Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy

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.

Nutritional Comparison of Sacha Inchi (Plukenetia volubilis) Residue with Edible Seeds and Nuts in Taiwan: A Chromatographic and Spectroscopic Study

“…Standard normal variate transformation (SNV) pretreated spectra were not significantly different from smoothing spectra ( Figure 2 ). SNV was performed to reduce baseline drift and scattering spectra [ 22 ]. The spectral pattern of each seed and nut sample appeared the same.…”

“…The following process is to correlate the spectra with the results of laboratory measurements to obtain NIR predictions. The models commonly used are partial least squares (PLS) and principal component regression (PCR) [ 18 , 21 , 22 ]. The application of NIR for seeds and nuts has been carried out by several researchers, using walnut kernels [ 22 ], pine (Pinus koraiensis) seeds [ 23 ], cotton seeds [ 19 ], and almonds [ 24 ].…”

“…The models commonly used are partial least squares (PLS) and principal component regression (PCR) [ 18 , 21 , 22 ]. The application of NIR for seeds and nuts has been carried out by several researchers, using walnut kernels [ 22 ], pine (Pinus koraiensis) seeds [ 23 ], cotton seeds [ 19 ], and almonds [ 24 ]. The NIR method can be applied for drying, peeling, and processing peanuts factories [ 20 ].…”

Quantitative Analysis of Alkane Gases by Infrared Absorption Spectrometry Based on Spectral Comprehensive Pretreatment Method

“…Zhou et al proposed a wavelet transform-based spectral pretreatment method, which can effectively remove spectral noise [8]. Peng et al investigated the standard normal variate transformation and first derivative (PNV-FD) pretreatment methods and proposed the idea of segmental pretreatment [9]. Meng et al analyzed the spectral pretreatment effect of second-order derivative, standard normal transform and multivariate scattering correction pretreatment algorithm [10].…”

Non-Destructive Detection of Moldy Walnuts Based on Hyperspectral Imaging Technology

“…Some researchers have used mapping combined with stoichiometry to measure the water, fat, protein, and other contents as well as antioxidant characteristics in walnuts. However, the near-infrared spectral resolution is high, but its point scanning method can only provide an average spectrum of the object to be detected, and it is unable to reflect the spatial distribution state of the analyte [ 16 , 17 , 18 , 19 ].…”