Electronic nose (E-nose) plays an important role in the detection of Chinese baijiu, which is an alcoholic beverage of high reputation. However, traditional e-nose data processing relies on manual selection of features with complicated preprocessing steps. The production process of baijiu with the same flavor is similar, but due to the limited number of sensors, it is not easy to quickly extract specific features with e-nose. To overcome this shortcoming, we propose a novel method based on Channel Attention Convolutional Neural Network (CA-CNN) for authenticity identification of Chinese baijiu. The underlying channel attention module analyzes the dependencies between channels and learning weights in order to improve the detection accuracy. In particular, we evaluated the comprehensive performance of the system by comparing it with traditional machine learning methods. The prediction accuracy of the CA-CNN (98.53%) is better than the back-propagation artificial neural network (BP-ANN) (90.83%), the support vector machine (SVM) (84.5%), and the random forest (RF) (86.867%). The experiment results show that the CA-CNN has reasonable reliability, stability and good prediction performance in the quality classification of Chinese baijiu. It provides an effective reference method for the standardization of baijiu quality inspection.

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Odor descriptors are words used to express human olfactory perception. At a certain level, predicting the odor descriptor rating using an electronic nose(E-nose) equips the machine with the ability to perceive odors. In this paper, we propose a novel deep learning framework for predicting odor descriptor rating via E-nose signals. The proposed framework has multiple sibling neural networks, including a Convolutional LSTM(ConvLSTM) layer and a regression layer. The ConvLSTM layers are used to learn the spatiotemporal features from sensor signal. On the one hand, it effectively extracts the temporal features of the signal, and on the other hand, it makes good use of the sensor dependencies. The output of the regression layer corresponds to a combination of descriptors or single descriptors, depending on their similarity. The experiment results show that: i) the framework utilizing the spatiotemporal correlation of the E-nose signal is more effective than using the temporal or spatial model alone, and that ii) the combination of descriptors based on descriptors similarity can effectively improve the prediction accuracy. On the DREAM dataset, our proposed framework outperforms the state-of-the- art methods in odor descriptor rating prediction.

Predicting odor’s pleasantness with electronic nose can simplify the evaluation process of odors, and it has potential applications in the perfumes and environmental monitoring industry. Classical algorithms for predicting odor’s pleasantness generally use a manual feature extractor and an independent classifier. The feature extractor is the key to developing accurate algorithms. However, its design requires expertise and experience. In order to circumvent this difficulty, we propose a model for predicting odor’s pleasantness by using convolutional neural network. It was found that our model, which uses convolutional neural layers, outperforms manual feature extractor. Experiment results showed that the correlation between our model and human was over 90% on pleasantness rating. Our model also achieved an accuracy of 99.9% in distinguishing between absolutely pleasant and unpleasant odors.

Background: Alpinia officinarum Hance is both an herbal medicine and a condiment, and generally has different cultivars such as Zhutou galangal and Fengwo galangal. The appearance of these A. officinarum cultivars is similar, but their chemical composition and quality are different. It is therefore important to discriminate between different A. officinarum plants to ensure the consistency of the efficacy of the medicine. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two cultivars for fast and robust discrimination. Methods: Odor and volatile components of all A. officinarum samples were detected by the E-nose and gas chromatography-mass spectrometry (GC-MS), respectively. The E-nose sensors and GC-MS data were analyzed respectively by principal component analysis (PCA), the correlation between E-nose sensors and GC-MS data were analyzed by partial least squares (PLS). Results: It was found that Zhutou galangal and Fengwo galangal can be discriminated by combining the E-nose with PCA, and the E-nose sensors S2, S6, S7, S9 were important sensors for distinguishing different cultivars of A. officinarum. A total of 56 volatile components of A. officinarum were identified by the GC-MS analysis, and the composition and content of the volatile components from the two different A. officinarum cultivars were different, in particular the relative contents of 1,8-cineole and α-farnesene. The classification result by PCA analysis based on GC-MS data was consistent with the E-nose results. The PLS analysis demonstrated that the volatile terpene, alcohol and ester components primarily interacted with the sensors S2 and S7, indicating that particular E-nose sensors were highly correlated with some aroma constituents. Conclusions: Combined with advanced chemometrics, the E-nose detection technology can discriminate two cultivars of A. officinarum, with GC-MS providing support to determine the material basis of the E-nose sensors’ response.

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.