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Researches of Fruit Quality Prediction Model Based on Near Infrared Spectrum

机译：基于近红外光谱的水果品质预测模型的研究

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With the improvement in standards for food quality and safety, people pay more attention to the internal quality of fruits, therefore the measurement of fruit internal quality is increasingly imperative. In general, nondestructive soluble solid content (SSC) and total acid content (TAC) analysis of fruits is vital and effective for quality measurement in global fresh produce markets, so in this paper, we aim at establishing a novel fruit internal quality prediction model based on SSC and TAC for Near Infrared Spectrum. Firstly, the model of fruit quality prediction based on PCA + BP neural network, PCA + GRNN network, PCA + BP adaboost strong classifier, PCA + ELM and PCA + LS_SVM classifier are designed and implemented respectively; then, in the NSCT domain, the median filter and the Savitzky-Golay filter are used to preprocess the spectral signal, Kennard-Stone algorithm is used to automatically select the training samples and test samples; thirdly, we achieve the optimal models by comparing 15 kinds of prediction model based on the theory of multi-classifier competition mechanism, specifically, the non-parametric estimation is introduced to measure the effectiveness of proposed model, the reliability and variance of nonparametric estimation evaluation of each prediction model to evaluate the prediction result, while the estimated value and confidence interval regard as a reference, the experimental results demonstrate that this model can better achieve the optimal evaluation of the internal quality of fruit; finally, we employ cat swarm optimization to optimize two optimal models above obtained from non-parametric estimation, empirical testing indicates that the proposed method can provide more accurate and effective results than other forecasting methods.
机译：随着食品质量和安全标准的提高，人们对水果的内部质量越来越重视，因此对水果内部质量的测量越来越重要。总体而言，水果的非破坏性可溶性固形物含量（SSC）和总酸含量（TAC）分析对于全球新鲜农产品市场的质量测量至关重要且有效，因此，本文旨在基于该模型建立一种新颖的水果内部质量预测模型在SSC和TAC上用于近红外光谱。首先，分别设计并实现了基于PCA + BP神经网络，PCA + GRNN网络，PCA + BP adaboost强分类器，PCA + ELM和PCA + LS_SVM分类器的水果品质预测模型;然后，在NSCT域中，使用中值滤波器和Savitzky-Golay滤波器对光谱信号进行预处理，使用Kennard-Stone算法自动选择训练样本和测试样本。第三，基于多分类器竞争机制理论，通过比较15种预测模型，获得最优模型，具体地，引入非参数估计来衡量所提模型的有效性，非参数估计评估的信度和方差。对每个预测模型进行评估，以估计值和置信区间为参考，实验结果表明，该模型可以较好地实现对果实内部质量的最佳评价。最后，我们采用猫群优化算法优化了以上两个从非参数估计获得的最优模型，经验检验表明，与其他预测方法相比，该方法可以提供更准确，更有效的结果。

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