为了提高山核桃干果品质、缩短干燥时间和降低干燥能耗,以前期微波功率密度、转换点含水率和后期微波功率密度为试验因素,对山核桃坚果分段变功率微波干燥工艺进行了试验研究。通过单因素试验,研究了山核桃坚果微波干燥特性,确定了山核...为了提高山核桃干果品质、缩短干燥时间和降低干燥能耗,以前期微波功率密度、转换点含水率和后期微波功率密度为试验因素,对山核桃坚果分段变功率微波干燥工艺进行了试验研究。通过单因素试验,研究了山核桃坚果微波干燥特性,确定了山核桃坚果微波干燥各因素合适范围。通过三因素五水平的二次回归正交试验,建立了三因素与失水速率、单位质量干燥能耗以及干燥后物料蛋白质保存率、不饱和脂肪酸保存率、感官品质指标综合分值的二次回归数学模型,分析了三因素对各指标影响的显著性。利用多目标非线性优化方法,确定了山核桃坚果分段变功率微波干燥的最佳工艺参数组合,即前期干燥微波功率密度为6.5 k W/kg,转换点含水率为23.4%(干基),后期干燥微波功率密度为3.3 k W/kg。在此条件下,山核桃坚果失水速率为4.072%/min、单位质量干燥能耗为3.467 k W·h/kg、蛋白质保存率为92.15%、不饱和脂肪酸保存率为91.63%、感官品质指标综合分值为35.28分。研究结果为山核桃坚果干燥加工生产提供一定的理论依据。展开更多
It is difficult to differentiate small,but harmful,shell fragments of Chinese hickory nuts from their kernels since they are very similar in color.Including shell fragments of Chinese hickory nuts by mistake may creat...It is difficult to differentiate small,but harmful,shell fragments of Chinese hickory nuts from their kernels since they are very similar in color.Including shell fragments of Chinese hickory nuts by mistake may create safety hazards for consumers.Therefore,there is a need to develop an effective method to differentiate the shells from the kernels of Chinese hickory nuts.In this study,a deep learning approach based on a two-dimensional convolutional neural network(2D CNN)and long short-term memory(LSTM)integrated with hyperspectral imaging for distinguishing the shells and kernels of Chinese hickory nuts at the pixel level was proposed.Two classical classification methods,principal component analysis-K-nearest neighbors(PCA-KNN)and the support vector machine(SVM),were employed to establish identification models for comparison.The results showed that the 2D CNN-LSTM model achieved the best performance with an overall classification accuracy of 99.0%.Moreover,the shells in mixtures of shells and kernels were detected based on the proposed deep learning method and visualized for subsequent operations for the removal of foreign bodies.展开更多
文摘为了提高山核桃干果品质、缩短干燥时间和降低干燥能耗,以前期微波功率密度、转换点含水率和后期微波功率密度为试验因素,对山核桃坚果分段变功率微波干燥工艺进行了试验研究。通过单因素试验,研究了山核桃坚果微波干燥特性,确定了山核桃坚果微波干燥各因素合适范围。通过三因素五水平的二次回归正交试验,建立了三因素与失水速率、单位质量干燥能耗以及干燥后物料蛋白质保存率、不饱和脂肪酸保存率、感官品质指标综合分值的二次回归数学模型,分析了三因素对各指标影响的显著性。利用多目标非线性优化方法,确定了山核桃坚果分段变功率微波干燥的最佳工艺参数组合,即前期干燥微波功率密度为6.5 k W/kg,转换点含水率为23.4%(干基),后期干燥微波功率密度为3.3 k W/kg。在此条件下,山核桃坚果失水速率为4.072%/min、单位质量干燥能耗为3.467 k W·h/kg、蛋白质保存率为92.15%、不饱和脂肪酸保存率为91.63%、感官品质指标综合分值为35.28分。研究结果为山核桃坚果干燥加工生产提供一定的理论依据。
基金The authors gratefully acknowledge the financial support of the National Key Research and Development Program of China(Grant No.2017YFC1600805)the help of Jie Yang in studying convolution neural networks.Trade and manufacturer names are necessary to report factually on the available data。
文摘It is difficult to differentiate small,but harmful,shell fragments of Chinese hickory nuts from their kernels since they are very similar in color.Including shell fragments of Chinese hickory nuts by mistake may create safety hazards for consumers.Therefore,there is a need to develop an effective method to differentiate the shells from the kernels of Chinese hickory nuts.In this study,a deep learning approach based on a two-dimensional convolutional neural network(2D CNN)and long short-term memory(LSTM)integrated with hyperspectral imaging for distinguishing the shells and kernels of Chinese hickory nuts at the pixel level was proposed.Two classical classification methods,principal component analysis-K-nearest neighbors(PCA-KNN)and the support vector machine(SVM),were employed to establish identification models for comparison.The results showed that the 2D CNN-LSTM model achieved the best performance with an overall classification accuracy of 99.0%.Moreover,the shells in mixtures of shells and kernels were detected based on the proposed deep learning method and visualized for subsequent operations for the removal of foreign bodies.
