Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging

Abstract

Potato (Solanum tuberosum L.) is one of the most significant vegetable crops grown globally, especially in developing countries. Over the last few years, global potato production has been increasing. This growth has created many opportunities for developing a wide range of value-added products from these crops. However, this requires monitoring the quality components of the tubers, such as moisture content, starch content, and soluble solid content. In particular, moisture content is one of the key quality parameters important for ensuring quality control throughout the supply chain and processing for consumer consumption. Ideally, moisture content would be estimated at the field level; however, current methods used by the industry to assess moisture content are time-consuming, labor-intensive, and destructive. Hence, the purpose of this study is to investigate the feasibility of hyperspectral imaging to quantify the moisture content of unpeeled potatoes before they were subsequently stored and processed. Hyperspectral images are collected from 47 intact potato tubers, with partial least squares regression (PLSR) models developed to predict moisture content from these spectra. The models showed predictive abilities for moisture content with acceptable ratios of prediction to deviation (RPDs) when considering the complete wavelength range (R2 = 0.53, RPD = 1.46, root mean square error (RMSE) = 5.04%) or the β-coefficient wavelength selection technique (R2 = 0.53, RPD = 1.47, RMSE = 5.02%). Furthermore, the prediction ability increased by more than 10% when the model wavelength was narrowed down to 733–970 nm. This study demonstrates the potential of using hyperspectral imaging for the quality assessment of intact, unpeeled potatoes, although further work is required to improve the model quality and implement this approach using remote sensing imagery.