Application of Temperature Modulation-SDP on MOS Gas Sensors: Capturing Soil Gaseous Profile for Discrimination of Soil under Different Nutrient Addition

Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages.

Section: Introductionmentioning

confidence: 99%

A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose

Voss

Júnior

Farinelli

et al. 2019

“…This powerful tool has assisted many fields in food analysis [ 7 , 8 ], such as beer quality inspection [ 9 , 10 ], quality level identification of tea [ 11 ], characterization of juices [ 12 ], and differentiation of aromatic flowers [ 13 ]. Additionally, e-nose can be applied to the monitoring of air quality [ 14 , 15 , 16 , 17 , 18 , 19 ] of gases emitted by the soil [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

Zhang

Sun

et al. 2020

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

“…This technique has been widely used on foods, medicines, beverages, and the environment [33,34,35,36,37,38]. Although there are some reports on artificial olfactory techniques in soil quality assessment [39,40,41,42,43,44], such as soil moisture distinction, soil microbial activity measurement, soil contamination detection, soil type identification, etc., to our knowledge, there are no papers on detecting soil organic matter by using AOS.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System

Zhu

Chen

et al. 2019

Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.