Rapid Quality Evaluation of Camellia Oleifera Seed Kernel Using a Developed Portable NIR With Optimal Wavelength Selection

Moisture content is one of the factors measured to evaluate the quality of Camellia oleifera seeds. High quality C. oleifera seeds used for trading must have a low moisture content, specifically not more than 15% on a dry basis (db). Moisture content analysis requires a prolonged laboratory investigation so that the development of fast and effective determination methods is helpful. The objective of this paper was to develop a low-cost portable NIR reflectance spectrometer collaborating with an android application for the rapid prediction of the moisture content in C. oleifera seeds. To calibrate the prediction model, an effective chemometric algorithm, based on partial least squares regression was established, and models based on wavelength selection algorithms such as backward interval partial least squares (biPLS) and partial least squares coupled with variable importance projection (VIP-PLS) were implemented as an improved version of PLS. Both algorithms (biPLS and VIP-PLS) improved the predictive performance and accuracy of the model. The experimental results showed that the biPLS model with the 1st derivative transformation provided the best prediction for measuring the moisture content of C. oleifera seeds with a coefficient of determination (R-2) value of 0.927, standard error of prediction (SEP) of 0.848%db, bias of -0.067%db, function slope of 1.005, and ratio of performance deviation (RPD) of 3.696. Finally, the device was tested according to the ISO 12099:2017(E) standard and confirmed the reliability of the device for in-field use.