A kinetic model based on visible/near-infrared spectroscopy of the peel brittleness of “Xintian-125” Cucumis melons, the research object, stored under room temperature, was established in order to realize real-time monitoring of the peel brittleness of Cucumis melons and for prediction of storage time. The NIR and peel brittleness of melons stored for 1, 4, and 7 days were collected and measured. SG was confirmed to be the best pretreatment by comparing the PLS models established with 4 pretreatment methods, and the differences of the prediction set determination coefficient and root-mean-square were 0.818 and 23.755, respectively. CARS and SPA were adopted to extract the feature wavelengths and establish the peel brittleness of PLS prediction model. The model’s prediction accuracy was 0.919, and the prediction root-mean-square was 25.413, indicating that NIR is able to realize the prediction of the peel brittleness of Cucumis melons. As a result, a NIR-based peel brittleness kinetic model was created. The value of the regression model was less than 0.001, and the model’s correlation coefficient was 0.8503, showing that the model is of extreme significance and high precision. The zero-order reaction equation was overfitted according to the variation tendency of the average peel brittleness of stored melons. The model’s correlation coefficient was 0.981, the standard error was 4.624, and the linear relation between the stored period and NIR was established based on it. The research proves that the NIR-based technology is able to realize quick and loss-free inspection of melons’ peel brittleness and prediction of the stored period.

中文翻译：

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基于可见/近红外光谱的黄瓜贮藏瓜皮脆性动力学模型

建立了基于可见/近红外光谱的“新天125”黄瓜瓜皮脆性的动力学模型，该研究对象在室温下保存，以实现对黄瓜瓜皮脆性的实时监测。并用于预测存储时间。收集并测量储存1天，4天和7天的瓜的NIR和果皮脆性。通过比较四种预处理方法建立的PLS模型，可以确定SG是最佳的预处理方法，预测集确定系数和均方根的差分别为0.818和23.755。采用CARS和SPA提取特征波长，建立PLS预测模型的皮脆性。该模型的预测准确性为0.919，预测均方根为25.413，表明NIR能够实现对黄瓜瓜皮脆性的预测。结果，创建了基于NIR的剥离脆性动力学模型。的回归模型的数值小于0.001，模型的相关系数为0.8503，说明该模型具有极高的意义和较高的精度。根据存储瓜的平均果皮脆性的变化趋势，过零拟合反应方程式被过度拟合。该模型的相关系数为0.981，标准误为4.624，并据此建立了存储周期与NIR的线性关系。研究证明，基于近红外光谱的技术能够快速，无损地检测瓜皮的脆性并预测储藏期。