# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
###### Abstract  
Potato (_Solanum tubersum_ 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 (R\({}^{2}\) = 0.53, RPD = 1.46, root mean square error (RMSE) = 5.04%) or the \(\beta\)-coefficient wavelength selection technique (R\({}^{2}\) = 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. moisture content; potato; hyperspectral imaging; infield assessment; post-harvest handling 20231353. 202231353. 202231353. 202231353. 20231353.  
potato processing, the preprocessing moisture content is one of the most critical parameters. Moisture content is an important factor in potatoes because it influences the quality of the product during the drying method in potato processing, and it is also associated with the rate of shrinkage and color of the potato [4; 5]. Excessive moisture content can also lead to product spoilage as it aids the growth and activities of microorganisms [6]. Moreover, the freshness of freshly cut potato slices is related to their moisture content. Hence, knowledge about the moisture content of potatoes can assist in effectively preserving product quality and preventing spoilage. Moisture content is traditionally determined using standard drying methods involving human inspection, oven-drying, and freeze-drying. However, these methods are not highly accurate, time- and energy-consuming, interruptive, and destructive [7]. Recent advancements in technology have made it possible to develop methods to determine moisture content utilizing imaging techniques. One of the rapid, convenient, and nondestructive approaches to measure moisture content is by using near-infrared spectroscopy. Despite this advancement in technology, these measurements are only taken from a single point, these techniques are unable to predict the moisture distribution [8], and cannot give information on the spatial distribution of moisture in the samples [9; 10]. RGB and near-infrared spectroscopy (NIR) sensors have shown to be effective in a variety of agriculture and its related applications. However, it is found that only hyperspectral sensors have the spectral resolution that surpasses the use of multispectral imaging [11; 12; 13]. Several studies [14; 15; 16] have indicated that hyperspectral imaging (HSI) can be efficiently used in a variety of agriculture and related applications. [PERSON] et al. [15] compared the effectiveness of HSI and NIRS in determining nitrogen and carbon concentrations in wheat.