Chemometrics advances on the challenges of the gas chromatography–mass spectrometry metabolomics data: a review

Kanginejad, Atefeh; Mani-Varnosfaderani, Ahmad

doi:10.1007/s13738-018-1461-5

Cited by 14 publications

(10 citation statements)

References 97 publications

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“…We selected three representative classical algorithms for comparison with our DPA method, i.e. wavelet [20], EMD [18] and airPLS [8]. These three algorithms were acknowledged as the most commonly used methods in processing electronic and spectroscopic signals.…”

Section: Resultsmentioning

confidence: 99%

“…There is a long history of developing numeric algorithms for processing the mass, fluorescent, or infrared spectroscopies. Shirley backgrounds [7], airPLS [8], AIMA [9], and Orthogonal Basis [10] are classic techniques playing important roles in different applications and subjects. The unified variation model [11], LMV-RSA [12] and the techniques based on neural networks [13], singular analysis [14], optimization method [15] and computational geometry [16] can also achieve improved effectiveness or efficiency, and some are able to perform joint baseline-correction and denoising.…”

Section: Introductionmentioning

confidence: 99%

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A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

Liu

Lin

2019

BMC Bioinformatics

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BackgroundAutomatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences.ResultsWe developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted.ConclusionsWe have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

Liu

Lin

2019

BMC Bioinformatics

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“…This definition evidences clearly the utilization of chemometrics in all stages of the chemical measurement process, from definition of optimal experimental conditions, data collection, and processing of data. Chemometrics has its roots in analytical chemistry [6], but it is totally interdisciplinary and has been applied in many different areas [7], such as food sciences [8,9,10,11,12], assessment of adulteration, geographical origin [13,14,15], metabolomics [16,17,18], engineering [19,20], forensics [21,22,23,24,25], pharmaceutical studies [26,27,28,29,30], cultural studies [31,32,33], environmental chemistry [34], etc. Chemometric tools are fundamental to solve real life problems [35].…”

Section: Chemometricsmentioning

confidence: 99%

Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs

Alvarenga

Carneiro

2019

Molecules

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Chemometrics is the chemistry field responsible for planning and extracting the maximum of information of experiments from chemical data using mathematical tools (linear algebra, statistics, and so on). Active pharmaceutical ingredients (APIs) can form impurities when exposed to excipients or environmental variables such as light, high temperatures, acidic or basic conditions, humidity, and oxidative environment. By considering that these impurities can affect the safety and efficacy of the drug product, it is necessary to know how these impurities are yielded and to establish the pathway of their formation. In this context, forced degradation studies of pharmaceutical drugs have been used for the characterization of physicochemical stability of APIs. These studies are also essential in the validation of analytical methodologies, in order to prove the selectivity of methods for the API and its impurities and to create strategies to avoid the formation of degradation products. This review aims to demonstrate how forced degradation studies have been actually performed and the applications of chemometric tools in related studies. Some papers are going to be discussed to exemplify the chemometric applications in forced degradation studies.

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“…Nowadays, headspace gas chromatography and gas chromatography-mass spectrometry are mainly applied to assess the acetone in transformer oil domestically and overseas. While their sensitivity and reliability have been reported previously, they suffer from a complicated operation, high cost, and long detection time [ 24 , 25 , 26 , 27 , 28 , 29 ]. Consequently, it is increasingly necessary to find a simple, rapid, and inexpensive method for the detection of acetone in transformer oil.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Zhang

Lei

Song

et al. 2022

IJMS

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Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

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Chemometrics advances on the challenges of the gas chromatography–mass spectrometry metabolomics data: a review

Cited by 14 publications

References 97 publications

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

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