Sugar content study

RESULTS from a recent study have demonstrated how sensor measurements and machine learning can be used to evaluate the sugar content in potatoes, to monitor sugar content during storage and result in higher quality processed potato products. 

The study looked at the effects of utilising different data sets collected over different seasons and was partially funded by the USDA-ARS-State Partnership Potato Program. It was carried out by Ahmed Rady, Daniel Guyer and Nicholas Watson, who have affiliations to the University of Nottingham, Alexandria University (Egypt) and Michigan State University.

It attempted to use data acquired from three spectroscopic systems, processed either individually or fused for building generic regression and classification models, for glucose and sucrose content evaluation in potato tubers. 

Although the model prediction results using data across different growing seasons were not as good as those developed for a single season, they could be improved by including data from more seasons and different cultivars, those carrying out the study concluded. 

For industrial adoption of these techniques, it was felt to be important to develop models that can be applied across multiple seasons. Data was collected over the three seasons, 2008, 2009, and 2011, and different machine learning algorithms were implemented for wavelength selection and model development. 

Sensor fusion generally showed improved model accuracy performance over individual sensors for both regression and classification although the improvement was greater for the regression models. 

In this study, classification models were found to perform better than regression models except when using one season’s model on data acquired from another season. Classification models are easier to develop (as they require less data) and could be more suitable for generating a rapid indication of potato condition.

For more details, visit https://link.springer.com/article/10.1007/s12161-020-01886-1#Fun