Abstract

In this study, near-infrared hyperspectral imaging was applied to predict the water content of golden pothos (<i>Epipremnum aureum</i>) leaves, after which partial least squares regression (PLSR) analysis was performed to predict their averaged water content. The resulting predictive model was then applied to each single-pixel spectra in order to construct a water content image that could be used to evaluate the model's applicability to the single-pixel spectra through partial least squares score comparisons between the averaged spectra used for calibration and the single-pixel spectra. In the next phase, it was determined that a rebuilt PLSR predictive model based on the averaged spectra of an applicable pixel showed higher prediction accuracy than that of the original model. This study provides effective information about the limitations of prediction mapping and the optimization of pixel selections for better calibrations.

Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms

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Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy

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Using near infrared measurement of water content as a cue for detecting biological materials

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Visualization of quantitative lipid distribution in mouse liver through near-infrared hyperspectral imaging

Kyohei Okubo, Yuichi Kitagawa, Naoki Hosokawa, Masakazu Umezawa, Masao Kamimura, Tomonori Kamiya, Naoko Ohtani, and Kohei Soga
Biomed. Opt. Express 12(2) 823-835 (2021)

UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat

Hongchun Zhu, Haiying Liu, Yuexue Xu, and Yang Guijun
Appl. Opt. 57(27) 7722-7732 (2018)