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Nondestructive detection for egg freshness grade based on hyperspectral imaging technology
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-04-25 , DOI: 10.1111/jfpe.13422 Kunshan Yao 1 , Jun Sun 1 , Xin Zhou 1 , Adria Nirere 1 , Yan Tian 1 , Xiaohong Wu 1
Journal of Food Process Engineering ( IF 2.7 ) Pub Date : 2020-04-25 , DOI: 10.1111/jfpe.13422 Kunshan Yao 1 , Jun Sun 1 , Xin Zhou 1 , Adria Nirere 1 , Yan Tian 1 , Xiaohong Wu 1
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In order to identify the freshness grade of eggs nondestructively and rapidly, hyperspectral imaging technology was used in this article. The hyperspectral data of 200 samples of three freshness grades was acquired by using hyperspectral image acquisition system (400.68–1,001.612 nm), and then the freshness grade of egg samples was measured by stoichiometry. First, Mahalanobis distance algorithm was used to remove abnormal sample data. Second, savitzky–golay and wavelet threshold denoising combined with standard normalized variable were used to pretreat the spectral data, respectively. Third, iteratively retains informative variables (IRIV), variable iterative space shrinkage approach, and competitive adaptive reweighted sampling were used for feature wavelength selection. Since the classification accuracy of support vector machine (SVM) model was affected by the selection of parameters, genetic algorithm (GA) was introduced to search the optimal parameters in SVM and compared with grid search algorithm. Finally, the result indicated that the classification accuracy of training set and test set of the optimal classification model (IRIV‐GA‐SVM) reached 99.29% and 97.87%, respectively. Thus, it is feasible to use hyperspectral image technology to detect egg freshness grade.
中文翻译：
基于高光谱成像技术的鸡蛋新鲜度无损检测
为了无损，快速地确定鸡蛋的新鲜度，本文使用了高光谱成像技术。使用高光谱图像采集系统（400.68–1,001.612 nm）采集了三个新鲜度等级的200个样品的高光谱数据，然后通过化学计量法测量了鸡蛋样品的新鲜度。首先，使用Mahalanobis距离算法删除异常样本数据。其次，分别使用savitzky-golay和小波阈值去噪与标准归一化变量进行预处理。第三，迭代保留信息变量（IRIV），使用变量迭代空间缩小方法，并使用竞争性自适应重加权采样进行特征波长选择。由于支持向量机（SVM）模型的分类精度受参数选择的影响，因此引入遗传算法（GA）在SVM中搜索最优参数，并与网格搜索算法进行比较。最后，结果表明，最佳分类模型（IRIV‐GA‐SVM）的训练集和测试集的分类准确率分别达到99.29％和97.87％。因此，使用高光谱图像技术检测鸡蛋的新鲜度是可行的。
更新日期：2020-04-25
中文翻译：
基于高光谱成像技术的鸡蛋新鲜度无损检测
为了无损，快速地确定鸡蛋的新鲜度，本文使用了高光谱成像技术。使用高光谱图像采集系统（400.68–1,001.612 nm）采集了三个新鲜度等级的200个样品的高光谱数据，然后通过化学计量法测量了鸡蛋样品的新鲜度。首先，使用Mahalanobis距离算法删除异常样本数据。其次，分别使用savitzky-golay和小波阈值去噪与标准归一化变量进行预处理。第三，迭代保留信息变量（IRIV），使用变量迭代空间缩小方法，并使用竞争性自适应重加权采样进行特征波长选择。由于支持向量机（SVM）模型的分类精度受参数选择的影响，因此引入遗传算法（GA）在SVM中搜索最优参数，并与网格搜索算法进行比较。最后，结果表明，最佳分类模型（IRIV‐GA‐SVM）的训练集和测试集的分类准确率分别达到99.29％和97.87％。因此，使用高光谱图像技术检测鸡蛋的新鲜度是可行的。
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