EVK developed a solution for an industrial partner, using quantitative hyperspectral analysis to increase sorting specificity, that sorts in real-time and at the production line. This application example illustrates how EVK collaborates with partners, to develop sensor-based solutions for them that are tailored to their particular food safety, and process optimization, challenges.
Sensor-based quality assurance removes the need for manual intervention, and in addition, performs real-time selective removal of objects that do not meet quality criteria.
The detection of this defect requires a more sophisticated analysis approach than that used for dealing with simple, one-to-one correlations of a measurement value to an observable. Hence, detection using colour cameras (see Figure 1), or using NIR simple spectra, is insufficient for sorting good quality from bad quality, chicken breasts. The reason is that many observable chemometric differences, between normal and wooden chicken breasts, do not necessarily correlate with the actual defect. Many of the differences are natural variations that are not related to the product quality. In fact, the wooden defect is hard to define and is therefore actually done subjectively, by experienced workers, using haptic touch.
The solution to this complex problem requires a new approach: one which uses of detection of multiple chemometric features, that are correlated to defect expression and are quantitatively measured. A further step of logical feature combination creates a chemometric model, which has the precision and accuracy to detect wooden chicken breasts.
The chosen optical sensor system is a HELIOS NIR G2 CLASS near-infrared hyperspectral camera, with halogen lighting, and positioned to monitor a conveyor belt. Data analysis and the creation of a chemometric model are performed with the EVK SQALAR software packet.