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Abstract：
The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.
摘要：
全球对蛋白质的需求呈上升趋势，花生蛋白粉已经成为一个重要的参与者，由于其可负担性和高质量，具有巨大的未来市场潜力。然而,该行业目前缺乏快速质量检测的有效方法。本文通过引入具有近红外光谱（NIR）的便携式设备来快速评估花生蛋白粉的质量，从而解决了这一差距。主成分分析(PCA)，偏最小二乘(PLS)，采用广义回归神经网络(GRNN)方法构建模型,进一步提高了装置的精度和效率。结果表明，新建立的NIR方法与PLS和GRNN分析同时预测脂肪，蛋白质，花生蛋白粉的水分。GRNN模型显示出比PLS模型更好的预测性能，脂肪的校准相关系数(Rcal)，蛋白质,花生蛋白粉的水分分别为0.995、0.990和0.990,残差预测偏差(RPD)分别为10.82、10.03和8.41。研究结果揭示了便携式近红外光谱设备结合GRNN方法实现了花生蛋白粉的快速定量分析。这一进步为该设备在行业中的重要应用，潜在的革命性的质量测试程序，并确保高质量产品的一致交付，以满足消费者的需求。
