基于近红外光谱和电子鼻技术的固态发酵过程检测研究及应用
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摘要
为有效提高固态发酵过程检测与控制的效率,以蛋白饲料固态发酵为研究对象,开展了基于近红外光谱和电子鼻技术的固态发酵过程检测研究及应用。着重探讨了基于近红外光谱技术的固态发酵过程参数检测方法,探讨了基于近红外光谱技术、电子鼻技术以及多传感器信息融合技术的固态发酵过程状态模式识别。具体研究工作如下：
(1)探讨了基于近红外光谱技术的固态发酵过程参数检测方法。首先对获取的固态发酵物样本的近红外光谱采用一阶导数法进行预处理,然后利用联合区间偏最小二乘(siPLS)法优选特征光谱子区间,接着引入遗传算法(GA)从优选的特征光谱子区间中进一步筛选特征波长变量,最后利用入选的特征波长变量结合偏最小二乘(PLS)法建立固态发酵过程参数pH和湿度的定量检测模型。试验结果显示,对于参数pH,其最终检测模型建立仅选用了45个特征波长变量,且当参与模型建立的主因子数为7时,可获得最佳的预测性能。该最佳模型在训练集中的交互验证均方根误差(RMSECV)和相关系数(R。)分别为0.0583和0.9878；当利用验证集中的独立样本对该模型进行验证时,其预测均方根误差(RMSEP)和相关系数(Rp)分别为0.0779和0.9779。对于参数湿度,其最终检测模型建立仅选用了53个特征波长变量,且当参与模型建立的主因子数为4时,可获得最佳的预测性能。该最佳模型在训练集中的RMSECV和Rc。分别为1.3286%w/w和0.8992；当利用验证集中的独立样本对该模型进行验证时,其RMSEP和Rp分别为1.2668%w/w和0.8700。研究结果表明,利用近红外光谱技术来实现固态发酵过程参数的快速检测是可行的；另外,在模型校正过程中进行近红外光谱特征波长的筛选是有必要的,可有效降低预测模型的复杂度、同时提高预测模型的泛化性能。
(2)探讨了基于近红外光谱技术的固态发酵过程状态模式识别。首先对获取的固态发酵物样本的近红外光谱,采用离散小波变换(DWT)结合主成分分析(PCA)对其进行滤噪和特征提取；然后利用提取的特征变量建立基于—类分类算法——支持向量数据描述(SVDD)的固态发酵过程状态识别模型。同时,四个传统的二分类算法,即线性判别分析(LDA)、K最近邻(KNN)、BP神经网络(BPNN)及支持向量机(SVM),分别有比较地被用来建立固态发酵过程状态识别模型。试验结果显示,当训练集中目标类与非目标类样本数量均衡时,各识别模型在验证集中均能取得较好的识别结果；当训练集中目标类与非目标类样本数量失衡时,SVDD方法就显示出其在处理失衡训练样本集问题上的独特优势。当训练集中的目标类与非目标类样本个数比为1：4和1：8时,SVDD识别模型在验证集中的正确识别率分别为95%和90%。而同等条件下,基于传统二分类算法的四个识别模型在验证集中的正确识别率均不高于70%。研究结果表明,利用近红外光谱技术来实现固态发酵过程状态的快速识别是可行的；另外,SVDD算法能有效处理出现失衡训练样本集时,固态发酵过程状态精确识别模型的建立问题,拓展了该算法的应用领域。
(3)探讨了基于电子鼻技术的固态发酵过程状态模式识别。首先对提取的电子鼻信号的原始特征信息进行PCA处理并提取特征变量,然后引入不同的线性(LDA和KNN)与非线性(SVM)模式识别方法并结合PCA提取的特征变量建立固态发酵过程状态的识别模型。试验结果显示,通过主成分得分图可以发现,不同发酵状态样本的聚类趋势是很明显的,尤其是在蛋白饲料固态发酵前期采集的样本可以利用PCA进行直接区分；另外,通过对比各线性与非线性识别模型的分类性能,SVM方法更适合用于本研究特定对象识别模型的建立,且最佳SVM识别模型在训练集和验证集中的正确识别率分别为97.14%和91.43%。研究结果表明,利用电子鼻技术快速识别固态发酵的过程状态是可行的。
(4)探讨了基于多传感器信息融合技术的固态发酵过程状态模式识别。首先对试验获取的固态发酵物样本的近红外光谱和电子鼻信号进行预处理和初始特征提取；然后利用PCA分别提取预处理后的近红外光谱和电子鼻信号的特征信息：接着建立基于各单技术的固态发酵过程状态识别模型,并在模型校正过程中优化参与模型建立的最佳主因子数；最后引入独立分量分析(ICA)对优化后的基于各单技术识别模型的最佳主因子组合在特征级层面进行融合,并建立基于BP_Adaboost的固态发酵过程状态的最佳融合识别模型。研究结果显示,基于近红外光谱和电子鼻两技术融合的固态发酵过程状态的最佳BP_Adaboost识别模型,参与模型建立的独立分量个数为4,该最佳模型在训练集和验证集中的正确识别率分别为99.05%和94.29%。从基于各技术的固态发酵过程状态最佳BP_Adaboost识别模型的分类结果来看,两技术融合识别模型的分类性能无论是在训练集中还是验证集中都要优于基于各单技术识别模型的分类性能,且其模型的复杂度也要低于基于各单技术识别模型的复杂度。研究结果表明,将近红外光谱和电子鼻两技术进行融合应用于固态发酵过程状态的识别是可行的,且其模型的识别精度和稳定性均优于基于各单技术模型的识别精度和稳定性。
本研究为固态发酵过程检测与控制带来新思路,旨在提高固体发酵过程参数检测和过程状态监测的准确度和时效性,研究成果可为固态发酵过程监控仪器装备的研发提供研究基础。
In order to improve the efficiency of process detection and control of solid-state fermentation (SSF), this work attempted to the feasibility and method of the measurement of process parameters of SSF of protein feed by use of near-infrared spectroscopy (NIRS) techniques. In addition, the pattern recognition of process state of SSF was also focused on by use of NIRS, electronic nose, and multi-sensors information fusion technique in this work. The main research points are summarized as follows:
(1) The feasibility and method for the measurement of process parameters of SSF were studied by use of NIRS technique. Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the first derivative. Secondly, several efficient subintervals were selected by use of the synergy interval partial least squares (siPLS) algorithm. Then, some efficient wavelength variables were selected by use of the genetic algorithm (GA) from the subintervals obtained. Lastly, the partial least squares (PLS) model was developed by use of the efficient wavelength variables selected for the measurement of process parameters (i.e. pH and moisture content) of SSF. Experimental results showed as follows:For the parameter of pH, the optimal detection model was achieved when seven principal components (PCs) included based on the45efficient wavelength variables selected. The result of the root mean square error cross-validation (RMSECV) is0.0583and the correlation coefficient (Rc) is0.9878in the training set. When the performance of the best model is evaluated by the independent samples in the validation set, the result of the root mean square error prediction (RMSEP) is0.0779and the correlation coefficient (Rp) is0.9779in the validation set. For the parameter of moisture content, the optimal detection model was achieved when four PCs included based on the53efficient wavelength variables selected. The result of RMSECV is1.3286%w/w and Rc is0.8992in the training set. When the performance of the best model is evaluated by the independent samples in the validation set, the result of RMSEP is1.2668%w/w and Rp is0.8700in the validation set. The overall results demonstrate the potentials of NIRS technique in rapid measurement of process parameters of SSF. Additionally, it is necessary to select characteristic wavelength variables of near-infrared spectra in model calibration. It can effectively reduce the complexity and improve generalization performance of the detection model when NIRS technique is used for on-line detection of the process parameters of SSF.
(2) The pattern recognition for the process state of SSF was studied by use of NIRS technique. Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the discrete wavelet transform (DWT), and the feature vectors were extracted by use of principal component analysis (PCA) from the spectral data preprocessed. Then, the identified model was developed by use of support vector data description (SVDD) algorithm, which is a one-class classification method. Simultaneously, four traditional two-class classification approaches (i.e. linear discriminant analysis, LDA;K-nearest neighbor, KNN; back propagation neural networks, BPNN; and support vector machine, SVM) were comparatively utilized for monitoring time-related changes that occur during SSF. Experimental results showed that as follows:When the number of samples from the target class and those from non-target class in the training set is equal, all identification models can achieve good classification performance in the validation set. However, the SVDD model could reveal its unique superiority in disposing the imbalance training sets. When the ratios achieve one to four and one to eight, the identification rates of SVDD model are95%and90%in the validation set, respectively. Nevertheless, in the same condition, the classification rates of the other four models are all under70%. The overall results demonstrate that SVDD algorithm is a prominent approach in developing one-class classification model with imbalance training set, and NIRS technique combined with SVDD has high potential to monitor the process state of SSF in a no-invasion way.
(3) The pattern recognition for the process state of SSF was studied by use of electronic nose technique. The electronic nose technique, with the help of chemometrics analysis, was attempted in this work. Firstly, the feature vectors were extracted by use of PCA, and the number of PCs were optimized by a five-fold cross-validation in model calibration. Then, LDA, KNN, and SVM respectively were used to calibrate identification models in order to evaluate the influences of different linear and non-linear classification algorithms on the classification performance. Experimental results showed as follows: Investigated from PCs scores plot, seven sample groups appeared in cluster trend along two principal component axes, confirming the presence of seven different clusters just associated with their condition of fermentation. Especially, the samples from the initial stage of SSF of protein feed could be separated directly by PCA. In addition, the identification accuracy of SVM model was superior to those of the other two. and the optimal SVM model was obtained when five PCs were included. The identification rates of the SVM model were97.14%and91.43%in the training and validation sets, respectively. The overall results sufficiently demonstrate that the electronic nose technique coupled with an appropriate chemometrics method could be successfully used in identification of process state of SSF.
(4) The pattern recognition for the process state of SSF was studied by use of multi-sensors information fusion technique. Firstly, the raw spectra and electronic nose signals were preprocessed, and some initial feature information were extracted by use of traditional methods. Secondly, PC A was implemented on these initial feature information extracted from near-infrared spectra and electronic nose signals preprocessed, and then PCs vectors were extracted and optimized as the inputs of pattern recognition based on the single technique (i.e. NIRS or electronic nose technique) in model calibration. Lastly, the optimal PCs on the single technique model were fused in feature extraction level fusion by use of independent component analysis (ICA), and the best BP_Adaboost model was developed by use of the optimal independent components (ICs). Experimental results showed that the optimal fusion model of BP_Adaboost based on NIRS and electronic nose techniques was obtained when four ICs included. The identified rate equaled to99.05%in the training set, and94.29%in the validation set. Compared with the best BP_Adaboost model based on the single technique, the identified results of fusion model on the two techniques are much better than those of the single technique model both in the training and validation sets, and the complexity of the fusion model was also less than that of the single technique model. The overall results demonstrate that it is feasible to identify the process state of SSF with multi-sensors information fusion technique. The identification accuracy and stability of the recognition model from the multi-sensors information fusion were better than those of the recognition model from the single-sensor information.
This study provides a new idea for the process detection and control of SSF. The main aim of improving the accuracy and timeliness for the measurement of process parameters and the monitoring of process state of SSF has been achieved. The results in this work can provide research foundation for developing instruments and equipment for the monitoring of SSF process.
(1)探讨了基于近红外光谱技术的固态发酵过程参数检测方法。首先对获取的固态发酵物样本的近红外光谱采用一阶导数法进行预处理,然后利用联合区间偏最小二乘(siPLS)法优选特征光谱子区间,接着引入遗传算法(GA)从优选的特征光谱子区间中进一步筛选特征波长变量,最后利用入选的特征波长变量结合偏最小二乘(PLS)法建立固态发酵过程参数pH和湿度的定量检测模型。试验结果显示,对于参数pH,其最终检测模型建立仅选用了45个特征波长变量,且当参与模型建立的主因子数为7时,可获得最佳的预测性能。该最佳模型在训练集中的交互验证均方根误差(RMSECV)和相关系数(R。)分别为0.0583和0.9878；当利用验证集中的独立样本对该模型进行验证时,其预测均方根误差(RMSEP)和相关系数(Rp)分别为0.0779和0.9779。对于参数湿度,其最终检测模型建立仅选用了53个特征波长变量,且当参与模型建立的主因子数为4时,可获得最佳的预测性能。该最佳模型在训练集中的RMSECV和Rc。分别为1.3286%w/w和0.8992；当利用验证集中的独立样本对该模型进行验证时,其RMSEP和Rp分别为1.2668%w/w和0.8700。研究结果表明,利用近红外光谱技术来实现固态发酵过程参数的快速检测是可行的；另外,在模型校正过程中进行近红外光谱特征波长的筛选是有必要的,可有效降低预测模型的复杂度、同时提高预测模型的泛化性能。
(2)探讨了基于近红外光谱技术的固态发酵过程状态模式识别。首先对获取的固态发酵物样本的近红外光谱,采用离散小波变换(DWT)结合主成分分析(PCA)对其进行滤噪和特征提取；然后利用提取的特征变量建立基于—类分类算法——支持向量数据描述(SVDD)的固态发酵过程状态识别模型。同时,四个传统的二分类算法,即线性判别分析(LDA)、K最近邻(KNN)、BP神经网络(BPNN)及支持向量机(SVM),分别有比较地被用来建立固态发酵过程状态识别模型。试验结果显示,当训练集中目标类与非目标类样本数量均衡时,各识别模型在验证集中均能取得较好的识别结果；当训练集中目标类与非目标类样本数量失衡时,SVDD方法就显示出其在处理失衡训练样本集问题上的独特优势。当训练集中的目标类与非目标类样本个数比为1：4和1：8时,SVDD识别模型在验证集中的正确识别率分别为95%和90%。而同等条件下,基于传统二分类算法的四个识别模型在验证集中的正确识别率均不高于70%。研究结果表明,利用近红外光谱技术来实现固态发酵过程状态的快速识别是可行的；另外,SVDD算法能有效处理出现失衡训练样本集时,固态发酵过程状态精确识别模型的建立问题,拓展了该算法的应用领域。
(3)探讨了基于电子鼻技术的固态发酵过程状态模式识别。首先对提取的电子鼻信号的原始特征信息进行PCA处理并提取特征变量,然后引入不同的线性(LDA和KNN)与非线性(SVM)模式识别方法并结合PCA提取的特征变量建立固态发酵过程状态的识别模型。试验结果显示,通过主成分得分图可以发现,不同发酵状态样本的聚类趋势是很明显的,尤其是在蛋白饲料固态发酵前期采集的样本可以利用PCA进行直接区分；另外,通过对比各线性与非线性识别模型的分类性能,SVM方法更适合用于本研究特定对象识别模型的建立,且最佳SVM识别模型在训练集和验证集中的正确识别率分别为97.14%和91.43%。研究结果表明,利用电子鼻技术快速识别固态发酵的过程状态是可行的。
(4)探讨了基于多传感器信息融合技术的固态发酵过程状态模式识别。首先对试验获取的固态发酵物样本的近红外光谱和电子鼻信号进行预处理和初始特征提取；然后利用PCA分别提取预处理后的近红外光谱和电子鼻信号的特征信息：接着建立基于各单技术的固态发酵过程状态识别模型,并在模型校正过程中优化参与模型建立的最佳主因子数；最后引入独立分量分析(ICA)对优化后的基于各单技术识别模型的最佳主因子组合在特征级层面进行融合,并建立基于BP_Adaboost的固态发酵过程状态的最佳融合识别模型。研究结果显示,基于近红外光谱和电子鼻两技术融合的固态发酵过程状态的最佳BP_Adaboost识别模型,参与模型建立的独立分量个数为4,该最佳模型在训练集和验证集中的正确识别率分别为99.05%和94.29%。从基于各技术的固态发酵过程状态最佳BP_Adaboost识别模型的分类结果来看,两技术融合识别模型的分类性能无论是在训练集中还是验证集中都要优于基于各单技术识别模型的分类性能,且其模型的复杂度也要低于基于各单技术识别模型的复杂度。研究结果表明,将近红外光谱和电子鼻两技术进行融合应用于固态发酵过程状态的识别是可行的,且其模型的识别精度和稳定性均优于基于各单技术模型的识别精度和稳定性。
本研究为固态发酵过程检测与控制带来新思路,旨在提高固体发酵过程参数检测和过程状态监测的准确度和时效性,研究成果可为固态发酵过程监控仪器装备的研发提供研究基础。
In order to improve the efficiency of process detection and control of solid-state fermentation (SSF), this work attempted to the feasibility and method of the measurement of process parameters of SSF of protein feed by use of near-infrared spectroscopy (NIRS) techniques. In addition, the pattern recognition of process state of SSF was also focused on by use of NIRS, electronic nose, and multi-sensors information fusion technique in this work. The main research points are summarized as follows:
(1) The feasibility and method for the measurement of process parameters of SSF were studied by use of NIRS technique. Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the first derivative. Secondly, several efficient subintervals were selected by use of the synergy interval partial least squares (siPLS) algorithm. Then, some efficient wavelength variables were selected by use of the genetic algorithm (GA) from the subintervals obtained. Lastly, the partial least squares (PLS) model was developed by use of the efficient wavelength variables selected for the measurement of process parameters (i.e. pH and moisture content) of SSF. Experimental results showed as follows:For the parameter of pH, the optimal detection model was achieved when seven principal components (PCs) included based on the45efficient wavelength variables selected. The result of the root mean square error cross-validation (RMSECV) is0.0583and the correlation coefficient (Rc) is0.9878in the training set. When the performance of the best model is evaluated by the independent samples in the validation set, the result of the root mean square error prediction (RMSEP) is0.0779and the correlation coefficient (Rp) is0.9779in the validation set. For the parameter of moisture content, the optimal detection model was achieved when four PCs included based on the53efficient wavelength variables selected. The result of RMSECV is1.3286%w/w and Rc is0.8992in the training set. When the performance of the best model is evaluated by the independent samples in the validation set, the result of RMSEP is1.2668%w/w and Rp is0.8700in the validation set. The overall results demonstrate the potentials of NIRS technique in rapid measurement of process parameters of SSF. Additionally, it is necessary to select characteristic wavelength variables of near-infrared spectra in model calibration. It can effectively reduce the complexity and improve generalization performance of the detection model when NIRS technique is used for on-line detection of the process parameters of SSF.
(2) The pattern recognition for the process state of SSF was studied by use of NIRS technique. Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the discrete wavelet transform (DWT), and the feature vectors were extracted by use of principal component analysis (PCA) from the spectral data preprocessed. Then, the identified model was developed by use of support vector data description (SVDD) algorithm, which is a one-class classification method. Simultaneously, four traditional two-class classification approaches (i.e. linear discriminant analysis, LDA;K-nearest neighbor, KNN; back propagation neural networks, BPNN; and support vector machine, SVM) were comparatively utilized for monitoring time-related changes that occur during SSF. Experimental results showed that as follows:When the number of samples from the target class and those from non-target class in the training set is equal, all identification models can achieve good classification performance in the validation set. However, the SVDD model could reveal its unique superiority in disposing the imbalance training sets. When the ratios achieve one to four and one to eight, the identification rates of SVDD model are95%and90%in the validation set, respectively. Nevertheless, in the same condition, the classification rates of the other four models are all under70%. The overall results demonstrate that SVDD algorithm is a prominent approach in developing one-class classification model with imbalance training set, and NIRS technique combined with SVDD has high potential to monitor the process state of SSF in a no-invasion way.
(3) The pattern recognition for the process state of SSF was studied by use of electronic nose technique. The electronic nose technique, with the help of chemometrics analysis, was attempted in this work. Firstly, the feature vectors were extracted by use of PCA, and the number of PCs were optimized by a five-fold cross-validation in model calibration. Then, LDA, KNN, and SVM respectively were used to calibrate identification models in order to evaluate the influences of different linear and non-linear classification algorithms on the classification performance. Experimental results showed as follows: Investigated from PCs scores plot, seven sample groups appeared in cluster trend along two principal component axes, confirming the presence of seven different clusters just associated with their condition of fermentation. Especially, the samples from the initial stage of SSF of protein feed could be separated directly by PCA. In addition, the identification accuracy of SVM model was superior to those of the other two. and the optimal SVM model was obtained when five PCs were included. The identification rates of the SVM model were97.14%and91.43%in the training and validation sets, respectively. The overall results sufficiently demonstrate that the electronic nose technique coupled with an appropriate chemometrics method could be successfully used in identification of process state of SSF.
(4) The pattern recognition for the process state of SSF was studied by use of multi-sensors information fusion technique. Firstly, the raw spectra and electronic nose signals were preprocessed, and some initial feature information were extracted by use of traditional methods. Secondly, PC A was implemented on these initial feature information extracted from near-infrared spectra and electronic nose signals preprocessed, and then PCs vectors were extracted and optimized as the inputs of pattern recognition based on the single technique (i.e. NIRS or electronic nose technique) in model calibration. Lastly, the optimal PCs on the single technique model were fused in feature extraction level fusion by use of independent component analysis (ICA), and the best BP_Adaboost model was developed by use of the optimal independent components (ICs). Experimental results showed that the optimal fusion model of BP_Adaboost based on NIRS and electronic nose techniques was obtained when four ICs included. The identified rate equaled to99.05%in the training set, and94.29%in the validation set. Compared with the best BP_Adaboost model based on the single technique, the identified results of fusion model on the two techniques are much better than those of the single technique model both in the training and validation sets, and the complexity of the fusion model was also less than that of the single technique model. The overall results demonstrate that it is feasible to identify the process state of SSF with multi-sensors information fusion technique. The identification accuracy and stability of the recognition model from the multi-sensors information fusion were better than those of the recognition model from the single-sensor information.
This study provides a new idea for the process detection and control of SSF. The main aim of improving the accuracy and timeliness for the measurement of process parameters and the monitoring of process state of SSF has been achieved. The results in this work can provide research foundation for developing instruments and equipment for the monitoring of SSF process.
引文
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