Abstract
Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content.
Highlights
Potato is a major root vegetable for human consumption and a known source of micronutrients including vitamins (C, niacin, B6, and thiamine) and minerals (Rama and Narasimham 2003)
This study aims to investigate the use of optical systems, data fusion at the feature level, and machine learning algorithms to evaluate the quality of potato tubers based on their glucose and sucrose content
The selected wavelengths for glucose or sucrose in potato tubers obtained for all sensors are shown in Fig. 3 for regression using interval partial least squares (IPLS) and Fig. 4 for classification using sequential forward selection (SFS)
Summary
Potato is a major root vegetable for human consumption and a known source of micronutrients including vitamins (C, niacin, B6, and thiamine) and minerals (potassium, phosphorus, magnesium, and iron) (Rama and Narasimham 2003). Frozen potato and chip consumptions together increased from 20.82 to 31.53 kg per capita during the same period (NPC 2019). The value of US exports of chips and frozen French fries increased from $610 million in 2006 to more than $1083 million in 2018 (Bohl and Johnson 2010; NPC 2019). Among several quality attributes that attract consumers to fried potato products, color is the most important. A reaction takes place between the reducing sugars, mainly glucose and fructose in potatoes, and the amino acid, asparagine, at relatively high temperatures (around 180 °C). This phenomenon is known as the Maillard reaction
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