Evaluation of Sugar Content of Potatoes using Hyperspectral Imaging

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

“…By calculating the mean value of each scattering profile, a 2-D hyperspectral image can be reduced to a 1-D mean spectrum, which then enables the implementation of commonly used methods in NIR spectral analysis for wavelength selection. For mean spectra extracted from hyperspectral scattering images, uninformative variable elimination (UVA), affinity propagation and interval partial least squares have been used for wavelength selection for quality prediction [61][62][63][64].…”

“…The wavelength of 677 nm, corresponding to chlorophyll absorption, was the most correlated with firmness with r = 0.69 and the best predictions with r 2 = 0.58-0.77 were obtained by cascading the two function parameters at a combination of several wavelengths. Rady et al [63] used hyperspectral scattering imaging to predict glucose and sucrose contents of potatoes. They explored three types of datasets including the mean spectra, scattering profile features and their combination, and a number of calibration techniques including PLS and NNs, for model calibration.…”

Postharvest monitoring of organic potato (cv. Anuschka) during hot‐air drying using visible–NIR hyperspectral imaging

“…With a production capacity of 368 096 Mt (2013), potatoes are amongst the most important staple foods worldwide, and their use in processed form has increased significantly over the last three decades, thus increasing the need for monitoring of quality throughout the involved processes …”

Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat

“…In recent years, hyperspectral imaging (HSI) as a rapid and nondestructive analytical technique has gained importance in the evaluation of food quality (Rady, Guyer, & Lu, 2015;Zhu, Zhang, Shao, He, & Ngadi, 2014;Kamruzzaman, Makino, Oshita, & Liu, 2015;Liu & Ngadi, 2012;Pu, Sun, Ma, Liu, & Cheng, 2014;Yu, Zhao, Liu, Li, Liu, & He, 2014). The fact that HSI integrates conventional imaging or computer vision Jackman, Sun, Du, & Allen, 2009;Wang, & Sun, 2002; and spectroscopic techniques into one system enables this technique to provide the spatial and spectral information of the sample simultaneously, which is powerful for food quality discrimination ; ElMasry, Feng & Sun, 2012Wu, Sun, & He, 2012).…”