多毛刺小样本高光谱数据下鹰嘴蜜桃含水率的预估  

Prediction of moisture content of hummus peach based on multi-burr hyperspectral data

作　　者：高艾迪 乔奉璋 朱文轩 钟小品[1] 邓元龙[1,2] GAO Aidi;QIAO Fengzhang;ZHU Wenxuan;ZHONG Xiaopin;DENG Yuanlong(Shenzhen University,College of Mechatronics&Control Engineering,Shenzhen,Guangdong 510086,China;Shenzhen Institute of Technology,Shenzhen,Guangdong 518116,China)

机构地区：[1]深圳大学机电与控制工程学院,广东深圳510086 [2]深圳技师学院,广东深圳518116

出　　处：《食品与机械》2023年第10期123-129,共7页Food and Machinery

基　　金：国家自然科学基金面上项目(编号:62171288);广东省乡村振兴战略专项资金(农村特派员)(编号:163-2019-XMZC-0009-03-0059)。

摘　　要：目的:提出并解决鹰嘴蜜桃高光谱测量数据多毛刺和小样本问题。方法:基于高光谱成像技术,使用图像处理方法识别高光谱图像中鹰嘴蜜桃所在区域,计算该区域内的光谱图像从而得到平均光谱反射率数据,形成高光谱曲线图像。对于存在抖动和毛刺的高光谱图像数据,比较多项式平滑算法(SG)、多元散射矫正算法(MSC)、标准正态变量算法(SNV)、一阶导数算子(D1)、二阶导数算子(D2)等数据预处理方法对模型预测精度的影响;针对数据维度高且样本量少的特点,使用主成分分析算法(PCA)对数据进行降维,再对降维后的数据应用马氏距离测度方法(MD)进行异常值剔除;最终利用Kennard-Stone算法(KS)划分出训练集和测试集,并选取小样本场景下表现较好的偏最小二乘回归(PLSR)模型对鹰嘴蜜桃的含水率进行估计和分析。结果:SG-PCA-MD-KS-PLSR模型在高光谱曲线存在抖动和毛刺情况时对鹰嘴蜜桃含水率估计的效果最好,训练集下决定系数(R^(2))达到0.928,均方根误差(RMSE)为0.0084,测试集下R^(2)达到0.926,RMSE为0.0092。在进一步对鹰嘴蜜桃以含水率为指标进行分级试验时,该模型的预测结果可以较好地对鹰嘴蜜桃含水状况进行分级,训练集下分级正确率为0.956,测试集下分级正确率为0.923。结论:利用高光谱成像技术建立SG-PCA-MD-KS-PLSR模型,在高光谱样本数较小且存在毛刺的情况下,仍能对鹰嘴蜜桃含水率进行无损估计。Objective:To propose a new solution to overcome the two challenges of data with spikes and small sample sizes in nectarine hyperspectral measurement.Methods:Based on hyperspectral imaging technology,image processing methods were used to identify the area of nectarines in the hyperspectral image,and the spectral reflectance data of the area was calculated to form a hyperspectral curve image.For hyperspectral image data with spikes and noise,compared the effects of several data preprocessing methods,including polynomial smoothing algorithm(SG),multivariate scatter correction algorithm(MSC),standard normal variate algorithm(SNV),first-order derivative operator(D1),and second-order derivative operator(D2)on model prediction accuracy.To address the high-dimensional and small sample size characteristics of the data,the principal component analysis algorithm(PCA)was used for dimensionality reduction,followed by outlier removal using the Mahalanobis distance measure method(MD).Finally,the Kennard-Stone algorithm(KS)was used to divide the data into training and testing sets,and the partial least squares regression(PLSR)model,which performed well in the small sample scenario,was selected for estimation and analysis of nectarine water content.Results:The SG-PCA-MD-KS-PLSR model performed best for estimating nectarine water content when there were spikes and noise in the hyperspectral curve.The coefficient of determination(R^(2))was 0.928,and the root mean square error(RMSE)was 0.0084 on the training set.The R^(2)was 0.926,and the RMSE was 0.0092 on the testing set.In further experiments grading nectarines based on their water content,the model's predictions showed good performance.The accuracy rate of grading was 0.956 for the training set and 0.923 for the testing set.Conclusion:By using hyperspectral imaging technology and establishing the SG-PCA-MD-KS-PLSR model,non-destructive estimation of nectarine water content and grading of nectarine water content can be achieved in scenarios with small hyperspectral sample sizes a

关 键 词：高光谱 鹰嘴蜜桃 含水率 无损检测 

分 类 号：O657.3[理学—分析化学] TS255.7[理学—化学]