Novel Multipoint Near Infrared Spectroscopy: Overcoming the Limitation of Sample Heterogeneity by Prediction of Minced Beef in Static and Motion Conditions
Document Type Presentation
This presentation was delivered by lead researcher Yash Dixit at the International Congress on Engineering and Food which was held in Quebec City, Canada, June 14-18, 2015. It is representative of initial research conducted by the School of Food Science and Environmental Health under the direct supervision and guidance of Dr Carl Sullivan
This project will develop novel process spectral based systems to predict meat quality and ensure that the product is not contaminated.
A prototype area scanning NIR Hyperspectral Imaging (HSI) system with high spatial and spectral resolution will be developed and assessed for detailed meat inspection and referenced against current line scanning HSI systems available to the consortium.
Secondly, a novel real-time multi-point NIR system will be developed for "quasi" imaging of meats, offering full speed on-line assessment from varying fields of view. Also, a novel portable HSI system with high spatial resolution features will be assessed and compared to the lab based systems.
Finally, a novel guided wave microwave spectrometry technology will be assessed for on-line monitoring of ground meat products. Quantification and classification algorithms will be developed for the prediction and mapping of meat quality indices including major beef constituents (e.g. moisture, protein and fat), physical properties (pH, colour, water holding capacity, and slice shear force) and consumer assessed eating quality (odour, flavour, juiciness and tenderness).
The high resolution HSI systems will also be assessed for their efficacy to detect gross contamination and assure product quality. This project will enable the transfer from the laboratory to the processing plant of novel platform technologies to improve the competitiveness, sustainability and international reputation of the Irish meat industry.