By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xuan-Tien Doan ; Sch. of Electr. & Electron. Eng., Univ. of Manchester, Manchester ; Kidd, P.T. ; Goodacre, R. ; Grieve, B.D.

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.

Published in:

Signal Processing, 2008. ICSP 2008. 9th International Conference on

Date of Conference:

26-29 Oct. 2008