Logics of generalization: Derrida, grammatology and transdisciplinarity

This article seeks to explore some issues regarding the different modes of generality at stake in the formation of transdisciplinary concepts within the production of ‘theory’ in the humanities and social sciences. Focused around Jacques Derrida’s seminal account of ‘writing’ in his 1967 book Of Grammatology, the article outlines what it defines as a logic of generalization at stake in Derrida’s elaborations of a quasi-transcendental ‘inscription in general’. Starting out from the questions thereby raised about the relationship between such forms of generality and those historically ascribed to philosophy, the article concludes by contrasting Derrida’s generalized writing with more recent returns to ‘metaphysics’ in the work of Bruno Latour and others. Against the immediately ‘ontological’ orientation of much recent ‘new materialist’ or ‘object-oriented’ thought, the article argues for the necessity of ‘different levels of writing in general’ through a continual folding back of absolute generalization into historically specific disciplinary crossings and exchanges; something suggested by but never really developed in Derrida’s own work.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling 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 indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

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 microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage