Abstract: Provided is an electronic nose instrument based on gas sensitivity and gas chromatography and an online analysis method of multiple state parameters of fermentation and malodorous pollutant processes. The main constituent units of the instrument include a gas sensor array module, a capillary gas chromatographic column module, an automatic gas sampling module, and a computer control and analysis module. A single gas sampling period is T0=300-600s. Not only are two flow rates and two accumulative volumes of gas sampling unequal to each other, but also two starting time points are not synchronized to each other, between the gas sensor array module and the gas chromatography module. 3 pieces of sensitive information, i.e.

Abstract: Provided is a method for multi-information fusion of gas sensitivity and chromatography and on-site detection and analysis of flavor substances using an electronic nose instrument. The electronic nose instrument includes a gas sensor array module (I), a capillary gas chromatographic column module (II), an automatic headspace sampling module (III), a computer control and data analysis module (IV), an automatic lifter (V) for headspace sampling, a large-volume headspace vapor generation device (VI) and two auxiliary gas sources (VII-1, VII-2). The electronic nose instrument detects a large number of odorous samples to establish a big odor data. On this basis, the normalization fusion preprocessing is done, and the cascade machine learning model realizes both an on-site recognition of many foods, condiments, fragrances and flavors, and petroleum waxes and a real-time quantitative prediction of their odor quality grades and many key component concentrations.

Abstract: Provided is an online centralized monitoring and analysis system using an electronic nose instrument for multi-point malodorous gases, and the system includes an electronic nose instrument, which connects with multiple monitoring points through pipes. On-site malodorous gases in the maximum range of 2.5 km are drawn into the electronic nose instrument within 1 min by the external vacuum pump, and forced to flow through an annular working chamber of a gas sensor array for 30 s by the internal vacuum pump periodically. The modular convolution neural networks online learn the recent time-series responses of the gas sensor array and predict their coming responses, and the modular deep neural networks offline set up the relationship between the responses and multiple concentration items according to odor big data.

Abstract: Provided is a sensory quality evaluation method for tobacco and tobacco products using an electronic nose instrument. The electronic nose instrument includes a gas sensor array module, an automatic smoke sampling system, a computer control and data analysis system, and an automatic ignition device. These components are integrated in a test box to make the instrument be structure miniaturization and work automation. The large tobacco data is established, in which the relationship between the responses of gas sensor array and the brand labels and sensory quality index scores by testing a large number of standard cigarette samples. A cascade type of modular neural networks is proposed with revised activation function and new decision-making and quantification rules to simulate the smoking and evaluating process of the professional panel. The electronic nose instrument and method realize on-site detection, discrimination and quality score estimation of a large number of tobacco and tobacco products.

Abstract: Provided is an online centralized monitoring and analysis system based on an electronic nose instrument for multi-point malodorous gases, and the system includes an electronic nose instrument, which connects with multiple monitoring points through pipes. On-site malodorous gases in the maximum range of 2.5 km are drawn into odor electronic nose instrument within 1.0 min by the external vacuum pump, and forced to flow through the annular gas sensor array for 30 s by the internal vacuum pump periodically. The modular convolution neural networks online learn the recent time-series responses of the gas sensor array and predict their coming responses, and the modular deep neural networks offline set up the relationship between the responses and multiple concentration items according to odor big data.

Abstract: Provided is a sensory quality evaluation method for tobacco and tobacco products using an electronic nose instrument. The electronic nose instrument includes a gas sensor array module, an automatic smoke sampling system, a computer control and data analysis system, and an automatic ignition device. These components are integrated in a test box to make the instrument be structure miniaturization and work automation. The large tobacco data is established, in which the relationship between the responses of gas sensor array and the brand labels and sensory quality index scores by testing a large number of standard cigarette samples. A cascade type of modular neural networks is proposed with revised activation function and new decision-making and quantification rules to simulate the smoking and evaluating process of the professional panel. The electronic nose instrument and method realize on-site detection, discrimination and quality score estimation of a large number of tobacco and tobacco products.