智能人工味觉分析方法在几种食品质量检验中的应用研究
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摘要
随着生活水平的提高,人们对食品质量检验与质量控制越来越重视,不但要求检测方法准确、客观,而且要求快速、简便,因此,引进新的检测手段并探索其在食品质量检验与质量控制中应用的可行性具有重要的意义。人工味觉技术(电子舌)即是符合这些要求的一种分析、识别液体味道的新型分析技术。人工味觉领域主要有三个研究方向：传感器及传感器阵列的研制、模式识别方法的优化与比较、在各种分析问题中的应用。除了开发具有优良性能的传感器之外,恰当的模式识别方法对于电子舌的成功应用也是至关重要的。对于食品工业来说,人工味觉技术的最大优势是可以辅助、取代甚至超越人类的相应感觉,而与味觉相关的食品质量属性正是应该利用人工味觉技术进行检测的方面。本论文的研究范围主要集中在电子舌模式识别方法的优化与比较、在食品工业领域中的具体应用,从而为开辟人工味觉技术的应用途径提供理论依据与数据支持。
将机器学习领域开发的新兴模式识别工具引进电子舌领域解决模式识别问题是非常必要的,以克服通常采用的那些模式识别方法所具有的缺点。本论文首次将随机森林算法引入到人工味觉技术的模式识别中来,考察其在食品样品分类(3个数据集)和回归问题(3个数据集)上的性能,并与目前比较流行的算法,BP神经网络和支持向量机的性能进行了比较。在采用了严格的模型验证方法(5-重交互验证)的情况下,随机森林表现出了优秀的分类性能和预测性能,而且是在没有经过任何数据预处理和特征选择/提取的前提下获得的；尤其是对于不平衡、多类别和小样本的数据,随机森林更是表现出了卓越的分类性能,BP神经网络、支持向量机和随机森林对三个分类数据集交互验证集的整体平均正确分类率分别为83.35%、58.06%和98.83%；对三个回归数据集交互验证集的整体平均Q2分别为0.886、0.353和0.913；而且,对于独立测试集,随机森林也给出了令人满意的预测结果(整体平均正确分类率和Q2分别为99.75%和0.932),这意味着由电子舌生成的特征结合随机森林所构建的模型能够用于实际的样品检测。
电子舌传感器电化学信号与食品理化指标之间具有相关性是采用电子舌对食品进行分析的基础。本论文以不同品牌的10%橙汁饮料和不同类型不同质量等级的食醋为例,以电子舌的测量结果结合能够反映其感官品质(味道)的主要理化指标的测量结果,对样品进行了检验,以考察该技术在食品质量检验中应用的可行性,并为相应的感官检验法(味觉检验)提供可行的辅助手段。结果表明,电子舌的电化学信号与食品的理化指标之间具有显著的相关性,说明可以用电子舌检测食品的相关质量,同时也可以用相关的理化指标来解释电子舌的分析结果；电子舌可以很好地区分不同品牌的橙汁饮料、不同类型的食醋和不同质量等级的食醋；主成分分析时第一主成分可以解释为甜度,典型判别分析时,第一二典型变量分别可以解释为甜度和糖酸比,而且样品是近似按照这些指标的升序在坐标轴正方向上进行排列的；并且通过建立适当的模型(如随机森林分类模型)电子舌可以预测未知样品的种类；在对样品进行区分时主成分分析和典型判别分析具有一定的优势,但是,当比较(模仿)竞争对手的同类产品时,聚类分析和软独立模型类别分析具有优势,可以简单地初步判断产品之间在味道上是否具有明显的差异。这些结果都表明人工味觉分析方法可以根据食品的感官特性(味道属性)对其进行质量检验,从而证明其在食品质量感官检验中具有应用潜力。
以人工味觉技术为依托,以先进的模式识别方法为手段,提出了人工味觉结合随机森林的分析方法,在方法学研究的基础上进行了应用型研究,以在食品质量控制领域中具有代表性的应用案例为例考察该方法的实际效果。具体应用包括食品地理来源识别、食品贮藏条件鉴别、食品生产批次质量稳定性检测、食品贮藏期质量稳定性检测、货架期的确定及贮藏时间的测定。结果表明,人工味觉技术结合不同数据处理方法均能够胜任上述检测任务,为其在食品质量控制领域中的实际应用提供了基础研究数据,同时为食品研究提供了新的研究方法。并提出了采用主成分绘制质控图、主成分随时间演进图来判断产品质量稳定性的方法。
People pay more attention to food quality inspection and quality control with increasing life quality. The detection methods for food quality are required to be not only accurate and objective, but also rapid and convenient. Thus, it may be very important to introduce new detection means and investigate their feasibility in food quality inspection and quality control. Artificial taste technique (electronic tongue, E-tongue) as a new technique is one of the ways matching these requirements, which could be used to analyze and recognize the taste of liquid. The researches in the artificial taste field have been focused on three main aspects:the developments of sensor and sensor array, the optimizations and comparisons of pattern recognition methods, and the applications to various analytical tasks. Besides the development of sensors with excellent performance, appropriate pattern recognition methods are crucial for successful applications of E-tongues. The key advantage of the artificial taste is that it may be possible to assist, substitute, even surpass the human sensory, and may be suitable to detect the quality attributions about the taste of food. The study scopes of this paper will focus on the optimizations and comparisons of pattern recognition methods and the applications to the food industry. The study offers theory and data bases to open up the application ways of artificial taste technique.
It is necessary for the E-tongue field to introduce novel and more effective pattern recognition methods developed in the machine learning community, in order to overcome the problems of pattern recognition methods usually used. In this paper, for the first time, RF is introduced and investigated for the E-tongue data processing, and the performance of RF in classification (three data sets) and regression (three data sets) will be focused on. Furthermore, the comparisons of the performance of RF with currently more popular methods, BPNN and SVM, have been done. RF with5-fold cross-validation and without data preprocessing or feature selection/extraction, exhibits better classification and prediction performance than other two classical methods—BPNN and SVM, especially for unbalanced, multiclass and small sample data sets. The average correct rates (CR) on CV sets of the three data sets performed by BPNN, SVM and RF were83.35%,58.06%and98.83%, respectively. The average Q2on CV sets of the three data sets performed by BPNN, SVM and RF were0.886,0.353and0.913, respectively. Moreover, for the independent testing sets, RF also gives satisfactory prediction results (average CR and Q2were99.75%and0.932, respectively), which means the prediction models constructed by the E-tongue signal features and RF can actually be employed for the sample inspection.
Whether the physicochemical indexes of food are correlated with the sensor response signals of the E-tongue is the basis of applying the E-tongue to analyze food. In this paper, the feasibilities of the E-tongue for food quality inspection are studied by using10%orange juices of different brands and Chinese vinegars of different types and different grades as examples through combining the measurement results of the E-tongue and the main physicochemical indexes related with sensory quality (taste quality) of food, with the purpose of offering a feasible assistant means for the sensory evaluation (taste evaluation). The results indicate that:(1) the physicochemical indexes of food are significant correlated with the sensor response signals of the E-tongue, which means the E-tongue can be used to detect the taste quality of foods and the related physicochemical indexes can be used to explain the results obtained by the E-tongue;(2) the E-tongue can differentiate10%orange juices of different brands and Chinese vinegars of different types and different grades;(3) the first principle component can be explained as sweetness in PCA (principle component analysis) and the first and second canonical variables can be explained as sweetness and the ratio of sugar and acid in CDA (canonical discriminant analysis), respectively, besides, these indexes are ranged in an increasing sequence along the positive directions of the coordinates;(4) the E-tongue can predict the class memberships and grades of the unknown samples by constructing models, such as RF classification models;(5) PCA and CDA have some advantages when differentiate samples, but cluster analysis and SIMCA (soft independent modeling of class analogy) have advantages when compare (imitate) the same types of products with competitors and can initially judge whether the diversities exist in the taste quality between products. All these results confirm the E-tongue can be applied for food quality inspection according to the sensory profiles (taste properties) of food, which also prove that the E-tongue has potential for the application to food sensory inspection.
An analytical method based on artificial taste and random forest has been established and investigated when being applied for food quality control. The investigated cases include the recognition of food geographical origin, the identification of food under different storage conditions, the quality stability study of different batches of food, the quality stability study of food under storage period, the determination of shelf life and storage times of food. The results show'artificial taste technique coupled with different data analysis methods can fulfill all those tasks. These are basic researches for the application of the established analytical method in the food quality control field and open up a new research field for the artificial taste technique. At same time, this established analytical method provides a new means for food research. The methods of the quality control chart plotted based on principle component (PC) and the plot of PC vs time were also proposed in this paper.
将机器学习领域开发的新兴模式识别工具引进电子舌领域解决模式识别问题是非常必要的,以克服通常采用的那些模式识别方法所具有的缺点。本论文首次将随机森林算法引入到人工味觉技术的模式识别中来,考察其在食品样品分类(3个数据集)和回归问题(3个数据集)上的性能,并与目前比较流行的算法,BP神经网络和支持向量机的性能进行了比较。在采用了严格的模型验证方法(5-重交互验证)的情况下,随机森林表现出了优秀的分类性能和预测性能,而且是在没有经过任何数据预处理和特征选择/提取的前提下获得的；尤其是对于不平衡、多类别和小样本的数据,随机森林更是表现出了卓越的分类性能,BP神经网络、支持向量机和随机森林对三个分类数据集交互验证集的整体平均正确分类率分别为83.35%、58.06%和98.83%；对三个回归数据集交互验证集的整体平均Q2分别为0.886、0.353和0.913；而且,对于独立测试集,随机森林也给出了令人满意的预测结果(整体平均正确分类率和Q2分别为99.75%和0.932),这意味着由电子舌生成的特征结合随机森林所构建的模型能够用于实际的样品检测。
电子舌传感器电化学信号与食品理化指标之间具有相关性是采用电子舌对食品进行分析的基础。本论文以不同品牌的10%橙汁饮料和不同类型不同质量等级的食醋为例,以电子舌的测量结果结合能够反映其感官品质(味道)的主要理化指标的测量结果,对样品进行了检验,以考察该技术在食品质量检验中应用的可行性,并为相应的感官检验法(味觉检验)提供可行的辅助手段。结果表明,电子舌的电化学信号与食品的理化指标之间具有显著的相关性,说明可以用电子舌检测食品的相关质量,同时也可以用相关的理化指标来解释电子舌的分析结果；电子舌可以很好地区分不同品牌的橙汁饮料、不同类型的食醋和不同质量等级的食醋；主成分分析时第一主成分可以解释为甜度,典型判别分析时,第一二典型变量分别可以解释为甜度和糖酸比,而且样品是近似按照这些指标的升序在坐标轴正方向上进行排列的；并且通过建立适当的模型(如随机森林分类模型)电子舌可以预测未知样品的种类；在对样品进行区分时主成分分析和典型判别分析具有一定的优势,但是,当比较(模仿)竞争对手的同类产品时,聚类分析和软独立模型类别分析具有优势,可以简单地初步判断产品之间在味道上是否具有明显的差异。这些结果都表明人工味觉分析方法可以根据食品的感官特性(味道属性)对其进行质量检验,从而证明其在食品质量感官检验中具有应用潜力。
以人工味觉技术为依托,以先进的模式识别方法为手段,提出了人工味觉结合随机森林的分析方法,在方法学研究的基础上进行了应用型研究,以在食品质量控制领域中具有代表性的应用案例为例考察该方法的实际效果。具体应用包括食品地理来源识别、食品贮藏条件鉴别、食品生产批次质量稳定性检测、食品贮藏期质量稳定性检测、货架期的确定及贮藏时间的测定。结果表明,人工味觉技术结合不同数据处理方法均能够胜任上述检测任务,为其在食品质量控制领域中的实际应用提供了基础研究数据,同时为食品研究提供了新的研究方法。并提出了采用主成分绘制质控图、主成分随时间演进图来判断产品质量稳定性的方法。
People pay more attention to food quality inspection and quality control with increasing life quality. The detection methods for food quality are required to be not only accurate and objective, but also rapid and convenient. Thus, it may be very important to introduce new detection means and investigate their feasibility in food quality inspection and quality control. Artificial taste technique (electronic tongue, E-tongue) as a new technique is one of the ways matching these requirements, which could be used to analyze and recognize the taste of liquid. The researches in the artificial taste field have been focused on three main aspects:the developments of sensor and sensor array, the optimizations and comparisons of pattern recognition methods, and the applications to various analytical tasks. Besides the development of sensors with excellent performance, appropriate pattern recognition methods are crucial for successful applications of E-tongues. The key advantage of the artificial taste is that it may be possible to assist, substitute, even surpass the human sensory, and may be suitable to detect the quality attributions about the taste of food. The study scopes of this paper will focus on the optimizations and comparisons of pattern recognition methods and the applications to the food industry. The study offers theory and data bases to open up the application ways of artificial taste technique.
It is necessary for the E-tongue field to introduce novel and more effective pattern recognition methods developed in the machine learning community, in order to overcome the problems of pattern recognition methods usually used. In this paper, for the first time, RF is introduced and investigated for the E-tongue data processing, and the performance of RF in classification (three data sets) and regression (three data sets) will be focused on. Furthermore, the comparisons of the performance of RF with currently more popular methods, BPNN and SVM, have been done. RF with5-fold cross-validation and without data preprocessing or feature selection/extraction, exhibits better classification and prediction performance than other two classical methods—BPNN and SVM, especially for unbalanced, multiclass and small sample data sets. The average correct rates (CR) on CV sets of the three data sets performed by BPNN, SVM and RF were83.35%,58.06%and98.83%, respectively. The average Q2on CV sets of the three data sets performed by BPNN, SVM and RF were0.886,0.353and0.913, respectively. Moreover, for the independent testing sets, RF also gives satisfactory prediction results (average CR and Q2were99.75%and0.932, respectively), which means the prediction models constructed by the E-tongue signal features and RF can actually be employed for the sample inspection.
Whether the physicochemical indexes of food are correlated with the sensor response signals of the E-tongue is the basis of applying the E-tongue to analyze food. In this paper, the feasibilities of the E-tongue for food quality inspection are studied by using10%orange juices of different brands and Chinese vinegars of different types and different grades as examples through combining the measurement results of the E-tongue and the main physicochemical indexes related with sensory quality (taste quality) of food, with the purpose of offering a feasible assistant means for the sensory evaluation (taste evaluation). The results indicate that:(1) the physicochemical indexes of food are significant correlated with the sensor response signals of the E-tongue, which means the E-tongue can be used to detect the taste quality of foods and the related physicochemical indexes can be used to explain the results obtained by the E-tongue;(2) the E-tongue can differentiate10%orange juices of different brands and Chinese vinegars of different types and different grades;(3) the first principle component can be explained as sweetness in PCA (principle component analysis) and the first and second canonical variables can be explained as sweetness and the ratio of sugar and acid in CDA (canonical discriminant analysis), respectively, besides, these indexes are ranged in an increasing sequence along the positive directions of the coordinates;(4) the E-tongue can predict the class memberships and grades of the unknown samples by constructing models, such as RF classification models;(5) PCA and CDA have some advantages when differentiate samples, but cluster analysis and SIMCA (soft independent modeling of class analogy) have advantages when compare (imitate) the same types of products with competitors and can initially judge whether the diversities exist in the taste quality between products. All these results confirm the E-tongue can be applied for food quality inspection according to the sensory profiles (taste properties) of food, which also prove that the E-tongue has potential for the application to food sensory inspection.
An analytical method based on artificial taste and random forest has been established and investigated when being applied for food quality control. The investigated cases include the recognition of food geographical origin, the identification of food under different storage conditions, the quality stability study of different batches of food, the quality stability study of food under storage period, the determination of shelf life and storage times of food. The results show'artificial taste technique coupled with different data analysis methods can fulfill all those tasks. These are basic researches for the application of the established analytical method in the food quality control field and open up a new research field for the artificial taste technique. At same time, this established analytical method provides a new means for food research. The methods of the quality control chart plotted based on principle component (PC) and the plot of PC vs time were also proposed in this paper.
引文
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