Jincan Lei

Discrimination of Lung Cancer Related Volatile Organic Compounds with a Colorimetric Sensor Array

A simple and rapid discrimination of four lung cancer related volatile organic compounds (VOCs) was achieved with a novel colorimetric sensor array. Based on the cross-responsive mechanism, the sensor system exhibited high sensitivity to selected lung cancer biomarkers, including p-xylene, styrene, isoprene, and hexanal with concentrations varying from 50 ppb to 500 ppb. By extracting color information, it provided good selectivity and fine discrimination of selected gases via pattern recognition with Fisher linear discriminant (FLD). Additionally, with the employment of the Takagi-Sogeno Fuzzy Neural Network (TSFNN), different concentrations of selected VOCs were discriminated. It also suggested that the colorimetric sensor array proposed could not only distinguish different lung cancer related VOCs but also discriminate specific VOCs of different concentrations with an average rate of classification up to 95%. Our preliminary study demonstrated that the cross-responsive sensor array had infinite potential for further clinical and commercial use for early diagnosis of lung cancer.

Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis

Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.

A novel device based on a fluorescent cross-responsive sensor array for detecting lung cancer related volatile organic compounds

In this paper, a novel, simple, rapid, and low-cost detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. For this task, a sensor array based on cross-responsive mechanism was designed. A special gas chamber was made to insure sensor array exposed to VOCs sufficiently and evenly, and FLUENT software was used to simulate the performance of the gas chamber. The data collection and processing system was used to detect fluorescent changes of the sensor arrays before and after reaction, and to extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene, and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze data. The results showed that the methods could 100% discriminate the four VOCs. What is more, combined with artificial neural network, the correct rate of quantitative detection was up to 100%, and the device obtained responses at concentrations below 50 ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer.

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.

Capillarity-based preparation system for optical colorimetric sensor arrays

In recent years, optical colorimetric sensor arrays have demonstrated beneficial features, including rapid response, high selectivity, and high specificity; as a result, it has been extensively applied in food inspection and chemical studies, among other fields. There are instruments in the current market available for the preparation of an optical colorimetric sensor array, but it lacks the corresponding research of the preparation mechanism. Therefore, in connection with the main features of this kind of sensor array such as consistency, based on the preparation method of contact spotting, combined with a capillary fluid model, Washburn equation, Laplace equation, etc., this paper develops a diffusion model of an optical colorimetric sensor array during its preparation and sets up an optical colorimetric sensor array preparation system based on this diffusion model. Finally, this paper compares and evaluates the sensor arrays prepared by the system and prepared manually in three aspects such as the quality of array point, response of array, and response result, and the results show that the performance index of the sensor array prepared by a system under this diffusion model is better than that of the sensor array of manual spotting, which meets the needs of the experiment.

A novel device based on a fluorescent cross-responsive sensor array for detecting pesticide residue

In this paper, a novel, simple, rapid, and low-cost detection device for pesticide residue was constructed. A sensor array based on a cross-responsive mechanism was designed. The data collection and processing system was used to detect fluorescent signal of the sensor arrays, and to extract unique patterns of the tested pesticide residue. Four selected pesticides, carbendazim, diazine, fenvalerate, and pentachloronitrobenzene, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze the data. The results showed that the methods could 100% discriminate the four pesticide residues. According to the standard regression linear curve of the fluorescence intensity and the concentration of pesticide, the quantitative value of the pesticide was detected, and the device obtained responses at concentrations below 8 ppb, and it has a good linear relationship in the range of 0.01–1 ppm. According to the results, the proposed detection device showed excellent selectivity and discrimination ability for the pesticide residues. However, our preliminary study demonstrated that the proposed detection device has excellent potential application for the safety inspection of food.