Clusterization of Pure And Formalin Fresh Noodles with Electronic Nose Based on Kernel Principal Component Analysis

Lelono, Danang; Abdillah, Muhammad Z.; Widodo, Triyogatama Wahyu; Apandi, Mohamad

doi:10.1109/icst47872.2019.9166268

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…ܺ ‫)ݐ(‬ → ܺ (2) With ܺ ‫)ݐ(‬ representing the sensor signal of the i-th sensor sample j that depend on time, its features are then extracted into ܺ , which represents the feature of the i-th sensor for sample j that is independent of time [8]. The sensor responses generated by a total of n sensors for sample j can be arranged into a time-independent matrix, as shown in Equation 3.…”

Section: Feature Extractionmentioning

confidence: 99%

Performance of feature extraction method combination in arabica coffee roasting classification

Cynthiarani,

Lelono,

Putri

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Feature extraction is vital in electronic nose technology, particularly for classification tasks. However, challenges like noise, temperature variations, humidity, drift, and unwanted aromas can introduce inconsistencies in feature extraction, diminishing the machine’s classification capabilities. This study aimed to assess the electronic nose’s performance in recognizing aroma patterns of Arabica coffee at four roasting levels. It involved comparing 63 feature extraction method combinations derived from six primary methods (mean, skewness, kurtosis, standard deviation, maximum, minimum). After extracting features, Linear Discriminant Analysis (LDA) was used for dimensionality reduction and analysis. Subsequently, a Support Vector Machine (SVM) model was trained and validated using Stratified K-Fold Cross Validation to comprehend feature patterns and labels and determine hyperplanes for distinct classes. The results revealed that the best feature combination to classify arabica coffee aromas at various roasting levels was achieved by using a combination of mean, kurtosis, and standard deviation, with an accuracy of 86.11%, precision of 86.73%, recall of 86.11%, and MCC of 0.8159 in a training time of 0.0574 seconds. Utilizing LDA improved accuracy by 9.81% and MCC by 15.01%.

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Section: Feature Extractionmentioning

confidence: 99%

Performance of feature extraction method combination in arabica coffee roasting classification

Cynthiarani,

Lelono,

Putri

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…The GUI functions as a display program and hardware controller in measuring samples, the data displayed is the response of the gas sensor, the response of the temperature and humidity sensors. Sample measurement consisted of one sniffing cycle, namely flushing, collecting, and purging as shown in Figure 2, this process has also been cried out in Bulletin of Electr Eng & Inf ISSN: 2302-9285  Implementation of an electronic nose for classification of synthetic flavors (Radi) 1285 [21], [22]. Flushing is the process of reading the sensor response without a test sample.…”

Section: Softwarementioning

confidence: 99%

Implementation of an electronic nose for classification of synthetic flavors

et al. 2021

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Classification and identification of synthetic flavor become routine activities in the flavor and food industry due to its application. As a modern olfactory technology, electronic nose (e-nose) has the possibility to be applied in these activities. This study aimed to evaluate an e-nose for classifying synthetic flavors. In this study, an e-nose was designed with an array of gases sensors as the main sensing component and principal component analysis (PCA) for the pattern recognition software. This research was started with preparation of the hardware, continued with preparation of sample, data collection, and analysis. There were nine samples of synthetic flavors with different aroma, namely: grapes, strawberry, mocha, pandanus, mango, jackfruit, orange, melon, and durian. The data collection process includes three stages, i.e. flushing, collecting, and purging of 2 min, 3 min, 2 min respectively. These sensor responses were then analyzed for forming aroma patterns. Four pre-treatment methods were applied for the aroma pattern formation: absolute data, normalize of absolute data, relative data, and normalize of relative data. With the PCA for evaluation, the results showed that the absolute data treatment provided the best results, indicated from the distribution of aroma patterns that were grouped according to the type of samples.

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“…In addition, electronic nose has good detection performance on formalin. For example, in [27], formalin fresh noodles with electronic nose based on kernel principal component analysis was introduced. In [28], formalin on fresh tilapia via electronic nose and assessment of toxicity levels with reference to average adult Filipino weight was proposed.…”

Section: The Related Applicationsmentioning

confidence: 99%

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

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Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.

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Clusterization of Pure And Formalin Fresh Noodles with Electronic Nose Based on Kernel Principal Component Analysis

Cited by 6 publications

References 11 publications

Performance of feature extraction method combination in arabica coffee roasting classification

Performance of feature extraction method combination in arabica coffee roasting classification

Implementation of an electronic nose for classification of synthetic flavors

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

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