基于透射光谱技术的温州蜜柑含水率检测
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1.华中农业大学工学院;2.华中农业大学园艺林学学院

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湖北省技术创新专项“晚熟柑橘安全优质高效栽培技术研发与示范”(编号:2017ABA158)


Water content detection of Satsuma Orange based on transmission spectroscopy
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    摘要:

    柑橘的含水率是影响柑橘后续储存和加工的关键因素之一,为了检测柑橘的含水率,本文利用可见/近红外光谱透射技术对温州蜜柑进行含水率检测,采用了微分处理(Differential processing,SD)、多元散射校正(Multivariate Scattering Correction,MSC)、标准正态变换(standard normal variate,SNV)、SG卷积平滑以及标准化等预处理方法并比较,同时采用竞争性自适应重加权采样算法(Competitive adaptive reweighted sampling algorithm,CARS)提取特征波长,以此建立了基于柑橘含水率的偏最小二乘回归模型(Partial Least Squares regression,PLS)、BP神经网络模型和最小二乘支持向量机模型(Least squares support vector machine,LSSVM)。结果表明,使用经过SNV预处理后的光谱进行CARS筛选得到的359个波长建立的LSSVM模型预测效果最佳,校正集的相关系数和均方根误差分别为0.9375和0.0086,验证集相关系数和均方根误差分别为0.8316和0.0120,结果表明可见/近红外光谱技术用于温州蜜柑的含水率检测是可行的。

    Abstract:

    The moisture content of citrus is one of the key factors affecting the subsequent storage and processing of citrus. In order to detect the moisture content of citrus, the visible/near infrared transmission spectroscopy was used to detect the moisture content of satsuma orange. Differential processing, multivariate scattering correction, standard normal variate,SG convolution smoothing and MinMaxScaler are used and compared. At the same time, the Competitive adaptive reweighted sampling algorithm was used to extract the characteristic wavelengths, and then the partial least squares regression model(PLS), BP neural network model and least squares support vector machine (LSSVM) model based on citrus moisture content were established. The results show that the LSSVM model with 359 wavelengths obtained by CARS screening using the SNV preprocessed spectrum is the best predictor. The correlation coefficient and root mean square error of the correction set are 0.9375 and 0.0086, respectively. The square root errors are 0.8316 and 0.0120, respectively. The results show that the visible/near infrared spectroscopy technique is feasible to detect the water content of satsuma orange.

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  • 收稿日期:2020-07-05
  • 最后修改日期:2020-09-16
  • 录用日期:2020-09-17
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