为了实现对榛子蛋白质的快速无损检测，提出了一种结合近红外光谱技术和间隔随机跳蛙算法的榛子蛋白质检测模型。提取榛子的近红外光谱数据后，对榛子光谱数据进行一阶导和标准正态变量变换预处理。针对随机跳蛙算法的初始子集以及最终波段数量阈值不确定的问题，采用间隔随机跳蛙算法进行特征波段提取，并对比了该算法与竞争性自适应重加权采样算法、连续投影算法和原始随机跳蛙算法的提取结果。基于提取的特征波段建立偏最小二乘回归模型。实验结果表明，相比其他算法，间隔随机跳蛙算法的性能最好且建立的模型稳定性更高。间隔随机跳蛙算法对交叉验证集的回归系数和均方根误差分别为0.9082和0.0178，对测试集的回归系数和均方根误差分别为0.8999和0.0372。

To achieve the quick nondestructive detection of hazelnut protein, a near infrared spectroscopy and interval random frog algorithm-based hazelnut protein detection model is proposed in this paper. After extracting the near infrared spectral data of hazelnut, first-order derivative and standard normal variable transformation preprocessing on the hazelnut spectral data is performed. Considering the uncertainty of the initial subset of the random frog algorithm and the final band number threshold's uncertainty, an interval random frog algorithm is utilized to extract the characteristic band, competitive adaptive reweighted sampling algorithm, and successive projections algorithm, and the original random frog algorithm are compared. Furthermore, a partial least squares regression model is developed based on the extracted feature bands. The experimental findings depict that the interval random frog algorithm had the best performance and the developed model is more stable when compared with other algorithms. The regression coefficient and root mean square error of the interval random frog algorithm for the cross-validation set are 0.9082 and 0.0178, respectively, and the regression coefficient and root mean square error for the test set are 0.8999 and 0.0372, respectively.