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首页> 外文会议>Annual International Meeting of the American Society of Agricultural and Biological Engineers >Feature Wavebands Selection Using Immune Genetic Algorithm for Prediction of Soluble Solids Content inCitrus Fruit
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Feature Wavebands Selection Using Immune Genetic Algorithm for Prediction of Soluble Solids Content inCitrus Fruit

机译：采用免疫遗传算法的特征波带选择可溶性固体含量incitrus果实的预测

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摘要

FT-NIR spectroscopy is a promising technique for rapid and noninvasive measurement of multiple quality attributes of citrus fruit. But conventional methods (such as multi-linear regression or MLR) for feature wavebands selection are time-consuming andmay not be optimal. An immune genetic algorithm (IGA) approach was proposed to select the feature wavebands for the FT-NIR spectroscopy data of 'Gong Chuan' citrus as a precursor to the development of calibration models for predicting fruit soluble solids content (SSC). All wavebands were average divided into 25 intervals and 11 optimal intervals including 662 wavebands were achieved using IGA. Partial least squares (PLS) regression and cross-validation methods were used to develop models predicting SSC coupled with 662 feature wavebands. Compared with standard genetic algorithm (SGA), IGA yielded the optimal regions with faster convergence and preventing premature. The prediction results of IGA model (R_P= 0.915 and RMSEP = 0.71 %) was better than full wavebands model (R_P = 0.886 and RMSEP = 0.81 %). IGA approach would provide an effective means for selecting feature wavebands to predict SSC in citrus fruits, it is possible to optimize data selection, improve the precision of prediction and reducethe number of variables of calibration.
机译：FT-NIR光谱是一种很有希望的柑橘类水果的多种品质属性的快速和非侵入性的技术。但是特征波段选择的传统方法（例如多线性回归或MLR）是耗时的，也不是最佳的。提出了一种免疫遗传算法（IGA）方法，用于选择“龚川”柑橘FT-NIR光谱数据的特征波段作为预测果实可溶性固体含量（SSC）的校准模型的前兆。所有波段平均分为25个间隔，使用IGA实现了11个最佳间隔，包括662波带。局部最小二乘（PLS）回归和交叉验证方法用于开发预测SSC的模型，其与662个特征波段耦合。与标准遗传算法（SGA）相比，IgA产生了更快的收敛性和预防过早的最佳区域。 IGA模型的预测结果（R_P = 0.915和RMSEP = 0.71％）优于全波带模型（R_P = 0.886和RMSEP = 0.81％）。 IGA方法将提供一种用于选择特征波带的有效手段，以预测柑橘类水果中的SSC，可以优化数据选择，提高预测的精度和校准的变量数量。

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