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Spectra of walnut kernel for moisture content measurement.

The rapid and accurate detection of moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring moisture content in walnut kernel. In this paper, an analysis model for the moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to pre-process the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content and different pretreatment spectral data. The PLS, MLR, PCR and SVR were used to establish the relationship model between the spectral data and measurement values of moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength range from 1349 to 1490nm, SNV+1st preprocessing, and PLS modeling. Under these conditions, the square correlation coefficient(R2) and root mean square error of prediction(RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. In addition, in order to improve the the performance and applicability of the model, it was necessary to continuously expand the size of the sample set.