莴苣是全球重要的经济作物,因其营养成分和令人愉悦的味道而受到消费者的重视。然而,关于国内生菜生长期生长指标变化的研究有限。质量评估主要依靠主观评价,导致显著的变异性。这项研究的重点是玫瑰花期的水培生菜,并研究了指标和光谱曲线随时间的变化模式。通过光谱预处理和选择特征波长,开发了三个模型来预测指标。结果表明,最佳模型结构为S_G-UVE-PLSR(SSC和维生素C)和Nor-CARS-PLSR(水分含量)。PLSR模型的预测集相关系数分别为0.8648、0.8578和0.8047,残差预测偏差分别为1.9685、1.9568和1.6689。最佳模型被集成到便携式设备中,使用Matlab2021a编写的实时分析软件,用于预测玫瑰花期生菜的理化指标。结果表明,样本大小为180时,预测集相关系数为0.8215、0.8472和0.7671,预测均方根误差为0.5348、1.5813和2.3347。预测值和实际值之间的微小差异表明所开发的设备可以满足实时检测的要求。
Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.