Characterization of Volatile Organic Compounds of Healthy and Huanglongbing-Infected Navel Orange and Pomelo Leaves by HS-GC-IMS

Cao, Shifeng; Sun, Jingyu; Yuan, Xiaotao; Deng, Weihui; Zhong, Balian; Chun, Jiong

doi:10.3390/molecules25184119

Cited by 18 publications

(11 citation statements)

References 32 publications

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“…Suppose that there are 6 QCs, the number of learner yards is 14, there are 900 learner jobs in total, learning times simulated experimentally are 12, 18, and 24, the speed v � 4 m/s, ω T : ω D � 1 : 1, and the number of yards for the purpose of learner jobs handled by QCs obeys a normal distribution N(μ i , σ 2 i ). If μ 4 � 9, it means that the learners handled by QC4 are stored near yard B9, and as σ 4 increases or decreases, the learners are stored in a larger or smaller range of B9, so different experimental scenarios can be simulated by changing the values of μ i and σ i [29].…”

Section: Experimental Scenesmentioning

confidence: 99%

Willingness and Evaluation Model of College Students’ Online Learning Behavior Based on Distributed Cognition

Chen

Huang

2021

Scientific Programming

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Guided by distributed cognition theory, we analyze the influential elements of content, tools, and contextual interactions in the online learning process through research and case studies to explore the characteristics and evaluation of college students’ willingness to engage in online learning behavior under distributed cognition and provide guidance for the experience design of online education platforms. Based on distributed cognition, this paper designs a convolutional neural network model based on InceptionNet, which uses a global average pooling layer instead of a fully connected layer to reduce the number of parameters, and InceptionNet increases the depth and width of the network by branching to improve the performance of the network and avoid overfitting. Distributed cognitive theory emphasizes the distributed nature of cognition, and the intrinsic variables that influence the willingness to participate in online learning communities from a systemic viewpoint are mainly attitudes, subjective norms, expected emotions, competence, sense of relatedness, desire, and perceived behavioral control. In addition, perceived behavioral control has a direct positive effect on the willingness to participate in online learning communities.

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Section: Experimental Scenesmentioning

confidence: 99%

Willingness and Evaluation Model of College Students’ Online Learning Behavior Based on Distributed Cognition

Chen

Huang

2021

Scientific Programming

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“…The presence and quantitative content of pathogenic microorganisms, viruses, and inflammation products in the sample considerably change the composition of the gases excreted from the biosample [ 68 , 69 , 70 , 71 ]. Consequently, the inclusion of parameters A i/j in the initial data matrix allows taking into account the presence or increase in the concentration of individual substances in the gas phase more comprehensively when constructing the classification model.…”

Section: Discussionmentioning

confidence: 99%

Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

Кучменко

Шуба

Умарханов

et al. 2021

Veterinary Sciences

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The paper demonstrates a new approach to identify healthy calves (“healthy”) and naturally occurring infectious bronchopneumonia (“sick”) calves by analysis of the gaseous phase over nasal secretions using 16 piezoelectric sensors in two portable devices. Samples of nasal secretions were obtained from 50 red-motley Holstein calves aged 14–42 days. Calves were subjected to rectal temperature measurements, clinical score according to the Wisconsin respiratory scoring chart, thoracic auscultation, and radiography (Carestream DR, New York, USA). Of the 50 calves, we included samples from 40 (20 “healthy” and 20 “sick”) in the training sample. The remaining ten calves (five “healthy” and five “sick”) were included in the test sample. It was possible to divide calves into “healthy” and “sick” groups according to the output data of the sensor arrays (maximum sensor signals and calculated parameters Ai/j) using the principal component linear discriminant analysis (PCA–LDA) with an accuracy of 100%. The adequacy of the PCA–LDA model was verified on a test sample. It was found that data of sensors with films of carbon nanotubes, zirconium nitrate, hydroxyapatite, methyl orange, bromocresol green, and Triton X-100 had the most significance for dividing samples into groups. The differences in the composition of the gaseous phase over the samples of nasal secretions for such a classification could be explained by the appearance or change in the concentrations of ketones, alcohols, organic carboxylic acids, aldehydes, amines, including cyclic amines or those with a branched hydrocarbon chain.

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“…HS-GC-IMS is a new technique developed in recent years for the detection of aromatic compounds, by which substances can be separated in two dimensions by GC and IMS drift tubes [ 30 , 31 , 32 , 33 , 34 ]. The method does not require complex sample pretreatment, and the sample can be directly injected after crushing, which has the advantages of environmental friendliness, high sensitivity and short analysis time [ 23 , 35 ]. HS-GC-IMS has a good application in the detection of flavor components in the food field, and has been increasingly widely used in the field of pharmaceutical research in recent years.…”

Section: Introductionmentioning

confidence: 99%

Development of the Volatile Fingerprint of Qu Aurantii Fructus by HS-GC-IMS

Cuifen

Xiao

et al. 2022

Molecules

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Volatile components are important active ingredients of Rutaceae. In this study, HS-GC-IMS (headspace-gas chromatography-ion mobility spectrometry) was used to study the volatile compounds of Qu Aurantii Fructus, and a total of 174 peaks were detected, 102 volatile organic compounds (131 peaks) were identified. To compare the volatile compounds of Qu Aurantii Fructus with its similar medical herb, Aurantii Fructus, and their common adulterants, principal component analysis (PCA) and cluster analysis (CA) were performed based on the signal intensity of all the detected peaks. The results showed that Qu Aurantii Fructus and Aurantii Fructus (Citrus aurantium L.) were clustered into one group, while their common adulterants could be well distinguished in a relatively independent space. In order to distinguish Qu Aurantii Fructus from Aurantii Fructus, the peaks other than the average intensity ±2 standard deviation (95% confidence interval) were taken as the characteristic components by using the Gallery Plot plug-in software. Additionally, the fingerprint method was established based on the characteristic compounds, which can be used to distinguish among Qu Aurantii Fructus, Aurantii Fructus and their common adulterants quickly and effectively. We found that the characteristic components with higher content of Qu Aurantii Fructus were nerol, decanal, coumarin and linalool. This study provides a novel method for rapid and effective identification of Qu Aurantii Fructus and a new dimension to recognize the relationship between Qu Aurantii Fructus and Aurantii Fructus.

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Characterization of Volatile Organic Compounds of Healthy and Huanglongbing-Infected Navel Orange and Pomelo Leaves by HS-GC-IMS

Cited by 18 publications

References 32 publications

Willingness and Evaluation Model of College Students’ Online Learning Behavior Based on Distributed Cognition

Willingness and Evaluation Model of College Students’ Online Learning Behavior Based on Distributed Cognition

Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

Development of the Volatile Fingerprint of Qu Aurantii Fructus by HS-GC-IMS

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