基于全透射可见-近红外光谱的西瓜糖度在线检测研究  

Online Detection of Sugar Content in Watermelon Based on Full-Transmission Visible and Near-Infrared Spectroscopy

作　　者：王贺功 黄文倩[2] 蔡仲磊 严忠伟 黎胜 李江波[2] WANG He-gong;HUANG Wen-qian;CAI Zhong-lei;YAN Zhong-wei;LI Sheng;LI Jiang-bo(College of Mechanical and Electrical Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Intelligent Equipment Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)

机构地区：[1]新疆农业大学机电工程学院,新疆乌鲁木齐830052 [2]北京市农林科学院智能装备技术研究中心,北京100097

出　　处：《光谱学与光谱分析》2024年第6期1710-1717,共8页Spectroscopy and Spectral Analysis

基　　金：国家重点研发计划子课题(2022YFD2002202-01);国家自然科学基金项目(31972152)资助。

摘　　要：糖度是评价西瓜品质的重要指标之一,影响着西瓜的受欢迎程度和销售价格。但体积大、皮厚的自然生物特征为西瓜糖度的快速、无损评估带来了挑战。为探索西瓜糖度快速无损检测,选择230个西瓜作为试验样本,使用自行研制的全透射可见-近红外光谱系统在线采集西瓜光谱数据,在线检测过程中保持光谱连续检测点在西瓜的赤道部位,并分别测量西瓜整果糖度和中心糖度作为理化糖度参考值。首先对样本在线检测过程中产生的多条光谱数据进行均值化处理,并选取波长范围690~1100 nm的光谱数据,使用蒙特卡洛方法剔除其中的异常样本,使用多种预处理方法(SNV、SG平滑等)对光谱数据优化,并采用SPXY算法划分样本校正集和预测集以减小因西瓜内部糖度分布差异造成的影响;基于优化的光谱数据构建了线性PLSR模型和非线性LS-SVM模型预测西瓜中心糖度和整果糖度。结果表明,基于SNV和SG组合法预处理光谱构建的LS-SVM模型预测西瓜整果糖度效果最好,其校正集相关系数R C=0.92,建模均方根误差RMSEC=0.37°Brix;预测集相关系数R P=0.88,预测均方根误差RMSEP=0.40°Brix。进一步使用特征波段挑选算法(CARS、UVE、SPA等)对光谱数据优化,结果显示构建的模型效果更好,其中使用CARS和UVE组合算法选取的特征波长构建LS-SVM模型预测西瓜整果糖度时效果最好,其校正集相关系数R C=0.94,校正均方根误差RMSEC=0.31°Brix;预测集相关系数R P=0.91,预测均方根误差RMSEP=0.37°Brix,且建模变量从1524个特征变量缩减到39个特征变量。该研究为实际生产中西瓜糖度快速无损检测应用提供了参考。Sugar content is a crucial parameter for assessing watermelon quality,influencing watermelon's marketability and commercial value.However,the natural biological characteristics of large volume and thick skin pose challenges for rapid and non-destructive evaluation of the sugar content of watermelon.In this study,230 watermelons were selected for investigation.A custom-designed full-transmission visible-near-infrared detection system was developed.Spectral data of all samples were acquired online.Each sample spectral data comes from the equatorial part of the watermelon.The overall watermelon sugar content and the central sugar content were measured separately to provide reference values for the assessment of sugar content.In the data processing phase,the spectral data of each sample was averaged,and spectral data in the 690~1100 nm was selected.The Monte Carlo method was implemented to remove abnormal samples,and preprocessing,such as Standard Normal Variate correction and Savitzky-Golay smoothing,was applied to optimize the spectral data.The SPXY algorithm was used to divide the calibration and prediction sets.Utilizing the optimized spectral data,linear Partial Least Squares Regression(PLSR)and non-linear Least Squares Support Vector Machine(LS-SVM)models were developed to forecast each sample's center sugar content and overall sugar content.The results revealed that,Combined with standard normal variate correction and Savitzky-golay smoothing,the LS-SVM model yielded the most favorable results in predicting the overall watermelon sugar content.The calibration correlation coefficient(R C)of 0.92 and root mean square error of calibration(RMSEC)of 0.37°Brix were obtained for the calibration set.Correspondingly,the prediction correlation coefficient(R P)of 0.88 and root mean square error of prediction(RMSEP)of 0.40°Brix were obtained for the prediction set.Furthermore,feature wavelength selection algorithms(e.g.,Competitive Adaptive Reweighted Sampling,Uninformative Variable Elimination,Successive Projections Al

关 键 词：全透射光谱 在线检测 西瓜 糖度 模型构建 

分 类 号：S24[农业科学—农业电气化与自动化]