Phenotyping is essential in the process of varietal selection. In the case of sugar beets, richness (g/100g), that is, sugar content, is the key information. The need to acquire this information in a rapid, non-destructive and cheap manner leads the sugar industry to look for portable solutions that enable the suitable field measurements. In this work, a low-cost handheld and narrow visible-NIR spectral range microspectrometer is assessed for its ability to provide such information. During a two-year campaign from 2017 to 2018, 649 samples of sugar beet were measured. The resulting data, along with the reference values for richness, were used to build a predictive model with partial least squares (PLS) regression. Acceptable performance in the estimation of richness from both 2017 data (SEP = 0.84 g/100 g) and 2018 data (SEP = 0.90 g/100 g) is achieved. This study also shows that updating the spectral database is possible by calibration transfer models. From the different tested transfer strategies, the combination of model update and slope-bias correction achieves the best performance, demonstrating that the use of 2017 model on different years is possible and only 75 new sugar beets are necessary to guarantee a richness error lower than 1.05 g/100 g. This work suggests that the molecular sensor could offer a useful tool for a rapid, low cost and non-destructive prediction of richness in sugar beets.