PhD position on ‘Optical sensing for subsurface meat quality assessment’

Visual and near infrared spectroscopy is increasingly implemented for quality control in the food industry. The fast and nondestructive character of spectroscopic measurements allows instantaneous feedback on the product quality. This is not only crucial for ensuring a high quality end product, but also allows optimization and timely intervention in the production process. The meat industry demands for solutions to monitor accurately, fast and nondestructively the quality of meat. Classical diffuse reflectance spectroscopy on meat has shown to be promising for determining chemical quality characteristics. However, accurately determining physical quality attributes such as tenderness, remains a challenge. This research project focuses on the potential of spatially resolved reflectance spectroscopy (SRS) combined with (chemometric) calibration models for the assessment of food quality, and meat quality in particular. Unlike conventional visual and near infrared point spectroscopy, a single SRS measurement yields multiple spectra. Each spectrum is obtained at a difference source-detector distance. Therefore, it contains its own specific signature of light absorption and light scattering by the sample. By combining these spectra, the effects of absorption and scattering can be separated and both become available as a valuable source of information to predict chemical and physical quality attributes of food.