Non-Destructive Inspection of Insects in Chocolate Using near Infrared Multispectral Imaging

Abstract

Foreign matters in food products strongly affect the commercial viability of food production companies. To monitor and improve the reliability of food production processes, fast and non-destructive online inspection methods are urgently required. In this study, we exploited the high transmittance of near infrared (NIR) light, and designed an NIR transmission-type imaging device that detects low-density foreign matters (such as insects) inside chocolate. Insect parts had consistently lower transmittance than chocolate parts. To enhance the important differences between the two parts, the images were processed using principal component analysis. Furthermore, an algorithm for detecting the insect parts was developed in conjunction with image threshold processing and Laplacian of Gaussian edge detection. A favourable detection rate (93%) was achieved using spectral regions 840–870 nm, 870–900 nm and 900–930 nm. The results suggested that NIR multispectral imaging is useful for detecting low-density matter (insects) in the chocolate manufacturing industry.