Gas Chromatography Data Classification Based on  Complex Coefficients of an Autoregressive Model

Zhao, Wenwei; Morgan, Joshua T.; Davis, Cristina E.

doi:10.1155/2008/262501

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…The Autoregressive Integrated Moving Average Model (ARIMA) is a widely known model for detecting the presence of serial correlation providing a detailed description of the stochastic process. By means of the Ljung-Box, we tested the overall randomness to verify the absence of autocorrelation (Garey et al, 2008;Zhao et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Chemometric analysis of the secondary metabolite profile of Yarrow (Achillea collina Becker ex Rchb.) affected by phloem feeding Myzus persicae Sulzer aphids

Giorgi

Madeo

Baumgärtner

et al. 2010

J Entomol Acarol Res

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-Yarrow (Achillea collina Becker ex Rchb.) has a high content of secondary metabolites including phenolic acids. Among them, hydroxycinnamic acid such as chlorogenic acid and its derivatives were found to be the most abundant ones. The phloem feeding Myzus persicae Sulzer was hypothesized to affect the contents of secondary metabolites and change the metabolite profile. A high-performance liquid chromatography technique (HPLC) was used to evaluate whether there is a difference in the phenolic profile between aphid infested and non-infested yarrow leaves. M. persicae colonies composed of between 20 and 30 individuals were allowed to feed for 10 and 20 days. Preprocessing was carried out to standardize the procedures in order to obtain optimal separation of analytes, good chromatographic peak shape and robustness of the results. The methanol extracts of leaves were analyzed by means of HPLC, and the time series of peak areas obtained from each extract were evaluated through chemometric analyses. Results of the phenolic fingerprints showed a specific chromatographic profile with 58 peaks. An autoregression analysis demonstrated the absence of correlation. The discriminant analysis carried out with the data satisfying the assumption of the absence of collinearity showed a significant effect of phloem feeding on soluble phenolic compounds and identified two peaks that separate aphid infested from non-infested plants. The hydroxycinnamic acids widely found in A. collina leaves were not affected by M. persicae feeding. The results are the basis for the current studies aiming at the identification of chemical compounds that correspond to the peaks.Riassunto -Analisi chemometrica del profilo dei metaboliti secondari di Achillea collina Becker ex Rchb. infestata da afidi appartenenti alla specie Myzus persicae Sulzer Achillea collina Becker ex Rchb. è una pianta medicinale caratterizzata da un alto contenuto in metaboliti secondari, in particolare in acidi fenolici. Tra questi, gli acidi idrossicinnamici quali il clorogenico e suoi derivati sono risultati essere i più abbondanti. Variazioni qualitative e quantitative di tali metaboliti sono state osservate in seguito ad attacchi entomatici. Si ipotizza che Myzus persicae Sulzer, afide che si nutre di sostanze floematiche, possa modificare il contenuto degli acidi fenolici della pianta. E' stata impiegata la tecnica di cromatografia liquida ad alta Journal of Entomological and Acarological Research, Ser. II, 42 (2), 2010 92 prestazione (HPLC) per valutare se vi sono differenze nel profilo degli acidi fenolici in estratti metanolici di foglie di A. collina non infestate e infestate con M. persicae. Ad una colonia di afidi costituita da 20-30 individui è stato permesso di nutrirsi sulle piante per 10 o 20 giorni. Una procedura di messa a punto del metodo analitico ("preprocessing") è stata effettuata per standardizzare l'analisi, così da ottenere una ottimale separazione e risoluzione degli analiti, garantendo la robustezza dei risultati. Le sequenze delle aree d...

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Section: Discussionmentioning

confidence: 99%

Chemometric analysis of the secondary metabolite profile of Yarrow (Achillea collina Becker ex Rchb.) affected by phloem feeding Myzus persicae Sulzer aphids

Giorgi

Madeo

Baumgärtner

et al. 2010

J Entomol Acarol Res

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“…AR modeling can permit us to directly work on time shifted chromatograph data without using time alignment as a preprocessing step. This advantage has recently been proven in a GC/MS based sensor design for bacterial identification [41]. …”

Section: Feature Extraction and Dimension Reductionmentioning

confidence: 99%

“…In the RBF-PLSR approach, all the training samples are used as radial basis vectors and then PLSR is applied for the linear regression between the hidden and output layers. This method statistically solves the construction of radial basis vectors and has been used for mass spectrometry calibration [74] and GC/MS based bacterial classification [41]. …”

Section: System Modelingmentioning

confidence: 99%

Machine Learning: A Crucial Tool for Sensor Design

et al. 2008

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Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

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“…Kisi [3] used the AR model to predict stream flow. Zhao, Morgan, and Davis [4] used the AR model to classify the output from gas chromatography. Lee and Chon [5] used the AR model to model the extraction of respiratory rate.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian Estimation for Stationary Autoregressive Models Using Reversible Jump MCMC Algorithm

Man¹,

Rusiman²

2018

IJET

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The autoregressive model is a mathematical model that is often used to model data in different areas of life. If the autoregressive model is matched against the data then the order and coefficients of the autoregressive model are unknown. This paper aims to estimate the order and coefficients of an autoregressive model based on data. The hierarchical Bayesian approach is used to estimate the order and coefficients of the autoregressive model. In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. The posterior distribution has a complex shape so that the Bayesian estimator is not analytically determined. The reversible jump Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain the Bayesian estimator. The performance of the algorithm is tested by using simulated data. The test results show that the algorithm can estimate the order and coefficients of the autoregressive model very well. Research can be further developed by comparing with other existing methods.

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Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model

Cited by 9 publications

References 24 publications

Chemometric analysis of the secondary metabolite profile of Yarrow (Achillea collina Becker ex Rchb.) affected by phloem feeding Myzus persicae Sulzer aphids

Chemometric analysis of the secondary metabolite profile of Yarrow (Achillea collina Becker ex Rchb.) affected by phloem feeding Myzus persicae Sulzer aphids

Machine Learning: A Crucial Tool for Sensor Design

Hierarchical Bayesian Estimation for Stationary Autoregressive Models Using Reversible Jump MCMC Algorithm

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