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      2. 基于透射光譜技術的溫州蜜柑含水率檢測
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        1.華中農業大學工學院;2.華中農業大學園藝林學學院

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        湖北省技術創新專項“晚熟柑橘安全優質高效栽培技術研發與示范”(編號:2017ABA158)


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

          柑橘的含水率是影響柑橘后續儲存和加工的關鍵因素之一,為了檢測柑橘的含水率,本文利用可見/近紅外光譜透射技術對溫州蜜柑進行含水率檢測,采用了微分處理(Differential processing,SD)、多元散射校正(Multivariate Scattering Correction,MSC)、標準正態變換(standard normal variate,SNV)、SG卷積平滑以及標準化等預處理方法并比較,同時采用競爭性自適應重加權采樣算法(Competitive adaptive reweighted sampling algorithm,CARS)提取特征波長,以此建立了基于柑橘含水率的偏最小二乘回歸模型(Partial Least Squares regression,PLS)、BP神經網絡模型和最小二乘支持向量機模型(Least squares support vector machine,LSSVM)。結果表明,使用經過SNV預處理后的光譜進行CARS篩選得到的359個波長建立的LSSVM模型預測效果最佳,校正集的相關系數和均方根誤差分別為0.9375和0.0086,驗證集相關系數和均方根誤差分別為0.8316和0.0120,結果表明可見/近紅外光譜技術用于溫州蜜柑的含水率檢測是可行的。

          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
        • 錄用日期:2020-09-17
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