Resolving quality issues in food manufacturing with advanced data analysis
Multivariate data analysis tools help manufacturers get deeper insights and make sense of their complex data, from understanding raw material properties to identifying critical process variables and the relationships between them which affect quality.
This case study explains how a chocolate manufacturer used best practice to resolve a quality issue and optimise their production processes using multivariate data analysis and experimental design.
- The challenge: Quality issue resulting in a large amount of end product being scrapped
- The solution: Used multivariate data analysis and Designed Experiments to identify and manage the variables causing the quality problem
- Methods used: Principal Component Analysis (PCA), various regression methods, Fractional factorial designs
- The result: Resolved the quality problem, enabling Nidar to reduce waste and process failures, saving approximately $1M per year. The newfound knowledge was transferred to other production lines.
This case study is restricted - login or subscribe free to access
Thank you for visiting our website. To access this content in full you'll need to login. It's completely free to subscribe, and in less than a minute you can continue reading. If you've already subscribed, great - just login.
Why subscribe? Join our growing community of thousands of industry professionals and gain access to:
- bi-monthly issues in print and/or digital format
- case studies, whitepapers, webinars and industry-leading content
- breaking news and features
- our extensive online archive of thousands of articles and years of past issues
- ...And it's all free!
Related content from this organisation
- Case study: Bringing science to the art of brewing better beers
- CAMO Software exhibiting at IFPAC 2015
- Dairy Processing supplement 2014
- CAMO release Service Pack 2014 for The Unscrambler® X Multivariate Data Analysis and Design of Experiments software
- Resolving quality issues in food manufacturing with advanced data analysis