Porphyrins and related macrocycles have been intensively exploited as sensing materials in chemical sensors, since in these devices they mimic most of their biological functions, such as reversible binding, catalytic activation, and optical changes. Such a magnificent bouquet of properties allows applying porphyrin derivatives to different transducers, ranging from nanogravimetric to optical devices, also enabling the realization of multifunctional chemical sensors, in which multiple transduction mechanisms are applied to the same sensing layer. Potential applications are further expanded through sensor arrays, where cross-selective sensing layers can be applied for the analysis of complex chemical matrices. The possibility of finely tuning the macrocycle properties by synthetic modification of the different components of the porphyrin ring, such as peripheral substituents, molecular skeleton, coordinated metal, allows creating a vast library of porphyrinoid-based sensing layers. From among these, one can...

Porphyrinoids for Chemical Sensor Applications.

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/ novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.

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One-class classification: taxonomy of study and review of techniques

Review on metabolomics for food authentication

Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques.

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

Food Analysis Using Artificial Senses

Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition.

Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors; next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data.

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Near infrared (NIR) spectroscopy combined with multivariate analysis were attempted to determine freshness of eggs. Independent component analysis (ICA) and principle component analysis (PCA) algorithms were performed comparatively to extract effective features from the original data. Artificial neural network combined with genetic algorithms (GA–ANN) were employed to calibrate regression model. Some parameters of GA–ANN model were optimized by cross-validation in building models. The performance of the final optimized model was evaluated according to the root mean square error of prediction (RMSEP) and the correlation coefficient (R) in the prediction set. The optimal performance was obtained when 7 ICs from ICA were used in GA–ANN model. It was achieved with RMSEP = 2.443 and R = 0.879. This work shows that NIR spectroscopy combined with multivariate calibration has significant potential in the analysis of freshness of eggs. Industrial relevance Freshness makes a major contribution to the quality of egg and egg products, because consumers may perceive variability in freshness as lack of quality. Egg freshness grading was mostly relied on storage time. However, based on individual differences of eggs, freshness of them fluctuated. Consumers might buy un-fresh eggs from the market. This work presents a non-destructive method for the measurement of egg freshness, and builds a robust calibration model to improve the prediction ability. The research data presents a potential way for fast, non-destructive and automatic measurement of freshness in egg industry.

Hao Lin

Papers

Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition.

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis

Identification of egg’s freshness using NIR and support vector data description

Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array