Infrared hyperspectral analysis for non-invasive, inline fat content determination in bakery products

In the food industry, an accurate determination of the quantity of ingredients is of key importance. On the one hand, because this is prescribed by European regulations, which require a precise quantity indication on the food labeling. On the other hand, because small variations in the fat quantity and type produce a different taste experience for the consumer. Since current automated production lines for bakery products often cause fluctuations in the fat concentration from the target value, accurate and timely ingredient concentration measurements are required for quality control. However, conventional wet chemistry-based methods are a manual, destructive form of testing that require a considerable amount of time, typically several hours to days, to generate an outcome. To this end, this paper investigates the potential of machine learning-based regression using features extracted from hyperspectral imaging for the non-invasive and inline prediction of fat and moisture concentration in bakery products. While the conventional testing method is still advantageous in terms of accuracy, the results show that the proposed hyperspectral imaging approach is a promising alternative due to its real-time and non-destructive nature, offering the possibility to inspect larger quantities of products.