基于可见光/近红外光谱对莲座期生菜理化指标的无损检测

Foods Pub Date : 2024-06-13 DOI:10.3390/foods13121863
Wei Li, Qiaohua Wang, Yingli Wang
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引用次数: 0

摘要

生菜是全球重要的经济作物,因其营养丰富、味道鲜美而受到消费者的青睐。然而,国内对莴苣生长期生长指标变化的研究十分有限。质量评估主要依靠主观评价,因此存在很大差异。本研究以水培莴苣莲座期为研究对象,调查了莴苣生长指标和光谱曲线随时间的变化规律。通过采用光谱预处理和选择特征波长,建立了三个模型来预测指标。结果表明,最佳模型结构是 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,剩余预测偏差分别为 1.9685、1.9568 和 1.6689。使用 Matlab2021a 编写的实时分析软件将最优模型集成到便携式设备中,用于预测莲座期生菜的理化指标。结果表明,在 180 个样本量下,预测集相关系数分别为 0.8215、0.8472 和 0.7671,预测均方根误差分别为 0.5348、1.5813 和 2.3347。预测值和实际值之间的差异很小,这表明所开发的设备能够满足实时检测的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy
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.
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