In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
In order to recognize the jamming pattern in anti-jamming, a novel fuzzy jamming recognition method based on statistic parameters of received signal’s power spectral density (PSD) is proposed. It exploits PSD’s shap...In order to recognize the jamming pattern in anti-jamming, a novel fuzzy jamming recognition method based on statistic parameters of received signal’s power spectral density (PSD) is proposed. It exploits PSD’s shape factor and skewness of received signal as classified characters of jamming pattern. After the mean center and variance of each jamming pattern are calculated by using some jamming samples, an exponential fuzzy membership function is used to calculate the membership value of the recognized sample. Finally, the jamming pattern of received signal is recognized by the maximum membership principle. The simulation results show that the proposed algorithm can recognize common eight jamming patterns accurately.展开更多
Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a nov...Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.展开更多
In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extractin...In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.展开更多
In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization...In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.展开更多
This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition ...This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.展开更多
Looking upon every encoding state of quantum information systems as a quantum information pattern, A kind of pattern-distance between different patterns as a measurement of comparability of quantum information pattern...Looking upon every encoding state of quantum information systems as a quantum information pattern, A kind of pattern-distance between different patterns as a measurement of comparability of quantum information patterns is defined, and two kinds of recognition algorithms based on pattern-distance for quantum information are proposed. They can respectively recognize quantum information with known objective pattern and unknown objective pattern. In the two algorithms, the phases and occurrence probabilities of different eigenpattems of quantum information are sufficiently considered. Two examples demonstrate the feasibility and effectiveness of the two recognition methods. These algorithms point out a new and important path for applications of quantum information and pattern recognition.展开更多
Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliabili...Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the airdefense system for the naval warships. Air target threat level judgment is an important component in naval warship com-bat command decision-making systems. According to the threat level judgment of air targets during the air defense of sin-gle naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algo-rithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamicallyupdate its parameters according to the state change of the attacking targets and the environment. The method presentedhere can be used for the air defense system threat judgment in the naval warships.展开更多
To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy ...To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.展开更多
In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the label...In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.展开更多
开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法...开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法准确率不足。针对上述问题,从PRI序列还原角度出发,并结合PRI序列本质是时序序列的特点,提出GAIN-LSTM(Generative Adversarial Imputation Nets and Long Short Term Memory)网络架构,其先对丢失脉冲位置进行补全操作,恢复PRI调制规律,然后对还原后PRI序列进行调制模式识别。仿真结果表明,提出的GAIN-LSTM网络架构在脉冲丢失率70%时仍保持95%的正确识别率。展开更多
The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as hi...The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.展开更多
为挖掘乌龙茶品种鲜叶的特征特性,以绿茶品种‘福鼎大毫茶’一芽二叶或三叶为对照(CK),分析比较了高香优质乌龙茶品种‘茗科1号’一芽二叶或三叶(TM)与中小开面二至四叶(MM)在转录和代谢水平的组成差异。结果表明,茶树品种特性和采摘标...为挖掘乌龙茶品种鲜叶的特征特性,以绿茶品种‘福鼎大毫茶’一芽二叶或三叶为对照(CK),分析比较了高香优质乌龙茶品种‘茗科1号’一芽二叶或三叶(TM)与中小开面二至四叶(MM)在转录和代谢水平的组成差异。结果表明,茶树品种特性和采摘标准为影响茶鲜叶转录和代谢轮廓的重要因素,其中CK、TM和MM的转录与代谢轮廓相互间均有良好的模式区分。差异表达基因与差异代谢物的富集和关联分析结果表明,TM vs CK与MM vs TM的差异表达基因在分子功能、细胞组分和生物过程方面存在不同的GO功能富集模式,且前者拥有较多的生物过程和较少的细胞组分;“植物次生代谢产物的生物合成”、“精氨酸和脯氨酸代谢”与“类胡萝卜素生物合成”、“苯丙素的生物合成”、“甘氨酸、丝氨酸和苏氨酸代谢”分别是TM vsCK、MM vs TM在2种组学分析中的前20条共有KEGG富集代谢通路。TM vs CK中L-苯丙氨酸、反式肉桂酸和4-胍基丁酸丰度的显著降低,以及MM vs TM中胆碱丰度的显著提高和L-苯丙氨酸、L-色氨酸、L-高丝氨酸丰度的显著降低均与相应共富集代谢通路中多个基因的显著上调或下调表达密切相关。此外,MM vs TM中脱落酸丰度的显著提高与“类胡萝卜素生物合成”通路中紫黄质脱环氧化酶和番茄红素β-环化酶基因的显著上调表达高度相关。这可为阐明‘茗科1号’茶鲜叶生物学特征及其多茶类兼制特性提供参考依据。展开更多
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金Sponsored by National Nature Science Foundation of China ( 61072078)China Postdoctoral Science Foundation Funded Project ( 20090461426)Jiangsu Planned Projects for Postdoctoral Research Funds ( 0902039C)
文摘In order to recognize the jamming pattern in anti-jamming, a novel fuzzy jamming recognition method based on statistic parameters of received signal’s power spectral density (PSD) is proposed. It exploits PSD’s shape factor and skewness of received signal as classified characters of jamming pattern. After the mean center and variance of each jamming pattern are calculated by using some jamming samples, an exponential fuzzy membership function is used to calculate the membership value of the recognized sample. Finally, the jamming pattern of received signal is recognized by the maximum membership principle. The simulation results show that the proposed algorithm can recognize common eight jamming patterns accurately.
基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,China
文摘Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.
文摘In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.
基金supported by Project of the National Natural Science Foundation of China(Grant No.62073256)in part by Shaanxi Provincial Science and Technology Department(Grant No.2020GY-125)。
文摘In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(61101179)
文摘This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.
文摘Looking upon every encoding state of quantum information systems as a quantum information pattern, A kind of pattern-distance between different patterns as a measurement of comparability of quantum information patterns is defined, and two kinds of recognition algorithms based on pattern-distance for quantum information are proposed. They can respectively recognize quantum information with known objective pattern and unknown objective pattern. In the two algorithms, the phases and occurrence probabilities of different eigenpattems of quantum information are sufficiently considered. Two examples demonstrate the feasibility and effectiveness of the two recognition methods. These algorithms point out a new and important path for applications of quantum information and pattern recognition.
基金This project was supported by the National Defense Foundation of China(40108070103)
文摘Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the airdefense system for the naval warships. Air target threat level judgment is an important component in naval warship com-bat command decision-making systems. According to the threat level judgment of air targets during the air defense of sin-gle naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algo-rithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamicallyupdate its parameters according to the state change of the attacking targets and the environment. The method presentedhere can be used for the air defense system threat judgment in the naval warships.
文摘To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.
基金supported by the National Natural Science of China(6057407560705004).
文摘In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.
文摘开展脉冲重复间隔(Pulse Repetition Interval,PRI)模式识别工作是电子支援系统的一项重要任务。现代复杂电磁环境下,受雷达辐射源部署和接收设备本身影响,雷达脉冲丢失率极高,导致分选后PRI序列调制规律被破坏,现有的PRI模式识别方法准确率不足。针对上述问题,从PRI序列还原角度出发,并结合PRI序列本质是时序序列的特点,提出GAIN-LSTM(Generative Adversarial Imputation Nets and Long Short Term Memory)网络架构,其先对丢失脉冲位置进行补全操作,恢复PRI调制规律,然后对还原后PRI序列进行调制模式识别。仿真结果表明,提出的GAIN-LSTM网络架构在脉冲丢失率70%时仍保持95%的正确识别率。
基金Project(51171209)supported by the National Natural Science Foundation of China
文摘The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.
文摘为挖掘乌龙茶品种鲜叶的特征特性,以绿茶品种‘福鼎大毫茶’一芽二叶或三叶为对照(CK),分析比较了高香优质乌龙茶品种‘茗科1号’一芽二叶或三叶(TM)与中小开面二至四叶(MM)在转录和代谢水平的组成差异。结果表明,茶树品种特性和采摘标准为影响茶鲜叶转录和代谢轮廓的重要因素,其中CK、TM和MM的转录与代谢轮廓相互间均有良好的模式区分。差异表达基因与差异代谢物的富集和关联分析结果表明,TM vs CK与MM vs TM的差异表达基因在分子功能、细胞组分和生物过程方面存在不同的GO功能富集模式,且前者拥有较多的生物过程和较少的细胞组分;“植物次生代谢产物的生物合成”、“精氨酸和脯氨酸代谢”与“类胡萝卜素生物合成”、“苯丙素的生物合成”、“甘氨酸、丝氨酸和苏氨酸代谢”分别是TM vsCK、MM vs TM在2种组学分析中的前20条共有KEGG富集代谢通路。TM vs CK中L-苯丙氨酸、反式肉桂酸和4-胍基丁酸丰度的显著降低,以及MM vs TM中胆碱丰度的显著提高和L-苯丙氨酸、L-色氨酸、L-高丝氨酸丰度的显著降低均与相应共富集代谢通路中多个基因的显著上调或下调表达密切相关。此外,MM vs TM中脱落酸丰度的显著提高与“类胡萝卜素生物合成”通路中紫黄质脱环氧化酶和番茄红素β-环化酶基因的显著上调表达高度相关。这可为阐明‘茗科1号’茶鲜叶生物学特征及其多茶类兼制特性提供参考依据。
