基于单一技术及多信息融合技术的猪肉新鲜度无损检测研究
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摘要
为满足消费者在肉品质量和安全方面的要求,对肉品新鲜度进行快速准确检测有着重要意义。反映肉品新鲜度的指标是多方面的,感官评价和理化检测都难以满足快速在线的检测要求,单一无损检测技术又难以实现全面准确的评价。本课题从变质猪肉中筛选出优势致腐菌,将其回接至新鲜肉样模拟猪肉的变质过程,分析猪肉变质过程中新鲜度指标之间的相关性,寻找最能反映新鲜度的指标,并利用近红外光谱技术,高光谱成像技术以及融合近红外光谱、计算机视觉和电子鼻三种技术对猪肉新鲜度进行无损检测,实现猪肉新鲜度的综合准确评价。主要研究内容如下：
1.猪肉优势致腐菌的筛选测定。微生物是引起肉品腐败变质的主要原因。试验从变质猪肉中优选得到5株优势致腐菌,结合形态、生理生化特征及16S rRNA分子鉴定方法,将其分别鉴定为Bacillus fusiformis J4、 Acinetobacter guillouiae P3、Enterobacter cloacae P5、Pseudomonas koreensis PS1和Brochothrix thermosphacta S5。进一步以TVB-N产量因子YTVB-N/CFU为指标定量分析各优势致腐菌的致腐能力。研究表明P.koreensis PS1对冷却猪肉致腐能力较强,B. fusiformis J4和B.thermosphacta S5致腐能力次之,而A.guillouiae P3和E. cloacae P5致腐能力较弱。通过把这些优势致腐菌定量回接至新鲜肉样,以模拟猪肉的变质过程,为后续无损检测研究提供建模所需要的样本。
2.新鲜度指标之间的相关性分析。新鲜度是反映肉品品质安全的重要参数,新鲜度的评价具有多个指标。为了寻找最能反映新鲜度的指标,试验将优势致腐菌P. koreensis PS1回接新鲜肉样并于4℃贮藏,定期取样通过感官评价、理化方法和微生物学方法检测反映肉品新鲜度的多个指标,并对新鲜度指标之间的相关性进行分析。结果表明,TVB-N在猪肉变质过程中变化较为明显,并且,TVB-N与总糖、蛋白质、感官评分、弹性、回复性和粘聚性等其它新鲜度指标之间均存在较强的相关性(P<0.01)。研究结果为寻找合适的无损检测技术进行肉品新鲜度的快速准确评价提供依据。
3.猪肉变质过程新鲜度的近红外光谱技术的快速检测。近红外光谱是一种能直接反映肉品内部化学组分含量的快速检测技术。试验以不同变质程度的猪肉样本为研究对象,对获取的近红外光谱数据,经光谱分析,TVB-N和其它指标(蛋白质、总糖、总脂肪和细菌总数)在近红外光谱区域均有各自的特定吸收谱峰,其强度随猪肉变质程度而发生明显变化；再利用标准正态变量变换(SNV)进行光谱预处理,采用联合区间偏最小二乘法(siPLS)优选与TVB-N等指标相关的波谱区间,并有比较地运用反向传播神经网络(BP-ANN)和siPLS两种方法分别构建TVB-N等指标预测的定量模型。试验结果表明,利用siPLS方法能够优选出与TVB-N等指标高度特异相关的特征波谱,结合BP-ANN或siPLS模型均能对猪肉变质过程中的TVB-N等指标实现同时定量测定,相互之间无明显影响,且BP-ANN模型对各指标的检测性能均优于siPLS模型。该结果为光谱技术应用于肉品新鲜度的快速检测提供依据。
4.猪肉变质过程新鲜度的高光谱成像技术的无损检测。高光谱成像技术是一种能获得更大信息量的无损检测新技术。本文仍以TVB-N为猪肉新鲜度评价指标,进行了采用高光谱成像技术手段来评价猪肉新鲜度的试验研究。试验以不同变质程度的猪肉样本为研究对象,对采集的高光谱图像数据,首先提取光谱信息进行处理,采用SNV光谱预处理结合siPLS方法优选出177个光谱特征变量；接着,对图像信息进行分析,有比较地采用主成分分析(PCA)和遗传联合区间偏最小二乘法(GA-siPLS)两种万法从海量的高光谱数据中进行降维处理,分别优选特征波长图像,试验结果表明GA-siPLS优选的5个特征波长图像更能表征肉样的TVB-N含量；然后从每个特征波长图像中提取基于灰度统计矩的6个纹理特征参数,共30个图像特征变量；最后,将177个光谱特征变量、30个图像特征变量以及融合两者信息的207(177+30)个特征变量各自进行主成分分析,分别提取合适的主成分因子与肉样TVB-N含量构建BP-ANN定量模型。研究结果表明,207个特征变量的融合模型要优于177个光谱特征变量的单一信息模型,也优于30个图像特征变量的单一信息模型,该融合模型对训练集中样本的交互验证均方根误差(RMSECV)为1.28mg/100g,决定系数(Rc2)为0.922,对预测集中样本的预测均方根误差(RMSEP)为1.60mg/100g,决定系数(RP2)为0.900。该结果为多信息融合技术综合评价肉品新鲜度提供理论依据。
5.猪肉变质过程新鲜度的多技术信息融合的综合评价。在采用单项技术的基础上,本文进一步利用多技术信息融合综合检测猪肉变质过程的新鲜度。试验利用近红外光谱、计算机视觉和电子鼻三种技术分别获取反映猪肉样本化学组分、颜色、纹理及气味等的特征信息数据,从各种信息数据中分别提取特征变量并进行特征层数据融合,通过主成分分析提取最佳得分向量作为模式识别的输入数据,运用BP-ANN方法建立新鲜度指标TVB-N含量的定量模型。研究结果表明,任意两种技术信息融合的模型均要优于单一技术信息模型,而基于三种技术(近红外光谱、计算机视觉和电子鼻)信息融合的模型对猪肉变质过程的TVB-N含量预测效果最佳,该模型对训练集中样本的交互验证均方根误差(RMSECV)为1.46mg/100g,决定系数(Rc2)为0.984,对预测集中样本的预测均方根误差(RMSEP)为2.73mg/100g,决定系数(Rp2)为0.953。结果表明,基于近红外光谱、计算机视觉和电子鼻三种技术的信息融合模型综合评价猪肉变质过程中的新鲜度是可行的,其模型检测的准确性和稳定性较单一技术或两种技术信息融合模型均有所提高。研究结果为多信息融合技术应用于猪肉质量安全的综合准确评价提供重要的理论依据。
本研究为利用多技术信息融合对猪肉新鲜度的综合准确评价提供了思路,对保障肉品质量与安全,维护消费者利益,有着重要的现实意义。
Rapid and accurate measurement of meat freshness is very significant in solving the food quality and safety for consumer sake. There are many indexs in assessing the meat freshness, Sensory evaluation and physical-chemical testing can not meet the requirements of rapid on-line detection, and single nondestructive testing technique is also difficult to achieve accurate comprehensive evaluation of meat freshness. In this work, the spoilage bacteria were isolated from the chilled metamorphic pork, and the fresh pork samples inoculated with the spoilage bacteria were stored to spoilage. Then, the pork freshness indicators were detected, the correlation among the pork freshness indicators was further analyzed during bacteria spoiling process. At the same time, the near infrared (NIR) spectroscopy, hyperspectral imaging, and NIR combined with computer vision (CV) and electronic nose (E-nose) were employed for non-destructive detecting the internal and external characteristic indexes of pork freshness, which achieves the comprehensive accurate evaluation of pork freshness. The main points are summarized as follows:
1. Isolation and identification of the dominant spoilage bacteria. Microorganism is the main cause of rottenness of meat.5strains dominant spoilage bacteria were isolated from the chilled metamorphic pork, which were identified as Bacillus fusiformis J4, Acinetobacter guillouiae P3, Enterobacter cloacae P5, Pseudomonas koreensis PS1and Brochothrix thermosphacta S5by the morphology, physiology, biochemistry and16S rRNA molecular biology. Then, the yield factor YTVB-N/CFU of the total volatile basic nitrogen (TVB-N) was used as quantitative indicators to assess the spoilage capability of the different spoilage bacteria. The results showed that P. koreensis PS1has the strongest spoilage capability in chilled pork, B. fusiformis J4and B. thermosphacta S5are the stronger spoilage ability, and A. guillouiae P3and E. cloacae P5are the weakest among them. Then, to provide the modeling experimental samples, the fresh pork samples were inoculated with these spoilage bacteria for simulating the pork metamorphic process and for non-destructive detecting of pork freshness in the later experiment.
2. Analysis on the correlation among the pork freshness indexes during bacterial spoiling process. Freshness is an important parameter for assessing the quality and safety of meat, which its evaluation have many indicators. In order to find the most indicator for reflecting freshness of meat, in the experiment, the fresh pork samples inoculated with P. koreensis PS1were stored in a refrigerator at4℃, the pork freshness indexes were detected by sensory evaluation, physicochemical methods and microbiological methods, and the correlation of these freshness indexes were analyzed during bacterial spoiling process. The results showed that the changes of TVB-N were significant, furthermore, it is significant difference (P<0.01) between TVB-N and total sugar, protein, sensory score, elasticity, resilience and adhesiveness, etc. And some of the appropriate non-destructive technology could be employed to assess pork freshness.
3. Rapid detection of pork freshness based on NIR during bacterial spoiling process. NIR can directly reflect the TVB-N conternt and can quickly detect the meat freshness. In this experiment, the pork samples of different freshness were used for study target. First, the different indexes have specific spectrum in the NIR region, and spectrum strength changes obviously with the pork spoilage grade. Then, synergy interval partial least squares (siPLS) was performed to select characteristic spectral variables of different indexes (TVB-N, total sugar, total fat, protein, and TVC) in pork based on NIR spectral data preprocessed by standard normal variate (SNV). The back-propagation neural network (BP-ANN) and siPLS were developed comparatively the quantitative models for predicting the different index. The experimental results showed that the optimum characteristic spectra related to each index can be selected by siPLS. The multi-index in pork can be determined simultaneously by BP-ANN or siPLS model during bacterial spoiling process. And the performance of BP-ANN model on the different indexes is better than siPLS model. The BP-ANN model have better prediction results of the indexes of TVB-N, total sugar, total lipid, and protein, which the determination coefficient (Rp2) are all more than0.850, except as TVC (Rp2=0.717). The results shows NIR can rapidly detect the meat freshness.
4. Non-destructive detection of pork freshness based on hyperspectral imaging technique (HSI) during bacterial spoiling process. In the paper, TVB-N was still as the evaluation index of pork freshness, HSI is an emerging non-destructive technique, which can obtain more information for assessing meat freshness. In this experiment, the hyperspectral image data were collected from the pork samples of different freshness. First,177spectral characteristic variables from the hyperspectral data were selected by SNV preprocessing combined with siPLS. Secondly, the characteristic wavelength images from the hyperspectral data were extracted comparatively using principal component analysis (PCA) and the genetic algorithm-synergy interval partial least squares (GA-siPLS) and5characteristic wavelength images by GA-siPLS are more relevant to TVB-N content in pork than PCA. Next,6statistical parameter based on gray statistical moments were extracted from each characteristic wavelength image, amount to30feature variables form image information. Finally, PCA was implemented on177spectra variables,30image variables and207(177+30) variables based on data-fusion, and the top principal components were extracted for developing the TVB-N prediction model using BP-ANN, respectively. The experimental results showed that the model based on data-fusion is superior to others, which was achieved with RMSECV=1.28mg/100g and Rc2=0.922in the training set, RMSEP=1.60mg/100g and Rp2=0.900in the prediction set.
5. Comprehensive evaluation of pork freshness based on multiple information fusion technology during bacterial spoiling process. Based on single technique for determinating the meat freshness, the study was further theorized that pork freshness during bacterial spoiling process was comprehensively evaluated by integrating NIR, CV and E-nose technology. In this experiment, the pork samples with different freshness were collected for data acquisition (such as chemical composition, color, texture, and odor, etc.) by three different techniques, respectively. Then, the individual characteristic variables from each sensor data were fused in feature level. Next, PC A was implemented on the individual characteristic variables and different data fusion from3different sensors data, and the top principal components were extracted for developing the TVB-N prediction model with BP-ANN, respectively. The experimental results showed that the model based on data fusion of three technologies (NIR, CV and E-nose) is superior to others, which was achieved with RMSECV=1.46mg/100g and Rc2=0.984in the training set, RMSEP=2.73mg/100g and Rp2=0.953in the prediction set. It is feasible to evaluate comprehensively the freshness during pork spoilage by integrating NIR, CV and E-nose technology through this experiment, and the accuracy and robustness of the model from three sensors information fusion were better than the model from single sensors or two sensors information. The study results offered a reference that multi-sensors information was applied to evaluate comprehensive pork quality and safety.
This research offers a new idea to detect pork freshness based on muti-technique information fusion, and there is also of great significance in ensuring pork quality and safety, as well as safeguarding consumer interests.
1.猪肉优势致腐菌的筛选测定。微生物是引起肉品腐败变质的主要原因。试验从变质猪肉中优选得到5株优势致腐菌,结合形态、生理生化特征及16S rRNA分子鉴定方法,将其分别鉴定为Bacillus fusiformis J4、 Acinetobacter guillouiae P3、Enterobacter cloacae P5、Pseudomonas koreensis PS1和Brochothrix thermosphacta S5。进一步以TVB-N产量因子YTVB-N/CFU为指标定量分析各优势致腐菌的致腐能力。研究表明P.koreensis PS1对冷却猪肉致腐能力较强,B. fusiformis J4和B.thermosphacta S5致腐能力次之,而A.guillouiae P3和E. cloacae P5致腐能力较弱。通过把这些优势致腐菌定量回接至新鲜肉样,以模拟猪肉的变质过程,为后续无损检测研究提供建模所需要的样本。
2.新鲜度指标之间的相关性分析。新鲜度是反映肉品品质安全的重要参数,新鲜度的评价具有多个指标。为了寻找最能反映新鲜度的指标,试验将优势致腐菌P. koreensis PS1回接新鲜肉样并于4℃贮藏,定期取样通过感官评价、理化方法和微生物学方法检测反映肉品新鲜度的多个指标,并对新鲜度指标之间的相关性进行分析。结果表明,TVB-N在猪肉变质过程中变化较为明显,并且,TVB-N与总糖、蛋白质、感官评分、弹性、回复性和粘聚性等其它新鲜度指标之间均存在较强的相关性(P<0.01)。研究结果为寻找合适的无损检测技术进行肉品新鲜度的快速准确评价提供依据。
3.猪肉变质过程新鲜度的近红外光谱技术的快速检测。近红外光谱是一种能直接反映肉品内部化学组分含量的快速检测技术。试验以不同变质程度的猪肉样本为研究对象,对获取的近红外光谱数据,经光谱分析,TVB-N和其它指标(蛋白质、总糖、总脂肪和细菌总数)在近红外光谱区域均有各自的特定吸收谱峰,其强度随猪肉变质程度而发生明显变化；再利用标准正态变量变换(SNV)进行光谱预处理,采用联合区间偏最小二乘法(siPLS)优选与TVB-N等指标相关的波谱区间,并有比较地运用反向传播神经网络(BP-ANN)和siPLS两种方法分别构建TVB-N等指标预测的定量模型。试验结果表明,利用siPLS方法能够优选出与TVB-N等指标高度特异相关的特征波谱,结合BP-ANN或siPLS模型均能对猪肉变质过程中的TVB-N等指标实现同时定量测定,相互之间无明显影响,且BP-ANN模型对各指标的检测性能均优于siPLS模型。该结果为光谱技术应用于肉品新鲜度的快速检测提供依据。
4.猪肉变质过程新鲜度的高光谱成像技术的无损检测。高光谱成像技术是一种能获得更大信息量的无损检测新技术。本文仍以TVB-N为猪肉新鲜度评价指标,进行了采用高光谱成像技术手段来评价猪肉新鲜度的试验研究。试验以不同变质程度的猪肉样本为研究对象,对采集的高光谱图像数据,首先提取光谱信息进行处理,采用SNV光谱预处理结合siPLS方法优选出177个光谱特征变量；接着,对图像信息进行分析,有比较地采用主成分分析(PCA)和遗传联合区间偏最小二乘法(GA-siPLS)两种万法从海量的高光谱数据中进行降维处理,分别优选特征波长图像,试验结果表明GA-siPLS优选的5个特征波长图像更能表征肉样的TVB-N含量；然后从每个特征波长图像中提取基于灰度统计矩的6个纹理特征参数,共30个图像特征变量；最后,将177个光谱特征变量、30个图像特征变量以及融合两者信息的207(177+30)个特征变量各自进行主成分分析,分别提取合适的主成分因子与肉样TVB-N含量构建BP-ANN定量模型。研究结果表明,207个特征变量的融合模型要优于177个光谱特征变量的单一信息模型,也优于30个图像特征变量的单一信息模型,该融合模型对训练集中样本的交互验证均方根误差(RMSECV)为1.28mg/100g,决定系数(Rc2)为0.922,对预测集中样本的预测均方根误差(RMSEP)为1.60mg/100g,决定系数(RP2)为0.900。该结果为多信息融合技术综合评价肉品新鲜度提供理论依据。
5.猪肉变质过程新鲜度的多技术信息融合的综合评价。在采用单项技术的基础上,本文进一步利用多技术信息融合综合检测猪肉变质过程的新鲜度。试验利用近红外光谱、计算机视觉和电子鼻三种技术分别获取反映猪肉样本化学组分、颜色、纹理及气味等的特征信息数据,从各种信息数据中分别提取特征变量并进行特征层数据融合,通过主成分分析提取最佳得分向量作为模式识别的输入数据,运用BP-ANN方法建立新鲜度指标TVB-N含量的定量模型。研究结果表明,任意两种技术信息融合的模型均要优于单一技术信息模型,而基于三种技术(近红外光谱、计算机视觉和电子鼻)信息融合的模型对猪肉变质过程的TVB-N含量预测效果最佳,该模型对训练集中样本的交互验证均方根误差(RMSECV)为1.46mg/100g,决定系数(Rc2)为0.984,对预测集中样本的预测均方根误差(RMSEP)为2.73mg/100g,决定系数(Rp2)为0.953。结果表明,基于近红外光谱、计算机视觉和电子鼻三种技术的信息融合模型综合评价猪肉变质过程中的新鲜度是可行的,其模型检测的准确性和稳定性较单一技术或两种技术信息融合模型均有所提高。研究结果为多信息融合技术应用于猪肉质量安全的综合准确评价提供重要的理论依据。
本研究为利用多技术信息融合对猪肉新鲜度的综合准确评价提供了思路,对保障肉品质量与安全,维护消费者利益,有着重要的现实意义。
Rapid and accurate measurement of meat freshness is very significant in solving the food quality and safety for consumer sake. There are many indexs in assessing the meat freshness, Sensory evaluation and physical-chemical testing can not meet the requirements of rapid on-line detection, and single nondestructive testing technique is also difficult to achieve accurate comprehensive evaluation of meat freshness. In this work, the spoilage bacteria were isolated from the chilled metamorphic pork, and the fresh pork samples inoculated with the spoilage bacteria were stored to spoilage. Then, the pork freshness indicators were detected, the correlation among the pork freshness indicators was further analyzed during bacteria spoiling process. At the same time, the near infrared (NIR) spectroscopy, hyperspectral imaging, and NIR combined with computer vision (CV) and electronic nose (E-nose) were employed for non-destructive detecting the internal and external characteristic indexes of pork freshness, which achieves the comprehensive accurate evaluation of pork freshness. The main points are summarized as follows:
1. Isolation and identification of the dominant spoilage bacteria. Microorganism is the main cause of rottenness of meat.5strains dominant spoilage bacteria were isolated from the chilled metamorphic pork, which were identified as Bacillus fusiformis J4, Acinetobacter guillouiae P3, Enterobacter cloacae P5, Pseudomonas koreensis PS1and Brochothrix thermosphacta S5by the morphology, physiology, biochemistry and16S rRNA molecular biology. Then, the yield factor YTVB-N/CFU of the total volatile basic nitrogen (TVB-N) was used as quantitative indicators to assess the spoilage capability of the different spoilage bacteria. The results showed that P. koreensis PS1has the strongest spoilage capability in chilled pork, B. fusiformis J4and B. thermosphacta S5are the stronger spoilage ability, and A. guillouiae P3and E. cloacae P5are the weakest among them. Then, to provide the modeling experimental samples, the fresh pork samples were inoculated with these spoilage bacteria for simulating the pork metamorphic process and for non-destructive detecting of pork freshness in the later experiment.
2. Analysis on the correlation among the pork freshness indexes during bacterial spoiling process. Freshness is an important parameter for assessing the quality and safety of meat, which its evaluation have many indicators. In order to find the most indicator for reflecting freshness of meat, in the experiment, the fresh pork samples inoculated with P. koreensis PS1were stored in a refrigerator at4℃, the pork freshness indexes were detected by sensory evaluation, physicochemical methods and microbiological methods, and the correlation of these freshness indexes were analyzed during bacterial spoiling process. The results showed that the changes of TVB-N were significant, furthermore, it is significant difference (P<0.01) between TVB-N and total sugar, protein, sensory score, elasticity, resilience and adhesiveness, etc. And some of the appropriate non-destructive technology could be employed to assess pork freshness.
3. Rapid detection of pork freshness based on NIR during bacterial spoiling process. NIR can directly reflect the TVB-N conternt and can quickly detect the meat freshness. In this experiment, the pork samples of different freshness were used for study target. First, the different indexes have specific spectrum in the NIR region, and spectrum strength changes obviously with the pork spoilage grade. Then, synergy interval partial least squares (siPLS) was performed to select characteristic spectral variables of different indexes (TVB-N, total sugar, total fat, protein, and TVC) in pork based on NIR spectral data preprocessed by standard normal variate (SNV). The back-propagation neural network (BP-ANN) and siPLS were developed comparatively the quantitative models for predicting the different index. The experimental results showed that the optimum characteristic spectra related to each index can be selected by siPLS. The multi-index in pork can be determined simultaneously by BP-ANN or siPLS model during bacterial spoiling process. And the performance of BP-ANN model on the different indexes is better than siPLS model. The BP-ANN model have better prediction results of the indexes of TVB-N, total sugar, total lipid, and protein, which the determination coefficient (Rp2) are all more than0.850, except as TVC (Rp2=0.717). The results shows NIR can rapidly detect the meat freshness.
4. Non-destructive detection of pork freshness based on hyperspectral imaging technique (HSI) during bacterial spoiling process. In the paper, TVB-N was still as the evaluation index of pork freshness, HSI is an emerging non-destructive technique, which can obtain more information for assessing meat freshness. In this experiment, the hyperspectral image data were collected from the pork samples of different freshness. First,177spectral characteristic variables from the hyperspectral data were selected by SNV preprocessing combined with siPLS. Secondly, the characteristic wavelength images from the hyperspectral data were extracted comparatively using principal component analysis (PCA) and the genetic algorithm-synergy interval partial least squares (GA-siPLS) and5characteristic wavelength images by GA-siPLS are more relevant to TVB-N content in pork than PCA. Next,6statistical parameter based on gray statistical moments were extracted from each characteristic wavelength image, amount to30feature variables form image information. Finally, PCA was implemented on177spectra variables,30image variables and207(177+30) variables based on data-fusion, and the top principal components were extracted for developing the TVB-N prediction model using BP-ANN, respectively. The experimental results showed that the model based on data-fusion is superior to others, which was achieved with RMSECV=1.28mg/100g and Rc2=0.922in the training set, RMSEP=1.60mg/100g and Rp2=0.900in the prediction set.
5. Comprehensive evaluation of pork freshness based on multiple information fusion technology during bacterial spoiling process. Based on single technique for determinating the meat freshness, the study was further theorized that pork freshness during bacterial spoiling process was comprehensively evaluated by integrating NIR, CV and E-nose technology. In this experiment, the pork samples with different freshness were collected for data acquisition (such as chemical composition, color, texture, and odor, etc.) by three different techniques, respectively. Then, the individual characteristic variables from each sensor data were fused in feature level. Next, PC A was implemented on the individual characteristic variables and different data fusion from3different sensors data, and the top principal components were extracted for developing the TVB-N prediction model with BP-ANN, respectively. The experimental results showed that the model based on data fusion of three technologies (NIR, CV and E-nose) is superior to others, which was achieved with RMSECV=1.46mg/100g and Rc2=0.984in the training set, RMSEP=2.73mg/100g and Rp2=0.953in the prediction set. It is feasible to evaluate comprehensively the freshness during pork spoilage by integrating NIR, CV and E-nose technology through this experiment, and the accuracy and robustness of the model from three sensors information fusion were better than the model from single sensors or two sensors information. The study results offered a reference that multi-sensors information was applied to evaluate comprehensive pork quality and safety.
This research offers a new idea to detect pork freshness based on muti-technique information fusion, and there is also of great significance in ensuring pork quality and safety, as well as safeguarding consumer interests.
引文
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