Nadine Hilgert

Milk acidification by Lactococcus lactis is improved by decreasing the level of dissolved oxygen rather than decreasing redox potential in the milk prior to inoculation

Although redox potential is very rarely taken into account in food fermentation it could be as influential as pH on bacterial activities. Lactococcus lactis is already known to exhibit a powerful reducing activity in milk but its reduction activity was shown to occur prior to its acidification activity with a potential interaction between these two lactococcal activities. Therefore, acidification lag-type phase could be shortened by decreasing the redox potential of milk before inoculation. As the redox potential is highly dependent on the dissolved oxygen level, our objective was to study their separate and combined influences on acidification and growth kinetics of pure L. lactis strains in milk. Results showed that high level of dissolved oxygen is significantly more influential on growth, and even more on acidification kinetics, than initial decreased redox potential of milk. Reduction of milk was drastic and mostly due to bacterial activity. The redox potential of milk only dropped when dissolved oxygen was entirely consumed. When there was no dissolved oxygen from the beginning, L. lactis immediately decreased the redox potential of milk and acidified afterwards. When the level of dissolved oxygen was initially high, acidification and reduction of milk occurred at the same time. Acidification kinetics was then biphasic with a slower rate during the aerobic stage and a faster rate during the anaerobic stage. The seven strains tested demonstrated diversity in both their acidification kinetics and their adaptation to high level of dissolved oxygen, independent of their growth kinetics. To conclude, we have shown that the level of dissolved oxygen in milk has a dramatic influence on acidification kinetics and could be used to control acidification kinetics in dairy industries.

How does winter pruning affect peach tree–Myzus persicae interactions?

Winter tree pruning is a cultural practice known to modify vegetative growth, which is likely to affect the development of pests. However, it has been poorly addressed as a cultural control method for diminishing the population levels of the green peach aphid, Myzus persicae (Sulzer) (Homoptera: Aphididae), in peach [Prunus persica (L.) Batsch (Rosaceae)] orchards. In this study, we conducted a 2‐year, on‐station experiment to evaluate how winter pruning affects peach–M. persicae interactions, by examining tree vegetative growth, aphid population dynamics, and crop yield and fruit quality. We collected data under an insect‐proof shelter on adult peach trees submitted to various levels of pruning and artificially infested with aphids. Our results showed that pruning enhanced shoot growth due to the proportion of growing shoots, which increased exponentially (10–60%), whereas the growth rate of growing shoots was not affected. The degree of infestation of peach trees increased with increasing pruning intensity. This effect was mainly due to the increase of the proportion of growing shoots, on which aphids developed better than on rosettes. In turn, the higher the aphid infestation, the higher the aphid‐induced shoot‐tip damage, leaf curling, and leaf fall that disturbed the growth of growing shoots. However, aphids did not considerably reduce fruit quality at harvest. They did not affect fresh fruit weight, and the refractometric index (indicator of sugar content) was reduced by only 3–4%. The relevance of winter pruning as a cultural method for pest control in orchards conducted under integrated fruit production guidelines is discussed.

Nonparametric identification and adaptive control of an anaerobic fluidized bed digester

Abstract In this paper, a new adaptive and robust control algorithm that is able to successfully deal with unpredictable internal changes (unmodeled dynamics) and external disturbances (changes in input) of the processes in an anaerobic digestion bioreactor is presented. The adaptive controller is based on a nonparametric statistical approach of the process identification. The regulation is done by optimally adapting the input liquid flow rate of the wastewater to designated changes in the output flow rate of the biogas (methane and carbon dioxide) resulting from the biological reaction. The fundamental advantage of this approach is its freedom from any a priori modeling assumptions about uncertain dynamic components. Experimental results, obtained using a pilot-scale 150 l fluidized bed reactor for the treatment of industrial wine distillery liquid wastes, demonstrates the usefulness of this approach in controlling biological processes.

Optimality of CUSUM Rule Approximations in Change-Point Detection Problems: Application to Nonlinear State–Space Systems

The well-known cumulative sum (CUSUM) sequential rule for abrupt model change detection in stochastic dynamic systems relies on the knowledge of the probability density functions of the system output variables conditional on their past values and on the system functioning mode at each time step. This paper shows how to build an asymptotically optimal detection rule under the common average run length (ARL) constraint when these densities are not available but can be consistently estimated. This is the case for nonlinear state-space systems observed through output variables: for such systems, a new class of particle filters based on convolution kernels allows to get consistent estimates of the conditional densities, leading to an optimal CUSUM-like filter detection rule (FDR).

Nonparametric Identification of Controlled Nonlinear Time Varying Processes

We are interested in the identification of an unknown time varying additive component of a controlled nonlinear autoregressive model, a problem of interest in the modeling and control of uncertain systems, such as those met in biotechnological processes. A kernel-based nonparametric estimator is proposed whose almost sure convergence is studied by means of a Lyapunov stabilizability assumption and laws of large numbers for martingales. We then adapt the general result to several classes of deterministic or random functional model uncertainties.

Ridge regression for the functional concurrent model

The aim of this paper is to propose estimators of the unknown functional coefficients in the Functional Concurrent Model (FCM). We extend the Ridge Regression method developed in the classical linear case to the functional data framework. Two distinct penalized estimators are obtained: one with a constant regularization parameter and the other with a functional one. We prove the probability convergence of these estimators with rate. Then we study the practical choice of both regularization parameters. Additionally, we present some simulations that show the accuracy of these estimators despite a very low signal-to-noise ratio. MSC 2010 subject classifications: Primary 62J05, 62G05, 62G20; secondary 62J07.

An agro-ecological model of the peach tree-Myzus persicae aphid system and its use to evaluate and design integrated management scenarios

We addressed the question of crop-pest management on the Prunus persica (L.) Batsch - Myzus persicae (Sulzer) (Hemiptera: Aphididiae) biological system. Firstly, we developed an agro-ecological model describing (i) peach-green aphid interactions as controlled by winter pruning and nitrogen fertilization, (ii) aphid insecticide induced-mortality, (iii) development of Harmonia axyridis Pallas (Coleptera: Coccinellidae) used as a biological control agent, and corresponding predation of aphids, (iv) and fruit quality. Secondly, we simulated management scenarios that combined different levels of cultural management with theoretical pest control strategies, and studied the relationships between management variables and system performances. We showed that (i) agronomical performances were largely controlled by cultural practices, while pest pressure was largely controlled by pest control strategies, and (ii) the way nitrogen and winter pruning influence pest numbers depended on the pest control strategy. Thirdly, we used a multi-objective optimization approach to design new management scenarios for three virtual grower production profiles. The selected scenarios using the conventional (chemical) pest control strategy were the best whatever the grower profile, but the selected scenarios using other pest control strategies had good performances. This study demonstrated that agro-ecological and multi-criteria crop-pest simulation models, associated with simulation-based evaluation and multi-objective optimization, have high potential for the implementation of integrated fruit production.

Some Optimal CUSUM Rule Extensions in Dynamic Model Parameter Change-Point Detection Problems

Abstract This paper proposes three extensions of the usual CUSUM rule for change point detection in dynamic systems, in situations where the application of the rule is problematic. The first two ones concern the case of nonlinear state-space systems indirectly observed and the case of uncertain systems. The third one improves the performance of the usual rule when applied to nonstationary systems, by ensuring a constant rate of false alarms. They all rely on kernel-based nonparametric estimation approaches.

Some Optimal CUSUM Rule Extensions in Dynamic Model Parameter Change-Point Detection Problems System Identification

This paper proposes three extensions of the usual CUSUM rule for change point detection in dynamic systems, in situations where the application of the rule is problematic. The first two ones concerns the case of nonlinear state-space systems indirectly observed and the case of uncertain systems. The third one improves the performance of the usual rule when applied to nonstationary systems, by ensuring a constant rate of false alarms. They all rely on kernel-based nonparametric estimation approaches.