Year: 2015
Type: dissertation
Abstract: With the current growing need for low production costs and high efficiency, the food industry is faced with a number of challenges, including maintenance of high-quality standards and assurance of food safety while avoiding liability issues. Meeting these challenges has become crucial in regards to grading food products for different markets. Food companies and suppliers need efficient, low-cost, and non-invasive quality and safety inspection technologies to enable them to satisfy different markets. With recent advancements in computer technology and instrumentation engineering, there have been significant advancements in techniques for assessment of food quality and safety. Machine vision and NIR spectroscopy are two of the more extensively applied methods for food quality and safety assessment and nowadays a new technique, combination of the two previous ones called Hyperspectral imaging, has become more and more popular. The aim of this work has been to analyze the capability of different optical systems in the NIR wavelength range for its possible implementation at line in a production chain for the food quality control in terms of adulteration detection. In order to do that, three different experiments with different analytical techniques have been performed: Detection of colt meat adulteration with beef using Hyperspectral imaging, detection of alcohol beverages adulteration with methanol using conventional NIR and finally, detection of fraud in the yogurts fat content using a NIR handheld device powered by IRIS. The prediction models for detection and quantification of different types of food adulteration that have been generated presents, in all cases good regression results, with a R2 values near to 1, and little calibration (RMSEC, RMSECV), and prediction (RMSEP) errors. Hyperspectral imaging technique seems to be an attractive solution for detecting adulterations in food industry. Therefore, the laborious and time-consuming conventional analytical techniques could be replaced or complemented by spectral data to provide a rapid and non destructive testing technique in the food industry.
Author: Sergi Fluvià Sabio
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W2179120420