A new method was developed to detect fish freshness nondestructively by combining electronic nose (E-nose) and electronic tongue (E-tongue) in conjunction with chemometric methods. An E-nose with nine metal oxide semiconductor gas sensors and a commercial E-tongue were employed in this research. Pseudosciaena crocea stored at 4 °C for different days were used as experimental samples. Total viable counts (TVC) of the fish were detected by the conventional method. E-nose and E-tongue data were analyzed by principal component analysis. Three-layer radial basis function neural network (RBF-NN) models were established for qualitative discrimination of the fish freshness. Performances of RBF-NN models with different numbers of principal components (PCs) as the input were compared. Experimental results revealed that the best RBF-NN model was acquired at seven PCs of E-nose data with an optimal performance of 87.9% and 80.0% in the training set and prediction set respectively. While, the best RBF-NN model of E-tongue data analysis was at five PCs with an optimal performance of 86.3% in the training set and 81.8% in prediction set. Another RBF-NN model was built with the combination of E-nose and E-tongue. The result shows that the discrimination rates improved to 94.0% and 93.9% in the training set and prediction set respectively. A support vector machine regression model was applied to establish a relationship between the combined data from E-nose and E-tongue and from TVC values for quantitative determination. A high correlation was found between the merged data and the parameter of TVC with correlation coefficients more than 0.91. The results proved that, a single system of E-nose and E-tongue was enough to classify samples stored on different days at 4 °C, while a higher discrimination rate was acquired by the combination of the two sensors. The combined system could also be used to quantitatively evaluate the fish freshness. In conclusion, the combined system of E-nose and E-tongue in conjunction with appropriate chemometric analysis methods can conveniently and nondestructively evaluate the freshness of fish stored at 4 °C.

A colorimetric sensor array is a rapid and high sensitivity sensor for the detection and identification of volatile organic compounds. Theoretical investigations are performed to study the binding ability of the colorimetric sensor array with volatile organic compounds. Cobalt-porphyrin is selected to investigate the binding ability of the colorimetric sensor array with small volatile organic compounds. The binding energy of cobalt-porphyrin with small volatile organic compounds, such as O₂, N₂, H₂S, trimethylamine, propanol, propane, ethyl acetate, butanone and so on, is investigated using density functional theory (DFT) methods at three different spin multiplicities: low-spin (singlet), intermediate-spin (triplet) and high-spin (quintet) states. The relative and absolute binding energies of all the complexes are obtained at the optimized geometries. The triplet state is found to have the lowest energy for the CoP–O₂ complex, whereas the singlet state has the lowest energy for the other complexes. The binding energies for the complexes considered are in order starting from the lowest energy state: H₂S < propane < O₂ < N₂ < ethyl acetate < butanone < propanol < trimethylamine. This theoretical result can be used to optimize the sensor to increase the detection ability of the colorimetric sensor array.

A metalloporphyrin-based fluorescent sensor was developed to determine the acid value in frying oil. The electronic and structural performances of iron tetraphenylporphyrin (FeTPP) were theoretically investigated using time-dependent density functional theory and density functional theory at the B3LYP/LANL2DZ level. The quantified FeTPP-based fluorescent sensor results revealed its excellent performance in discriminating different analytes. In the present work, the acid value of palm olein was determined after every single frying cycle. A total of 10 frying cycles were conducted each day for 10 consecutive days. The FeTPP-based fluorescent sensor was used to quantify the acid value, and the results were compared with the chemical data obtained by conventional titration method. The synchronous fluorescence spectrum for each sample was recorded. Parallel factor analysis was used to decompose the three-dimensional spectrum data. Then, the support vector regression (SVR), partial least squares, and back-propagation artificial neural network methods were applied to build the regression models. After the comparison of the constructed models, the SVR models exhibited the highest correlation coefficients among all models, with 0.9748 and 0.9276 for the training and test sets, respectively. The findings suggested the potential of FeTPP-based fluorescent sensor in rapid monitoring of frying oil quality and perhaps also in other foods with higher oil contents.

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