Abstract
The rapid and accurate detection of the 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 the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess 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. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. 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. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.
Highlights
Journal of Spectroscopy complication, it is important to find a rapid, nondestructive, and “online” method, which could provide significant information
A certain amount of walnut kernels were placed in a closed container with water at the bottom, and the container was stored in a constant temperature incubator at 20°C to make the water absorbed evenly
Each sample was measured twice, and the average value was taken as the reference value as given in Table 1. e coverage range of the moisture content was 1.20–9.92%, with the average value of 5.55% and the standard deviation of 0.27. e error margin of the drying method used for the sample was less than 8%
Summary
In order to make the walnut kernel samples more representative, the method of moisture absorption in a closed container was adopted. A total of 136 walnut kernel samples with different moisture contents were prepared in this way. According to the Chinese national standard GB/T 14489.1–2008, the moisture content in walnut kernel was measured by the drying method. E moisture content of each sample was measured immediately after the spectral data were collected. E coverage range of the moisture content was 1.20–9.92%, with the average value of 5.55% and the standard deviation of 0.27. In model construction for the moisture content in walnut kernel, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS), and support vector regression (SVR) have been applied to develop the corresponding models between spectral data and measurement values [34]. All modeling computations were implemented using the Unscrambler X10.4 and Matlab v2007a
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