Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

The quality grades of organic green teas are tightly correlated with their prices. In this work, samples of organic green teas of different quality grades are collected, and their aromas are analyzed with an electronic nose (E-nose). A multi-task model based on the back propagation neural network (MBPNN) is proposed for the simultaneous performance of the classification task (grade classification of tea) and regression task (quality prediction of tea with market price). The validity of the proposed MBPNN model is also verified; its performances of the tasks are compared with those of two classification models (random forest and support vector machine) and three regression models (partial least squares regression, kernel ridge regression, and support vector regression). Experimental results demonstrate that the MBPNN model achieves good performance both in the tasks of tea grade classification and tea quality evaluation (price regression). The study shows that the E-nose is effective for the classification and evaluation of organic green teas when an optimal pattern recognition algorithm is selected. Encouragingly, a novel application of the multi-task learning model in the tea industry is obtained to assess the tea quality in a simple, quick, and comprehensive way. INDEX TERMS Classification model, electronic nose, multi-task framework, organic green teas, regression model.

The electronic nose (E-nose) is a bionic olfactory system and a powerful tool in many fields. Sample classification and parameter prediction are the core functions of the E-nose. We present two algorithms for simultaneous recognition of four properties (wine region, grape variety, vintage, and fermentation processes) based on a back-propagation neural network (BPNN) and convolutional neural network (CNN), respectively, where the tasks (i.e., identification of the four properties) share underlying features. These algorithms exploited synergy among tasks to enhance their individual performance. Experimental results show that the model based on BPNN achieved the best performance with accuracies of 94.5%, 83.7%, 75.1%, and 76.9% in identifying wine region, grape, vintage, and fermentation processes, respectively. Furthermore, the results reveal that the models can capture global and local information and perform better than single-task models. INDEX TERMS Back-propagation neural network, convolutional neural network, electronic nose, multi-task learning, wine detection.