Skip navigation

Digital imaging based classification and authentication of granular food products

Digital imaging based classification and authentication of granular food products

Carter, R.M., Yan, Y. and Tomlins, Keith (2006) Digital imaging based classification and authentication of granular food products. Measurement Science and Technology, 17 (2). pp. 235-240. ISSN 0957-0233 (doi:10.1088/0957-0233/17/2/002)

Full text not available from this repository.

Abstract

In the food industry there are many types of product that are in the form of
particles, granules or grains. Consistent material size and quality within any
given sample is an important requirement that is well known in industry. In
addition it is possible that samples of material may be of unknown type or
have been subject to adulteration, thus making material authentication a real
requirement. The present work implements an advanced, but cost-effective,
digital imaging and image processing technique to characterize granular
foodstuffs either in real time process control or in an off-line, sample-based,
manner. The imaging approach not only provides cost-effective and rugged
hardware when compared with other approaches but also allows precise
characterization of individual grains of material. In this paper the imaging
system is briefly described and the parameters it measures are discussed.
Both cluster and discriminant analyses are performed to establish the
suitability of the measured parameters for authenticity study and a simple
fuzzy logic is implemented based on the findings. Tests are performed,
using rice as an example, to evaluate the performance of the system for
authenticity testing, and encouraging results are achieved

Item Type: Article
Uncontrolled Keywords: granular food, rice, imaging, particle size distribution, fuzzy logic, authentication, classification
Subjects: T Technology > TP Chemical technology
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TX Home economics
Faculty / Department / Research Group: Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department
Related URLs:
Last Modified: 11 Nov 2011 12:06
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/3162

Actions (login required)

View Item View Item