基于仿生电子鼻的肉品新鲜度多信息融合识别技术
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摘要
为有效提高肉品检测的效率，基于原始的感官检验，结合理化指标，以不同冷藏条件下的鸡肉样品、猪肉样品为研究对象，开展对肉新鲜度进行识别的仿生触觉和仿生电子鼻技术研究。着重探讨基于仿生触觉技术和仿生电子鼻技术的多传感器信息融合技术的肉品新鲜度识别。
基于人体鼻腔CT图像，构建了一个三维鼻腔模型，利用计算流体动力学(CFD)方法研究其气流动力学特性，分析鼻腔的结构特征以及鼻腔内部气流的流体力学特性对嗅觉产生的影响。依此设计仿生嗅觉气体室系统，经相应的嗅觉实验证明，能使气味可到达每个传感器的敏感元件处，其接触反应时间能够满足传感器的有效吸附时间。
研究不同特征值对肉新鲜度识别效果影响，对仿生电子鼻传感器阵列的响应信号完成特征提取。并采用RBF神经网络、BP神经网络及支持向量机（SVM）进行识别分析，对比不同特征选择方法的预测结果，全段数据平均值(Mean)特征突出。探讨仿生电子鼻技术的肉品新鲜度模式识别，在三种识别模型预测结果，SVM识别模型识别率最优，且样本数越少，优势越明显。该模型对鸡肉样品和猪肉样品的正确识别率在8℃时分别为92.35%和91.49%；0℃时，分别为90.87%和90.48%。优化传感器阵列，优化阵列后的仿生电子鼻的SVM肉品新鲜度识别模型识别率为91.47%，比初始阵列提高了3%。
利用WDW-20J型电子万能试验机模拟触觉，获取肉品弹性信息数据，对提取的仿生触觉信息的原始特征信息进行主成分分析（PCA）并提取特征变量。分别采用线性判别分析、RBF神经网络和组合网络结合PCA提取的特征信息建立肉品新鲜度的识别模型。结果表明遗传优化的组合RBF神经网络识别模型识别率最优，该模型对鸡肉和猪肉的正确识别率在8℃时，分别为85.54%和85.12%；0℃时，分别为83.37%和82.91%。
针对不同冷藏条件下的鸡肉样品、猪肉样品，提取其弹性信息和“指纹”气味信息的原始特征信息。采用传感器特征层融合的方法，利用粒子群优化的SVM结合主成分分析提取的仿生触觉和仿生电子鼻的特征融合信息，建立的肉品新鲜度的多传感器融合识别模型。结果表明肉品新鲜度的多传感器融合识别模型的识别率优于基于各单项技术识别模型，该模型对鸡肉样品和猪肉样品的识别率在8℃时，分别为95.20%和93.45%；0℃时，分别为94.23%和92.11%。研究结果表明，将仿生触觉和仿生电子鼻两项技术进行融合应用于肉新鲜度的识别具有高效性和准确性。
To improve the efficiency of meat freshness detection, the bionic electronic nose and tactiletechnology were studied with frozen chicken and pork samples based on the original sense testingcombining physical and chemical index. The multi-sensor information fusion technologies thatcan be used to identify meat freshness based on bionic tactile and electronic nose technologieswere essentially explored in this study.
Based on the CT images of a nose of human, a3D model of nasal cavity was established. Theairflow dynamics were studied, using computational fluid dynamics (CFD), to analyze the effectsof the nasal cavity structure and airflow dynamics inside the nasal cavity on the olfactorysensation. Accordingly, a gas-chamber system of bionic olfactory sensation imitating the nasalcavity was designed. It has been verified that odor can be transported to the locations where thesensitive components of each sensor assembled in the gas-chamber system; and their contact andreaction time can meet the requirement of valid absorption time of sensors.
The effects of eigenvalues on the identification of meat freshness were studied and thefeature extraction was completed for the response signal of sensor array of bionic nose. The RBFneural net, BP neural net and support vector machine (SVM) were applied for the identificationanalysis. The mean eigenvalues in all data set were very excellent in comparison with thepredicting results obtained from different eigenvalue selection methods. Comparing the predictingresults obtained from the three identification models, it shows that the SVM had the optimumidentification rate and this superiority was more obvious when the samples were lesser. Theaccuracy of the system in recognizing different meat freshness level for chicken and pork sampleswas up to92.35%and91.49%, respectively, at8℃; and,90.87%and90.48%, respectively, at0℃.The identification efficiency of SVM meat freshness using bionic nose with optimized array was91.47%and it increased3%compared with the initial array.
The WDW-20J electronic universal test machine was used to simulate the tactile sensation ofhuman and obtain the meat elasticity information. The original characteristic information of bionictactile sensation was extracted using primary component analysis (PCA) and the characteristicvariables were extracted as well. The models of meat freshness identification were establishedapplying the linear identification analysis, RBF neural net and combined net combining thecharacteristic information from PCA. The results show that identification model of hereditaryoptimization RBF neural net had the optimum identification rate. Its accuracy for identifyingchicken and pork met samples was up to85.54%and85.12%, respectively, at8℃; and,83.37% and82.91%, respectively, at0℃.
The original characteristics information of meat elasticity and odor “fingerprint” wereabstracted from chicken and pork samples frozen at different conditions. The multi-sensor fusionidentification models were established applying the multi-sensor information fusion technologiesand utilizing the characteristic fusion information of bionic tactile and electronic nose abstractedby SVM combing PCA. The experimental results show that identification rate of multi-sensorfusion identification model was superior to each individual identification method. Its identificationrate for chicken and pork samples was up to95.20%and93.45%, respectively, at8℃; and,94.23%and92.11%, respectively, at0℃. It experimentally verified the high feasibility andaccuracy of combining the electronic nose and tactile technology for meat freshness detection.
基于人体鼻腔CT图像，构建了一个三维鼻腔模型，利用计算流体动力学(CFD)方法研究其气流动力学特性，分析鼻腔的结构特征以及鼻腔内部气流的流体力学特性对嗅觉产生的影响。依此设计仿生嗅觉气体室系统，经相应的嗅觉实验证明，能使气味可到达每个传感器的敏感元件处，其接触反应时间能够满足传感器的有效吸附时间。
研究不同特征值对肉新鲜度识别效果影响，对仿生电子鼻传感器阵列的响应信号完成特征提取。并采用RBF神经网络、BP神经网络及支持向量机（SVM）进行识别分析，对比不同特征选择方法的预测结果，全段数据平均值(Mean)特征突出。探讨仿生电子鼻技术的肉品新鲜度模式识别，在三种识别模型预测结果，SVM识别模型识别率最优，且样本数越少，优势越明显。该模型对鸡肉样品和猪肉样品的正确识别率在8℃时分别为92.35%和91.49%；0℃时，分别为90.87%和90.48%。优化传感器阵列，优化阵列后的仿生电子鼻的SVM肉品新鲜度识别模型识别率为91.47%，比初始阵列提高了3%。
利用WDW-20J型电子万能试验机模拟触觉，获取肉品弹性信息数据，对提取的仿生触觉信息的原始特征信息进行主成分分析（PCA）并提取特征变量。分别采用线性判别分析、RBF神经网络和组合网络结合PCA提取的特征信息建立肉品新鲜度的识别模型。结果表明遗传优化的组合RBF神经网络识别模型识别率最优，该模型对鸡肉和猪肉的正确识别率在8℃时，分别为85.54%和85.12%；0℃时，分别为83.37%和82.91%。
针对不同冷藏条件下的鸡肉样品、猪肉样品，提取其弹性信息和“指纹”气味信息的原始特征信息。采用传感器特征层融合的方法，利用粒子群优化的SVM结合主成分分析提取的仿生触觉和仿生电子鼻的特征融合信息，建立的肉品新鲜度的多传感器融合识别模型。结果表明肉品新鲜度的多传感器融合识别模型的识别率优于基于各单项技术识别模型，该模型对鸡肉样品和猪肉样品的识别率在8℃时，分别为95.20%和93.45%；0℃时，分别为94.23%和92.11%。研究结果表明，将仿生触觉和仿生电子鼻两项技术进行融合应用于肉新鲜度的识别具有高效性和准确性。
To improve the efficiency of meat freshness detection, the bionic electronic nose and tactiletechnology were studied with frozen chicken and pork samples based on the original sense testingcombining physical and chemical index. The multi-sensor information fusion technologies thatcan be used to identify meat freshness based on bionic tactile and electronic nose technologieswere essentially explored in this study.
Based on the CT images of a nose of human, a3D model of nasal cavity was established. Theairflow dynamics were studied, using computational fluid dynamics (CFD), to analyze the effectsof the nasal cavity structure and airflow dynamics inside the nasal cavity on the olfactorysensation. Accordingly, a gas-chamber system of bionic olfactory sensation imitating the nasalcavity was designed. It has been verified that odor can be transported to the locations where thesensitive components of each sensor assembled in the gas-chamber system; and their contact andreaction time can meet the requirement of valid absorption time of sensors.
The effects of eigenvalues on the identification of meat freshness were studied and thefeature extraction was completed for the response signal of sensor array of bionic nose. The RBFneural net, BP neural net and support vector machine (SVM) were applied for the identificationanalysis. The mean eigenvalues in all data set were very excellent in comparison with thepredicting results obtained from different eigenvalue selection methods. Comparing the predictingresults obtained from the three identification models, it shows that the SVM had the optimumidentification rate and this superiority was more obvious when the samples were lesser. Theaccuracy of the system in recognizing different meat freshness level for chicken and pork sampleswas up to92.35%and91.49%, respectively, at8℃; and,90.87%and90.48%, respectively, at0℃.The identification efficiency of SVM meat freshness using bionic nose with optimized array was91.47%and it increased3%compared with the initial array.
The WDW-20J electronic universal test machine was used to simulate the tactile sensation ofhuman and obtain the meat elasticity information. The original characteristic information of bionictactile sensation was extracted using primary component analysis (PCA) and the characteristicvariables were extracted as well. The models of meat freshness identification were establishedapplying the linear identification analysis, RBF neural net and combined net combining thecharacteristic information from PCA. The results show that identification model of hereditaryoptimization RBF neural net had the optimum identification rate. Its accuracy for identifyingchicken and pork met samples was up to85.54%and85.12%, respectively, at8℃; and,83.37% and82.91%, respectively, at0℃.
The original characteristics information of meat elasticity and odor “fingerprint” wereabstracted from chicken and pork samples frozen at different conditions. The multi-sensor fusionidentification models were established applying the multi-sensor information fusion technologiesand utilizing the characteristic fusion information of bionic tactile and electronic nose abstractedby SVM combing PCA. The experimental results show that identification rate of multi-sensorfusion identification model was superior to each individual identification method. Its identificationrate for chicken and pork samples was up to95.20%and93.45%, respectively, at8℃; and,94.23%and92.11%, respectively, at0℃. It experimentally verified the high feasibility andaccuracy of combining the electronic nose and tactile technology for meat freshness detection.
引文
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