| 基于高光谱技术的猪肉肌红蛋白含量无损检测 |
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| 引用本文： | 王立舒, 胡金耀, 房俊龙, 陈曦, 李闯. 基于高光谱技术的猪肉肌红蛋白含量无损检测[J]. 农业工程学报, 2021, 37(16): 287-294. DOI: 10.11975/j.issn.1002-6819.2021.16.035 |
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| 作者姓名： | 王立舒 胡金耀 房俊龙 陈曦 李闯 |
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| 作者单位： | 1.东北农业大学电气与信息学院，哈尔滨 150030 |
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| 摘 要： | 为充分利用猪肉光谱与图像信息，实现猪肉肌红蛋白含量的在线检测，该研究提出一种基于深度学习模型的猪肉肌红蛋白含量无损检测方法。采用高光谱设备采集冷藏过程中猪肉高光谱图像，通过ENVI5.3选择图像感兴趣区域（Region Of Interest，ROI），分别提取ROI平均光谱信息与主成分图像信息。利用卷积自动编码器（Convolutional Auto Encoder，CAE）提取光谱与图像信息深度特征，分别建立光谱特征、图像特征及图-谱融合特征与肌红蛋白含量之间关系的卷积神经网络（Convolutional Neural Network，CNN）预测模型。其中基于融合深度特征CNN预测模型准确度较高，该模型对脱氧肌红蛋白（DeoMb）、氧合肌红蛋白（OxyMb）、高铁肌红蛋白（MetMb）含量预测集决定系数分别为0.964 5、0.973 2、0.958 5，预测集均方根误差RMSEP分别为0.015 8、0.226 6、0.381 6。为进一步验证图-谱融合特征与猪肉肌红蛋白存在对应关系，分别建立偏最小二乘回归（Partial Least Squares Regression，PLSR）、支持向量机回归（Support Vector Regression，SVR）预测模型。结果表明：CAE能充分提取图像与光谱特征；基于融合特征建立回归模型能提高肌红蛋白含量预测精度，相比于光谱信息与图像信息，以MetMb为例，其分别提高5.42%、16.12%。该检测方法为肉类质量在线检测提供参考，具有好的应用前景。
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| 关 键 词： | 无损检测 光谱特征 高光谱图片 卷积神经网络 卷积自编码器 |
| 收稿时间： | 2021-08-13 |
| 修稿时间： | 2021-08-13 |
Non-destructive detection of pork myoglobin content based on hyperspectral technology |
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| Wang Lishu, Hu Jinyao, Fang Junlong, Chen Xi, Li Chuang. Non-destructive detection of pork myoglobin content based on hyperspectral technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(16): 287-294. DOI: 10.11975/j.issn.1002-6819.2021.16.035 |
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| Authors: | Wang Lishu Hu Jinyao Fang Junlong Chen Xi Li Chuang |
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| Affiliation: | 1.School of Electricity and Information, Northeast Agricultural University, Harbin 150030 |
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| Abstract: | Abstract: Hyperspectral imaging system can widely be expected to acquire a set of sample images within certain spectral bands in each pixel at the same time. In this study, a rapid detection was proposed for the myoglobin content in pork samples using spectral images and deep learning. The pork was placed under the cold storage conditions at 4°C, where a total of 250 pork samples were settled at different times (0, 24, 48, 72, 96 and 120 h). A hyperspectral imager was used to collect the pork hyperspectral images (400 to 1 000 nm). ENVI5.3 software was also selected to determine the region of interest (ROI) in the hyperspectral images, thereby to extract the full-band average spectrum and principal component image of ROI. Subsequently, a savitzky-golay (SG) filter was used to denoise the spectral information for the curve smoothness and spectral resolution. A convolutional auto encoder (CAE) was utilized to extract spectral depth features. A prediction model was finally established for the content of deoxymyolglobin (DeoMb), oxymyoglobin (OxyMb) and metmyoglobin (MetMb) in the pork samples. The results showed that the determination coefficients of test datasets were 0.923 8, 0.920 3, and 0.909 2, and the root mean square errors (RMSE) were 0.033 4, 0.619 7, and 0.809 1, respectively. Furthermore, the image information of adjacent wavelengths was highly correlated against the image extraction and storage. Principal Component Analysis (PCA) was utilized to reduce the dimension of hyperspectral images for better storage and processing. As such, the images under all bands were linearly combined to form a principal component image in the ENVI5.3 software. The first three principal component images represented 90.62% of the original hyperspectral image, where the contribution rate of the first principal component was 88.50%, indicating the most information. Therefore, the first principal component image was selected for the subsequent image extraction. The first principal component image was unified to the size of 16×16 pixels, and then converted into a 768-dimensional column vector for the extraction of image depth features using a convolutional encoder. DeoM, OxyMb, and MetMb content prediction models were established using image depth features, in which the determination coefficients of test datasets were 0.772 1, 0.828 7, and 0.825 4, while the RMSE of prediction were 0.105 8, 1.302 7, and 1.566 7. The spectral and image features were fused at the data level, and then the fusion data was input into the CAE to extract the deep fusion features. The DeoMb, OxyMb and MetMb content prediction models were also established using the fusion depth features. The determination coefficients of test datasets were 0.964 5, 0.973 2, and 0.958 5, while the RMSE of prediction were 0.015 8, 0.226 6, and 0.381 6. Obviously, the determination coefficients of test dataset were improved, while the RMSE were reduced, compared with the individual image and spectrum information. Partial least square regression (PLSR) and support vector machine regression (SVR) prediction models were also established to further verify the relationship between the graph-spectrum fusion feature and pork myoglobin. It was found that the determination coefficients of the test dataset were greater than 0.85. Consequently, the convolutional autoencoder can be expected to extract the deep fusion features of image and spectral information. Moreover, the fusion features can better reflect the internal and external information of pork. The CNN regression model using the fusion features can also be used to improve the prediction accuracy. This finding can provide a new better way to detect the myoglobin content in pork using hyperspectral imaging. |
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| Keywords: | nondestructive detection spectral feature hyperspectral image convolutional neural network convolutional autoencoder |
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