ODRP: A Deep Learning Framework for Odor Descriptor Rating Prediction Using Electronic Nose

Guo, Juan; Cheng, Yu; Luo, Dehan; Wong, Kin-Yeung; Hung, Kevin; Li, Xin

doi:10.1109/jsen.2021.3074173

Cited by 34 publications

(19 citation statements)

References 31 publications

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“…The method of exploring the mass spectrum feature space using the gradient descent method is described below. The odor impression corresponding to the value in the mass spectrum feature space X (3) is expressed as the following equation:…”

Section: Mass Spectrum That Realizes Intended Odor Impressionmentioning

confidence: 99%

“…where L(X (9) ) is the weighted sum of squares error (WSE), η G,τ is the update rate, η 0 is the initial value, γ is the attenuation rate, and m i is a weight for the ith descriptor's score. By repeating the update (10), we obtain the optimal X (3) .…”

Section: Mass Spectrum That Realizes Intended Odor Impressionmentioning

confidence: 99%

“…As a result, it was found that the first principal component of physicochemical parameters and the first principal component of perception correlate the most, and it was concluded that the first principal component for perceptual data was pleasantness. Guo et al proposed a convolutional LSTM (Long Short Term Memory) that predicts sensory test data using sensing data obtained from an electronic nose (E-nose) [ 3 ]. E-nose is composed of multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture

2022

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Recently, olfactory information on odorants has been associated with their corresponding molecular features. Such information has been obtained by predicting the sensory test evaluation scores from the molecular structure parameters or the sensing data. On the other hand, we develop a method of the prediction of molecular features corresponding to the odor impression. We utilize a machine-learning-based odor predictive model introduced in our previous research, and we propose a mathematical model for exploring the sensing data space. By using mass spectrum as sensing data in the predictive model, we can represent predicted mass spectrum as those of an odor mixture, and the mixing ratio can be obtained. We show that the mass spectrum of apple flavor with enhanced ‘fruit’ and ‘sweet’ impressions can be obtained using 59 and 60 molecules respectively by using our analysis method.

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Section: Mass Spectrum That Realizes Intended Odor Impressionmentioning

confidence: 99%

Section: Mass Spectrum That Realizes Intended Odor Impressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture

2022

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“…Compared to the vanilla recurrent neural network (RNN) and gated recurrent unit (GRU), LSTM showed the lowest prediction error on all four different pollutants. Guo et al [98] proposed an E-Nose framework to predict odor descriptors using a CNN-LSTM model. E-nose data collected from 16 gas sensors were first sliced into small patches, among which each patch represented the same period.…”

Section: Quantitative Aroma Analysismentioning

confidence: 99%

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Liu

2021

Sensors

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

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“…The methods based on e-nose use electronic nose sensors to measure molecular odors, and to obtain high-dimensional odor data information as molecular feature information, the methods use machine learning or deep learning algorithms to perform odor prediction. Electronic nose technology is used in many aspects of life, such as food odor detection, industrial gas detection, disease detection, etc [6][7][8]. There are many studies on molecular odor impression prediction.…”

Section: Introductionmentioning

confidence: 99%

Odor-GCN: Graph Convolutional Network for Predicting Odor Impressions Based on Molecular Structures

Qiu

Cheng

Luo

et al. 2022

Preprint

Self Cite

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Predicting odor impression from molecular structure can help develop olfactory product such as perfumes, cosmetics and fragrances. At present, the methods of odor impression prediction can be divided into three categories: based on molecular physical and chemical characteristics, based on electronic nose, and based on mass spectrometry. In these methods, collecting odor data usually requires expensive equipment or time-consuming chemical and physical experiments. In order to overcome these difficulties, we proposed a molecular structure-based odor impression prediction method, which can be obtained in the existing chemical database (such as PubChem) without additional experiments. This greatly saves the time and cost of obtaining odor data. We believe that the olfactory information of odor is implicit in its molecular structure. First, molecules are represented as graphs, called molecule-graph, that is, atoms are regarded as the vertices of the graph, and covalent bonds are regarded as edges of the graph. Then a graph convolution network (GCN) was designed to extract olfactory information from molecule-graph. The GCN contains three con-volutional layers, an aggregation layer and a fully connected layer. In each graph convolution layer, vertices (atoms) can send message to the vertices (atoms) connected to it. The aggregation layer aggregates all the features of all vertices and calculates their average value. The fully connected layer outputs the odor impression value of the odor. Finally, we predicted the odor impressions (pleasantness , intensity and familiarity) for 476 odor single molecules. The best prediction of Pearson Correlation Coefficient for odor pleasantness impressions was r = 0.52 (p < 0.0000005), which is between the predicted and actual ratings, and the Mean Absolute Error was M AE = 8.53.

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ODRP: A Deep Learning Framework for Odor Descriptor Rating Prediction Using Electronic Nose

Cited by 34 publications

References 31 publications

Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture

Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Odor-GCN: Graph Convolutional Network for Predicting Odor Impressions Based on Molecular Structures

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