International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017
p-ISSN: 2395-0072
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Artificial Neural Network Analysis in Food Science 1Dr.A.Jaganathan, 2S.Muguntha
Kumar
1Principal
/ Secretary, Food Craft Institute, Hoshiarpur, Punjab, India professor, Department of Hotel Management & Catering Science Muthayammal College of Arts & Science, Namakkal, Tamilnadu, India.
2Assistant
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Abstract – This paper brings the cultural diversity of
need to acknowledge the fact that the links can differ in their strength and importance. This is demonstrated, e.g., in social networks where the relationship between two long-time friends presumably differs from that between two casual business associates, and in ecological systems where the strength of a particular species pair-interaction is crucial for the population dynamics.
culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. In western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. A complete characterization of real networks requires us to understand the consequences of the uneven interaction strengths between a system’s components. Solely by changing the nature of the correlations between weights and network topology, the structure of the TSs can change from scale-free to exponential.
An artificial neural network, also called as simulated neural network or commonly just neural network is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network. In more practical terms neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. The original inspiration of the technique was from the examination of the central nervous system and the neurons. In a artificial neural network model, simple nodes are connected together to form a network of nodes- hence the term neural network.
In additionally, for some choices of weight correlations, the efficiency of the TSs increases with increasing network size, a result with potential implications for the design and scalability of communication networks. Here we discuss how these approaches can yield new insights both into the sensory perception of food and the anthropology of culinary practice. We also show that this development is part of a larger trend. Over the past two decades large-scale data analysis has revolutionized the biological sciences, which have experienced an explosion of experimental data as a result of the advent of high-throughput technology. We argue that food science is likely to be one of the next beneficiaries of large-scale data analysis, perhaps resulting in fields such as ‘computational gastronomy’.
A neural network does not have to be adaptive, its practical use comes with algorithms designed to alter the strengths (weights) of the connections in the network to produce a desired signal flow. These networks are also similar to the biological neural networks in the sense that the functions are performed collectively and in parallel by the units, rather than there being a clear delineation of sub-tasks to which various units are assigned. Similar large-scale data analysis methods have
more recently arrived in the social sciences as a result of rapidly growing mobile communications networks and online social networking sites. Here too data analysis offers a birds-eye perspective of large social networks and the opportunity to study social dynamics and human mobility on an unprecedented scale. The most recent research areas to be transformed by information technology are the Arts and Humanities, which have witnessed the emergence of ‘digital humanities’. As more and more literary and historical documents are digitized, it becomes possible to uncover fundamental relationships that underlie large corpora of literary texts, or long-term historical and political developments. A striking example is the discovery by Lieberman et al. that the regularization of verbs across 15 centuries of English is governed by a simple quantitative
Key Words: Food Science, Artificial Neural Networks, TS, Data analysis.
1. INTRODUCTION In this study of many complex systems has benefited from representing them as networks. There is now extensive empirical evidence indicating that the degree distribution of the nodes in many networks follows a power law, strongly influencing properties from network robustness to disease spreading. However, to fully characterize these systems, we
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