A Data Preprocessing Method for Food Detection Data Warehouse
- DOI
- 10.2991/icmmcce-17.2017.97How to use a DOI?
- Keywords
- detection data; food quality; data warehouse; data preprocessing.
- Abstract
The food detection data plays an extremely important and indispensable role in the evaluation of food quality. However, there are a number of detection items based on different evaluation standards for a product. The detection items can be divided into four categories: optimum index, negative index, scope index and sampling index by the evaluation standards. Their measurement units and technical requirements are different, so it is difficult to construct a data warehouse by using these complex detection data. Data preprocessing is necessary before constructing the data warehouse. The existing data processing methods are difficulty in evaluating the food quality comprehensive and efficiently. There are two problems, one is the inconsistent function relationship between the detection data and the processing result, and the other is the sampling index can't be processed. To address these problems, this paper proposes an approach for data preprocessing by calculating the quality index for detection data. This method can not only evaluate the food quality comprehensively and efficiently, but also provide a scientific basis for constructing the data warehouse.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ying Han AU - Chunlong Yao AU - Qiuyan Xin AU - Xiaoqiang Yu AU - Xu Li PY - 2017/09 DA - 2017/09 TI - A Data Preprocessing Method for Food Detection Data Warehouse BT - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017) PB - Atlantis Press SP - 518 EP - 524 SN - 2352-5401 UR - https://doi.org/10.2991/icmmcce-17.2017.97 DO - 10.2991/icmmcce-17.2017.97 ID - Han2017/09 ER -