Identification of Flax Oil by Linear Multivariate Spectral Analys

Ходасевич, М. А.; Borisevich, D. A.

doi:10.1007/s10812-020-00929-z

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…The papers surveyed cover a range of application domains including food and agriculture (30 papers) [7][8][9][12][13][14][16][17][18][20][21][22][24][25][26]28,[34][35][36][37]39,41,44,45,47,48,54], biomedical (15 papers) [10,11,15,19,24,25,30,32,38,40,42,[51][52][53]56], industrial (3 papers) [29,43,49], and forensics (3 paper) [23,31,55].…”

Section: Methodsmentioning

confidence: 99%

“…A priori knowledge [8,9,15,16,24,26,38,41,42,45,47,52,53,55,56] 1 5 Visual spectra evaluation [13,[20][21][22]27,33,37,48,54] 9 Quantitative selection techniques [10,12,31,41,43,48] 6 Full spectrum used [7,8,11,14,[17][18][19]23,25,[28][29][30]32,[34][35][36]39,40,44,46,[48][49]…”

Section: Feature Selection Approach Instancesmentioning

confidence: 99%

“…This visualization aspect of PCA was also used in the evaluation in the results of the cluster analysis where clusters were visualized and compared to known class labels to evaluate the level of success in the clustering. [31,32,41] 3 Outlier removal [45,46] 2 19 of the papers used PCA for its general dimension reducing capabilities. Applying PCA can dramatically reduce the number of dimensions in IR or NIR spectroscopy data while still retaining a high percentage of the information.…”

Section: Principal Component Analysis (Pca) Usagementioning

confidence: 99%

See 2 more Smart Citations

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Crase¹,

Hall

Thennadil³

2021

Computers, Materials &Amp; Continua

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Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn't reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size. The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data. With this in mind, we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Selection Approach Instancesmentioning

confidence: 99%

Section: Principal Component Analysis (Pca) Usagementioning

confidence: 99%

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Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Crase¹,

Hall

Thennadil³

2021

Computers, Materials &Amp; Continua

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“…By combining chemometric algorithms, a rapid detection model can be established between the spectra and target properties, thus enabling the qualitative or quantitative analysis of samples. − NIR spectroscopy is highly efficient, rapid, and nondestructive and can simultaneously determine multiple components. It has been widely applied in various industries, such as chemical engineering, agriculture, and medicine. − In recent years, researchers have applied NIR spectroscopy to the characterization and quality detection of jet fuels. By utilizing NIR spectroscopy in conjunction with chemometrics, multivariate regression models have been constructed to rapidly detect the physicochemical properties of jet fuels. − Xiao et al developed a rapid analysis model for the total acid value of jet fuel based on NIR spectroscopy using a backpropagation neural network (BP-ANN) and partial least-squares (PLS) regression methods.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Li,

Zhang,

Zhang

et al. 2024

ACS Omega

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Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky−Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (R c 2 ) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (R p 2 ) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9−3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

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“…Earlier [7], we carried out a multivariate calibration of the concentration of unrefined sunflower oil, considered as adulteration, in a mixture with flaxseed oil using a model based on the principal component analysis (PCA) [8], cluster analysis and projection to latent structures (PLS) [9] of absorbance spectra in UV, visible and near IR ranges. To further reduce the root mean square error of prediction (RMSE P ), in this work we compared three methods for spectral variables selection: the Successive Projections Algorithm (SPA) [10], the searching combination moving window interval PLS (scmwiPLS) and the method using correlation coefficients ranging [11].…”

Section: Introductionmentioning

confidence: 99%

Variables Selection in the Ultraviolet, Visible and Near Infrared Range for Calibration of a Mixture of Vegetable Oils by Absorbance Spectra

Ходасевич

Borisevich

2021

Prib. metody izmer.

Self Cite

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The aim of the work was a multivariate calibration of the concentration of unrefined sunflower oil, considered as adulteration, in a mixture with flaxseed oil. The relevance of the study is due to the need to develop a simple and effective method for detecting the falsification of flaxseed oil which is superior in the content of essential polyunsaturated fatty acids to olive oil. A few works only are devoted to identifying adulteration of flaxseed oil, unlike olive oil.Multivariate calibration carried out using a model based on the principal component analysis, cluster analysis and projection to latent structures of absorbance spectra in UV, visible and near IR ranges. Calibration uses three methods for spectral variables selection: the successive projections algorithm, the method of searching combination moving window, and method for ranking variables by correlation coefficient.The application of the successive projections algorithm, ranking variables by correlation coefficient and searching combination moving window makes it possible to reduce the value of the root mean square error of prediction from 0.63 % for wideband projection to latent structures to 0.46 %, 0.50 %, and 0.03 %, respectively.The developed method of multivariate calibration by projection to latent structures of absorbance spectra in UV, visible and near IR ranges using the spectral variables selection by searching combination moving window is a simple and effective method of detecting adulteration of flaxseed oil.

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Identification of Flax Oil by Linear Multivariate Spectral Analys

Cited by 4 publications

References 13 publications

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

Variables Selection in the Ultraviolet, Visible and Near Infrared Range for Calibration of a Mixture of Vegetable Oils by Absorbance Spectra

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