Geoherbalism evaluation of Radix Angelica sinensis based on electronic nose

Zheng, Shu-Jian; Ren, Wei-Guang; Huang, Linfang

doi:10.1016/j.jpba.2014.10.033

Cited by 40 publications

(29 citation statements)

References 9 publications

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“…Many foods and herbs are often classified into several commodity grades in market transactions, and different grade ones are considered to be of diverse quality. These classifications are mainly dependent on some sensory indicators, including color , smell , flavor , size , and weight . Although this method is simple and easy to implement, it is still relatively primitive.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the chemical markers for experiential quality evaluation of crude aconite by UHPLC–Q‐TOF‐MS

Zhang

Han

et al. 2016

J of Separation Science

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Many foods and herbs are experientially classified into different commodity grades in commercial circulation. Regarding the hypertoxic herb aconite, large samples are considered to be of better quality. However, this experiential classification lacks a scientific basis. In this study, we focused on the quality diversity among different grades and studied it using the minimum lethal dose assay and a novel ultra high performance liquid chromatography coupled with time-of-flight mass spectrometry method. Toxicity assay result suggested grade I aconite had the lowest toxicity (p < 0.05). Using this method with partial least squares-discriminant analysis, we discovered nine chemomarkers, including neoline, songorine, fuziline, mesaconitine, talatizidine, dexyaconitine, talatisamine, hypaconitine, and fuzitine. Considering their toxicity and activity, we found the levels of toxic ingredients hypaconitine, dexyaconitine, and mesaconitine in grade I were lower than those in grade II (p < 0.01), while the levels of efficacy ingredients songorine, talatisamine, and neoline were the highest in grade I (p < 0.01). Further study demonstrated that the quality variation was associated with plant tissue development and toxic ingredient distribution law. Our results provide scientific evidence for the experiential quality evaluation of aconite, and it will be of great utility for other foods and herbs.

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Section: Introductionmentioning

confidence: 99%

Investigation of the chemical markers for experiential quality evaluation of crude aconite by UHPLC–Q‐TOF‐MS

Zhang

Han

et al. 2016

J of Separation Science

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“…Electronic nose (EN) has been rapidly developed as a novel gas detection technology in the past two decades. It is widely implicated in toxic/harmful gas detection [1], food safety [2], environmental monitoring [3], medical industry [4], and other fields [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

Sun

Luo

et al. 2018

Journal of Electrical and Computer Engineering

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Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO 2 , CH 4 , NH 3 , and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.

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“…In the past, we only processed labeled data by feature extraction methods [17,18], however, in actual experiments, the numbers of collected unlabeled samples are often far greater than that of the labeled samples, and they are easier to obtain while the cost of getting the labeled samples is usually higher than for unlabeled ones. On the other hand, in the sampling experiments, there can be unexpected mistakes such as the paper label identifying the target gas which is pasted on the gas bag is lost, the label information is not written down because of a fault of operators which will lead to the loss of the sample label, which all causes a certain amount of waste of the number of experimental samples.…”

Section: Introductionmentioning

confidence: 99%

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Jia

Huang

Duan

et al. 2016

Sensors

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When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

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Geoherbalism evaluation of Radix Angelica sinensis based on electronic nose

Cited by 40 publications

References 9 publications

Investigation of the chemical markers for experiential quality evaluation of crude aconite by UHPLC–Q‐TOF‐MS

Investigation of the chemical markers for experiential quality evaluation of crude aconite by UHPLC–Q‐TOF‐MS

Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

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