Study of modeling optimization for hyperspectral imaging quantitative determination of naringin content in pomelo peel

Abstract With the development of modern computational science and data metrology, hyperspectral imaging technology has been utilized in the field of remote sensing for the application in precision agriculture and crop quality testing. In this paper, the near infrared hyperspectral imaging (NIRHSI) technique was applied to quantitatively determine the naringin content in pomelo peel samples. Model optimization was studied by investigating the influence of system measuring conditions and parameters of the built-up HSI instrumental system. The HSI data acquisition was built up using a prevalent pushbroom scanner in the near infrared region. Calibration models were established using the partial least squares (PLS) regression in the mode of cross validation, in combined use of the Savitzky-Golay smoother (SGS) for data pretreatment. These multivariate analytical models were optimized in comparison about the region of interest (ROI) for NIRHSI model optimization. In the process of NIRHSI data acquisition, the rational values of some system measurement parameters were also tuned and tested, such as the use of different watts for light intensity, different lenses and different materials as the scanning backgrounds. Results showed that the cross-validation PLS regression methods performed well in the calibration and prediction processes, working well together with the parameter tuning of the SGS pretreatment. In addition to the fact that different materials as the scanning backgrounds obviously affected the quantitative result, there is no apparent difference in the comparing cases of different light intensities and different lenses. This work validates the capability of applying NIRHSI technique to quantitative determine the content of naringin in pomelo peel samples. The test for model optimization by comparing the measurement parameters and the system properties has prospective application ability in fields of other spectral/hyperspectral data analysis. It is an important lab simulation of remote sensing. It contributes significant theoretical reference to the design of the large-scale online hyperspectral data acquisition systems.

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