Abstract
Data mining, also referred to as knowledge extraction from databases, is one of the most important analytical methods for identifying the relationships between the various elements of the information collected in order to discover the useful knowledge and support of strategic decision-making and sustainable development systems in various industries. Mathematical modeling, quantitative analysis of data and new algorithms can identify new relationships between different data, which in turn leads to competitive advantage. Olive oil is one of the most important agricultural crops due to its digestive properties and economic status. However, olive oil production is a costly process which causes an expensive price of the final product. The most jobbery ways during olive oil production consist of mixing other oils such as maize, sunflower, Canola and corn into the olive oil. So, the aim of this study was to develop a dielectric-based system to Authenticate in olive oil using cylindrical capacitive sensor. For categorizing of fake olive oil by using frequency specification, Support vector machine, linear regression, Ensemble Trees and Gaussian was developed. A set of 16 samples of olive oil, sunflower, canola and corn oil which mixed with different ratio of Authentication, were used for calibration and evaluation of developed system.
Highlights
New information and communication technologies, as well as decision support technologies, can be very effective in providing timely, accurate, and relevant information to users by collecting, storing, evaluating, interpreting, analyzing, retrieving and disseminating information to specific users [1]
Data mining takes advantage of the progress made in artificial intelligence and statistics
Samples of olive oil provided from Khorramshahr Oil Company and produced at Rudbar oil plant located in Manjil
Summary
New information and communication technologies, as well as decision support technologies, can be very effective in providing timely, accurate, and relevant information to users by collecting, storing, evaluating, interpreting, analyzing, retrieving and disseminating information to specific users [1]. Data mining takes advantage of the progress made in artificial intelligence and statistics. Both of these areas work in model identification and data classification issues and will, in effect, be directly used in data mining, and both groups are active in identifying and using neural networks and decision trees [2]. Data mining simultaneously utilizes several disciplines such as artificial intelligence, machine learning, neural networks, statistics, pattern recognition, and science-based systems. Detecting the purity of different materials can be done in a variety of ways [3]. These methods are very damaging, costly and time-consuming. The purpose of the development of these methods is to estimate the quantitative and qualitative characteristics of the materials rapidly, non-destructively and reliably [4]
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