Mango DMC and NIR spectra

Full description

The dry matter content of mango fruit is an important metric for determining harvest maturity and ensuring the eating quality of the ripened fruit. Near infrared spectroscopy can be used as a non-invasive method of estimating attributes of individual fruit, including dry matter content. The technique relies on statistic models (‘chemometrics’) to deduce information on sample attributes from spectra collected from the fruit. Barriers to the adoption of this technique for practical use in the fruit industry include the robustness of models across fruit from different growing conditions and spectra collected on different instruments. The proposed research is intended to reduce these barriers for the assessment of mango dry matter content by exploring new techniques for developing robust, global models across season, growing conditions, fruit variety, individual instruments and other variations. This would allow new instruments to be used ‘out of the box’ without the need for local calibration, hence greatly reducing the cost of uptake. Deep learning modelling techniques have been recently applied to spectroscopic applications, with claims of improved performance over the standard chemometric method, Partial Least Squares Regression, although these studies have typically involved relatively small datasets with limited testing on new populations of data. With access to an extended dataset of over 80,000 spectra from over 500 fruit populations, the aim of this proposed study is to validate previous publication claims that the use of a Convolutional Neural Network (CNN) model, a deep learning technique, is superior to existing methods in NIRS based prediction of mango dry matter content. The study also aims to optimise the operation and the architecture of the CNN model over that employed in previous publications, in context of the mango dry matter data set and use on a portable instrument.