Hyperspectral Imaging for Predicting Soluble Solid Content of Starfruit
DOI:
https://doi.org/10.11113/jt.v73.3480Keywords:
Hyperspectral Imaging, Partial Least Square Regression, StarfruitAbstract
Hyperspectral imaging technology is a powerful tool for non-destructive quality assessment of fruits. The objective of this research was to develop novel calibration model based on hyperspectral imaging to estimate soluble solid content (SSC) of starfruits. A hyperspectral imaging system, which consists of a near infrared camera, a spectrograph V10, a halogen lighting and a conveyor belt system, was used in this study to acquire hyperspectral images of the samples in visible and near infrared (500-1000 nm) regions. Partial least square (PLS) was used to build the model and to find the optimal wavelength. Two different masks were applied for obtaining the spectral data. The optimal wavelengths were evaluated using multi linear regression (MLR). The coefficient of determination (R2) for validation using the model with first mask (M1) and second mask (M2) were 0.82 and 0.80, respectively.
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