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.

中文翻译：

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近红外漫反射光谱法无损检测核桃仁水分

水分含量的快速准确检测对核桃仁的品质评价和榨油过程具有重要意义。近红外 (NIR) 光谱是测量核桃仁水分含量的理想方法。在本研究中，基于 NIR 漫反射光谱，使用化学计量学方法开发了核桃仁水分含量的回归模型。采用不同的光谱预处理方法对原始光谱数据进行预处理。整个光谱带分为5个子带、10个子带、15个子带和20个子带，以筛选与核桃仁水分含量相关的特定波长。PLS（偏最小二乘回归）、MLR（多元线性回归）、PCR（主成分回归）、并利用SVR（支持向量回归）建立光谱数据与水分含量测量值之间的关系模型。相比之下，优化的建模条件确定如下：检测波长1349-1490 nm，SNV-FD（标准正态变量变换和一阶导数）预处理方法和PLS算法。在这些条件下，平方相关系数 (R ² ) 和预测模型的预测均方根误差 (RMSEP) 分别为 0.9865 和 0.0017。本研究结果为核桃仁水分含量的快速检测提供了一种可行的方法。为了提高模型的性能和适用性，需要不断扩大样本集的规模。