ESTIMATING DRY MATTER AND TOTAL SOLUBLE CONTENT IN APPLES USING A COMMERCIAL PORTABLE HYPERSPECTRAL IMAGING SYSTEM

Abstract. The quest for rapid, non-destructive, and precise technologies for fruit quality estimation is motivated by the needs across the whole food production chain. One of the emerging technologies fulfilling these requirements is spectral imaging. However, despite documented successes, the technology is yet to become established in commercial applications. The best results reported in the literature rely on fixed, non-portable dedicated setups, and controlled light conditions, which limits the potential use cases along the food production chain. In our study, we investigate the possibility of estimating dry matter content (DMC) and total soluble content (TSC) of store-bought apples in non-regulated indoor conditions using a commercial, portable, hand-held imaging system featuring a hyperspectral camera. The acquired images are transformed into per-fruit representative spectral profiles, pre-processed, and analyzed using partial least squares (PLS), the established method in the chemometrics community. We achieved the R² of 0.93 for TSC and 0.91 for DMC on the test dataset, with a mean absolute error of 0.71 °Brix for TSC and 0.7% for DMC, which is comparable to the state-of-the-art results presented in the literature. These results indicate that recent instrumental developments enable the deployment of spectral imaging systems in a wider range of tasks in food production, requiring portability and allowing for less stringent control of environmental conditions.