Aroma Recognition Research Articles

An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans.

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.

Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument

The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.

Recognition of Aromas from Tea Sources based on MQ3, MQ5, MQ7 Sensor Signal

This study investigated the capacity of a deep neural network to distinguish tea types based on their aromas. The data set of aromas from tea leaves, which contained sensor responses measured with a gas–sensing system using a mass– sensitive chemical sensors namelyMQ3, MQ5, MQ7, was used to evaluate the recognition accuracy. To define the input vectors of the deep neural network in aroma recognition experiments, frequency analysis using a continuous wavelet transform, with the Morlet function as the mother wavelet, was used to extract features from the sensor signals of the data set. The deep neural network achieved a recognition accuracy of 100% for the three tea types: oolong, jasmine and pu’erh, and the base gas of dehumidified indoor air. Comparing the recognition accuracy of the deep neural network to that obtained from other pattern recognition methods, such as naive Bayes and random forests, the experimental results demonstrated the effectiveness of applying a deep neural network to this task.

Remarks on Recognition of Aromas from Tea Sources Using MQ3, MQ5, MQ7 Sensor Signal

This study investigated the capacity of a deep neural network to distinguish tea sources based on their aromas. The data set of aromas from tea sources, which contained sensor responses measured with a gas–sensing system using a mass– sensitive chemical sensors namelyMQ3, MQ5, MQ7, was used to evaluate the recognition accuracy. To de?ne the input vectors of the deep neural network in aroma recognition experiments, frequency analysis using a continuous wavelet transform, with the Morlet function as the mother wavelet, was used to extract features from the sensor signals of the data set. The deep neural network achieved a recognition accuracy of 100% for the tea sources and the base gas of de humidified indoor air. Comparing the recognition accuracy of the deep neural network to that obtained from other pattern recognition methods, such as naive Bayes and random forests, the experimental results demonstrated the effectiveness of applying a deep neural network to this task.

From simple classification methods to machine learning for the binary discrimination of beers using electronic nose data

This paper deals with the development and the implementation of an electronic nose based on Metal Oxide Semiconductor (MOS) as an innovative instrument to beer aroma recognition. Principal component analysis (PCA) results revealed a good discrimination, as two clearly separated groups (alcoholic and non-alcoholic brands). Linear discriminant analysis (LDA) was performed on the important variables which were detected based on PCA loadings. The results showed that 100% accuracy for both training and test sets was obtained using those variables. Soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA) methods confirmed the PCA and LDA results of classification accuracy and their results were (100%, 100%) and (100%, 100%), for training and test sets, respectively. Finally, support vector machine (SVM) was considered and full accuracy (100%) for beer classification was again achieved for both training and test sets. The results showed that if simple methods perform well they may be preferred.

Identification and origin of the character‐impact compounds of raw oyster Crassostrea gigas

AbstractThe French consume large amounts of raw oysters. The study of the aroma of oyster Crassostrea gigas is of economic interest because it is a good method of checking the sensory quality. Aromas were extracted by vacuum steam distillation at 20 °C using whole oyster flesh. This extract presented similar sensory characteristics to raw oyster. The odour‐active compounds were characterised by gas chromatography coupled with olfactometry using a panel of 10 judges trained in seafood aroma recognition. Fifty‐nine volatile compounds were identified in oyster aroma extract. Among these, 25 were responsible for the overall odour of raw oyster. Four compounds identified in oysters were characterised by fresh and marine odour: 3‐(E)‐hexen‐1‐ol, decanal, 2‐undecanone and 3,6‐(E,Z)‐nonadien‐1‐ol. Some compounds were identified for the first time in oysters: 4‐(Z)‐heptenal (white boiled fish odour), which comes from n‐3 polyunsaturated fatty acid oxidation, and 3‐octanol (moss and sulphury odour), 2‐nonanol (cucumber odour) and octanoic acid, which arise from n‐6 polyunsaturated fatty acid oxidation.© 2002 Society of Chemical Industry