English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

An increasing shortening of product life cycles, as well as the trend towards highly individualized food products, force manufacturers to digitize their own production chains. Especially the collection, monitoring, and evaluation of food data will have a major impact in the future on how the manufacturers will satisfy constantly growing customer demands. For this purpose, an automated system for collecting and analyzing food data was set up to promote advanced production technologies in the food industry. Based on the technique of laser triangulation, various types of food were measured three-dimensionally and examined for their chromatic composition. The raw data can be divided into individual data groups using clustering technologies. Subsequent indexing of the data in a big data architecture set the ground for setting up real-time data visualizations. The cluster-based back-end system for data processing can also be used as an organization-wide communication network for more efficient monitoring of companies’ production data flows. The results not only describe the procedure for digitization of food data, they also provide deep insights into the practical application of big data analytics while helping especially small- and medium-sized enterprises to find a good introduction to this field of research.

Practical Classification and Evaluation of Optically Recorded Food Data by Using Various Big-Data Analysis Technologies