A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiolog

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Drought Impact Is Alleviated in Sugar Beets (Beta vulgaris L.) by Foliar Application of Fullerenol Nanoparticles

Over the past few years, significant efforts have been made to decrease the effects of drought stress on plant productivity and quality. We propose that fullerenol nanoparticles (FNPs, molecular formula C-60(OH)(24)) may help alleviate drought stress by serving as an additional intercellular water supply. Specifically, FNPs are able to penetrate plant leaf and root tissues, where they bind water in various cell compartments. This hydroscopic activity suggests that FNPs could be beneficial in plants. The aim of the present study was to analyse the influence of FNPs on sugar beet plants exposed to drought stress. Our results indicate that intracellular water metabolism can be modified by foliar application of FNPs in drought exposed plants. Drought stress induced a significant increase in the compatible osmolyte proline in both the leaves and roots of control plants, but not in FNP treated plants. These results indicate that FNPs could act as intracellular binders of water, creating an additional water reserve, and enabling adaptation to drought stress. Moreover, analysis of plant antioxidant enzyme activities (CAT, APx and GPx), MDA and GSH content indicate that fullerenol foliar application could have some beneficial effect on alleviating oxidative effects of drought stress, depending on the concentration of nanoparticles applied. Although further studies are necessary to elucidate the biochemical impact of FNPs on plants; the present results could directly impact agricultural practice, where available water supplies are often a limiting factor in plant bioproductivity

The catatonic dilemma expanded

Catatonia is a common syndrome that was first described in the literature by Karl Kahlbaum in 1874. The literature is still developing and remains unclear on many issues, especially classification, diagnosis, and pathophysiology. Clinicians caring for psychiatric patients with catatonic syndromes continue to face many dilemmas in diagnosis and treatment. We discuss many of the common problems encountered in the care of a catatonic patient, and discuss each problem with a review of the literature. Focus is on practical aspects of classification, epidemiology, differential diagnosis, treatment, medical comorbidity, cognition, emotion, prognosis, and areas for future research in catatonic syndromes

Motion Analysis System for Robot Traction Device Evaluation and Design

Abstract Though much research has been conducted regarding traction of tires in soft granular terrain, little empirical data exist on the motion of soil particles beneath a tire. A novel experimentation and analysis technique has been developed to enable detailed investigation of robot interactions with granular soil. This technique, the Shear Interface Imaging Analysis method, provides visualization and analysis capability of soil shearing and flow as it is influenced by a wheel or excavation tool. The method places a half-width implement (wheel, excavation bucket, etc.) of symmetrical design in granular soil up against a transparent glass sidewall. During controlled motion of the implement, high-speed images are taken of the sub-surface soil, and are processed via optical flow software. The resulting soil displacement field is of very high fidelity and can be used for various analysis types. Identification of clusters of soil motion, shear interfaces and shearing direction/magnitude allow for analysis of the soil mechanics governing traction. The Shear Interface Imaging Analysis Tool enables analysis of robot-soil interactions in richer detail than possible before. Prior state-of-art technique relied on longexposure images that provided only qualitative insight, while the new processing technique identifies sub-millimeter gradations in motion and can do so even for high frequency changes in motion. Results are presented for various wheel types and locomotion modes: small/large diameter, rigid/compliant rim, grouser implementation, and push-roll locomotion

Stability analysis of an articulated agri-robot under different central joint conditions

4noIn hilly terrains, the exploitation of (semi-)autonomous systems able to travel nimbly and safely on different terrains and perform agricultural operations is still far from reality. In this perspective, the articulated 4-wheeled system, that shows an optimal steering capacity and the possibility to adapt to uneven terrains thanks to a passive degree of freedom on the central joint, is one of the most promising mobile wheeledrobot architectures. In thiswork, the instability of this robotic platform is evaluated in the two different conditions, i.e. phase I and phase II [1], and the effect of blocking the passive DoF of the central joint investigated in order to highlight possible stabilizing conditions and best manoeuvring practices for overturning avoidance. In order to do so, a quasi-static model of the robotic platform has been developed and implemented in a Matlab™ simulator thanks to which the different conditions have been studied. © Springer International 2016.reservedmixedVidoni, R.; Carabin, G.; Gasparetto, A.; Mazzetto, F.Vidoni, R.; Carabin, G.; Gasparetto, Alessandro; Mazzetto, F

Cloud-based management of machine learning generated knowledge for fleet data refinement

The modern mobile machinery has advanced on-board computer systems. They may execute various types of applications observing machine operation based on sensor data (such as feedback generators for more efficient operation). Measurement data utilisation requires preprocessing before use (e.g. outlier detection or dataset categorisation). As more and more data is collected from machine operation, better data preprocessing knowledge may be generated with data analyses. To enable the repeated deployment of that knowledge to machines in operation, information management must be considered; this is particularly challenging in geographically distributed fleets. This study considers both data refinement management and the refinement workflow required for data utilisation. The role of machine learning in data refinement knowledge generation is also considered. A functional cloud-managed data refinement component prototype has been implemented, and an experiment has been made with forestry data. The results indicate that the concept has considerable business potential.acceptedVersionPeer reviewe