight and area loss. Weight loss was measured after careful cleaning, whereas area loss was quantified using image analysis. Image analysis gives reliable information on visual pollution while only requiring a rudimentary and thus quicker cleaning of the samples.