Convenient and accurate method for the identification of Chinese teas by an electronic nose

Liu, H.; Li, Q.; Gu, Yu

doi:10.3920/qas2018.1331

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…Chemical methods such as gas chromatography–mass spectrometry (GC–MS) and high‐performance liquid chromatography (HPLC) can achieve convincing results, but chemical methods are expensive and not suitable for large‐scale use (Ding et al, 2015; Lin et al, 2013). In recent years, researchers have proposed some other tea quality detection and evaluation methods, such as the electronic tongue (Zhang et al, 2019), electronic nose (Liu et al, 2019), hyperspectral imaging (Yan et al, 2019), computer vision (Ren et al, 2021), and ultraviolet radiation spectroscopy (Huang et al, 2020). Although many new detection methods have emerged recently, NIR spectroscopy technology is still widely used in food detection due to its simplicity, rapidity, and nondestructiveness.…”

Section: Introductionmentioning

confidence: 99%

Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform

et al. 2022

J Food Process Engineering

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The extraction of near‐infrared (NIR) spectral discrimination information is important for the NIR spectral classification task. Some discriminant information extraction algorithms such as linear discriminant analysis (LDA), uncorrelated discriminant transform (UDT), and fuzzy uncorrelated discriminant transform (FUDT) use the sample mean to calculate the total scattering matrix. However, the calculation of the sample mean will be affected by abnormal samples, which will affect the extraction of discriminant information. To solve this problem, this article proposes an improved discriminant information extraction algorithm called weighted global fuzzy uncorrelated discriminant transform (WGFUDT). The algorithm uses the Euclidean distance between the training samples to weight the training samples and assigns a smaller weight to the abnormal data to reduce its effect on the sample mean. The algorithm was used for the grade identification of two green teas (Huangshan Maofeng tea and Mee tea). The results show that the classification accuracies of WGFUDT on Mee tea and Huangshan Maofeng tea are 97.22% and 99.07%, respectively. Compared with LDA, UDT and FUDT, WGFUDT can obtain more spectral discriminant information and has higher accuracy in grade identification of Huangshan Maofeng tea and Mee tea. Practical Applications Some traditional discriminant information extraction algorithms sometimes cannot extract enough discriminant information, which will affect the recognition rate of tea grades. WGFUDT can extract more discriminant information in the face of green tea spectral information and achieve higher accuracy. WGFUDT for green tea grade identification is fast and effective.

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Section: Introductionmentioning

confidence: 99%

Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform

et al. 2022

J Food Process Engineering

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“…Liu et al. [ 20 ] proposed a fast and effective random forest approach for distinguishing teas in six categories (white tea, green tea, black tea, yellow tea, dark tea, and oolong tea) with an E-nose and achieved an accuracy of 100%. Banerjee et al.…”

Section: Introductionmentioning

confidence: 99%

“…[ 22 ] utilized an E-nose and a random forest approach to distinguish seven Longjing tea samples with different prices obtained from different companies and achieved an accuracy of over 95%. Although the E-nose has been successfully applied for analyzing teas, most studies primarily focused on distinguishing teas in different categories [ 20 , 23 , 24 ] or teas in the same category from different production areas or with different qualities [ 21 , 22 , 25 ]. Few studies focused on the fine-grained classification of tea in different categories (categories) and from different production areas (sub-categories) simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

2021

Foods

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Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

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“…A typical manufacturing process of the Chinese liquor generally involves the primary fermentation, distillation, aging, and, if required, blending [9], resulting in different flavors and textures. Chinese liquor has five traditional flavors, such as strong, mixed, fen, moutai, and special [10]. It is difficult to distinguish the flavor types of the Chinese liquor, and even more difficult to identify the different Chinese liquors with the same flavor type.…”

Section: Introductionmentioning

confidence: 99%

“…A growing demand for excellent wines and Chinese liquors has stimulated fraudulant activities, such as the sales of poor quality products under false labels, which damages not only the interests of the consumers, but also the reputation of the producers [10]. This highlights the shortcomings in the wine and Chinese liquor industry, such as poor quality control and lack of rapid detection in the market.…”

Section: Introductionmentioning

confidence: 99%

A Model Transfer Learning Framework With Back-Propagation Neural Network for Wine and Chinese Liquor Detection by Electronic Nose

Yang

Liu

2020

IEEE Access

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Electronic nose, as a non-destructive instrument, is widely used in the field of gas analysis. In this work, E-nose was employed to distinguish wines and Chinese liquors by means of a machine learning technique. First, a multi-hidden layers Back-Propagation Neural Network (BPNN) was designed to build an identification model for the classification of different wines. Then, a BPNN-based transfer-learning framework was developed with minimal changes to the architecture of the BPNN-based model which was trained on the wine sample dataset. Experimental results revealed that the BPNN-based model performed with a 98.27% accuracy in identifying different wines, and the BPNN-based transfer-learning framework performed with a 93.4% accuracy in identifying Chinese liquors by only retraining the output layer. This reduced the model training costs compared with the complete retraining of a new classification model. Results demonstrated the effectiveness of the proposed BPNN-based transfer-learning model, which was capable of identifying different kinds of wines based on their own properties and could be easily applied to the classification of Chinese liquors. The model-based transfer learning framework offered promising potential for different classification tasks of various beverage.

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Convenient and accurate method for the identification of Chinese teas by an electronic nose

Cited by 7 publications

References 31 publications

Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform

Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

A Model Transfer Learning Framework With Back-Propagation Neural Network for Wine and Chinese Liquor Detection by Electronic Nose

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