Commercial Instant Coffee Classification Using an Electronic Nose in Tandem with the ComDim-LDA Approach

Makimori, Gustavo Yasuo Figueiredo; Bona, Evandro

doi:10.1007/s12161-019-01443-5

Cited by 29 publications

(12 citation statements)

References 58 publications

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“…A study was carried out in [160] for the quality assessment of seven different instant coffee types from Brazil and England. The coffee samples were collected via PEN 2 EN equipped with ten metal-oxide sensors.…”

Section: Coffee and Coffee Quality 100%mentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

266

152

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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.

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Section: Coffee and Coffee Quality 100%mentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

266

152

View full text Add to dashboard Cite

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“…The data analysis methods used were PCA based on the average value (PCA-ave), LDA based on the average value (LDA-ave), PCA based on the maximum variance moment (PCA-var) and LDA based on the maximum variance moment (LDA-var). Similar methods are also described in [30].…”

Section: Classification Of Green Tea Varietiesmentioning

confidence: 99%

“…At present, there are various modes for extracting features from original sensor signals, such as the maximum value [23], average value [24], integral value [25], differential value [26], maximum energy [27] and wavelet packet decomposition [28]. Different pattern recognition technologies are introduced to make recognition decisions, including multi-layer perceptron (MLP) [29], LDA [30], principal component analysis (PCA) [31], support vector machine (SVM) [32] and artificial neural network (ANN) [33]. These methods have achieved excellent results in specific application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

et al. 2021

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The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

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“…E-nose produces a specific response when a sample of aroma compound is exposed on the sensor array headspace. These responses are then converted into a specific pattern that can be recognized by pattern recognition methods such as principal component analysis (PCA) [17], [20], [28], common dimension analysis (ComDim) [24], linear discriminant analysis (LDA) [24] and multiple discriminant analysis (MDA) [29] or other pattern recognition methods.…”

Section: Introductionmentioning

confidence: 99%

Freshness assessment of tilapia fish in traditional market based on an electronic nose

et al. 2021

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This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.

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Commercial Instant Coffee Classification Using an Electronic Nose in Tandem with the ComDim-LDA Approach

Cited by 29 publications

References 58 publications

Electronic Nose and Its Applications: A Survey

Electronic Nose and Its Applications: A Survey

Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

Freshness assessment of tilapia fish in traditional market based on an electronic nose

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