Regression analysis for supply chain logged data:A simulated case study on shelf life prediction

摘要：

The paper illustrates that valuable information can be mined from temperature data collected along the perishable food produce supply chain.Three regression techniques: Ordinary Least Square (OLS),Principal Component Regression (PCR) and Latent Root Regression (LRR) have been used to predict remaining shelf life of tropical seafood products.The results show that LRR is the best of the three regression techniques and works well in predicting remaining shelf life for tropical seafood.The results demonstrate the potential usefulness of utilizing automated temperature data collection (e.g.using RFID sensors) to help achieve a challenging business objective–remote real-time prediction of remaining shelf life of chilled foods.

作者: Xuan-Tien Doan P.T.Kidd R.Goodacre B.D.Grieve

作者单位: Syngenta Sensor University Innovation Centre,School of Electrical & Electronic Engineering,Universit School of Chemistry & Manchester Interdisciplinary Biocentre,University of Manchester,Manchester,M1

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）