A Transfer Learning Method for the Protection of Geographical Indication in China Using an Electronic Nose for the Identification of Xihu Longjing Tea

Wang, Xiaoran; Gu, Yu; Liu, Huixiang

doi:10.1109/jsen.2020.3048534

Cited by 26 publications

(8 citation statements)

References 33 publications

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“…When Ts equaled 312 and 416, the low accuracies indicated a significan difference between the newly added samples and the target domain samples. The curv of recognition accuracy was similar to that in the literature [37]. When the Ts was set t 520, the recognition accuracy was the highest.…”

Section: Optimizing the Parameters Of The Tradaboost Methodssupporting

confidence: 84%

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Chen

Wang

Yang

et al. 2023

Sensors

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Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region’s samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.

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Section: Optimizing the Parameters Of The Tradaboost Methodssupporting

confidence: 84%

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Chen

Wang

Yang

et al. 2023

Sensors

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show abstract

“…The second step is repeated until the node can no longer be split without pruning. Finally, the generated decision trees are formed into a random forest that is used to classify or regress the new data [ 36 , 37 ]. Compared to other machine learning methods, RF has various advantages, including low complexity, fast computing speed, lower overfitting, etc.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Huang

2022

Foods

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Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.

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“…Although gas sensor drift compensation was addressed by many recent works with machine learning methods such as ensemble learning and domain adaption learning, it remains a significant obstacle for E-Nose technology. Additionally, the performance of feature selection and models depends on the E-Nose system setup and target gas [119][120][121], which might be another challenge to overcome.…”

Section: Discussionmentioning

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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A Transfer Learning Method for the Protection of Geographical Indication in China Using an Electronic Nose for the Identification of Xihu Longjing Tea

Cited by 26 publications

References 33 publications

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

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

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