Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy

Li, Maogang; Xu, Yanyan; Men, Jing; Yan, Chun‐Hua; Tang, Hongsheng; Zhang, Tianlong; Li, Hua

doi:10.1016/j.saa.2021.119430

Cited by 18 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DTs, and consequently RDFs, do not mix input channels. Thus the impact of each channel on the nal classication result A state-of-the-art technique for variable selection in RDF cross-validation is variable importance measurement (VIM) 35 coupled with a variable reduction method (V-WSP) 36 applied to the RDF. To select variables from the variable importance of RDF thresholds are required.…”

Section: Single Channel Selectionmentioning

confidence: 99%

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: Single Channel Selectionmentioning

confidence: 99%

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In this study, Raman spectroscopy was used in the analysis of WPI-HA conjugates with protein:polysaccharide ratios of 10:1, 20:1, and 50:1. Two ensemble methods based on decision trees, namely, random forest (RF) [ 38 , 39 ] and gradient boosting (GB) [ 40 ], were implemented to process the data obtained. Feature importance calculated from the regression and classification models was used to determine the Raman bands that changed the most between samples.…”

Section: Introductionmentioning

confidence: 99%

Combination of Machine Learning and Raman Spectroscopy for Determination of the Complex of Whey Protein Isolate with Hyaluronic Acid

Mayorova,

Saveleva,

Bratashov

et al. 2024

Polymers

View full text Add to dashboard Cite

Macromolecules and their complexes remain interesting topics in various fields, such as targeted drug delivery and tissue regeneration. The complex chemical structure of such substances can be studied with a combination of Raman spectroscopy and machine learning. The complex of whey protein isolate (WPI) and hyaluronic acid (HA) is beneficial in terms of drug delivery. It provides HA properties with the stability obtained from WPI. However, differences between WPI-HA and WPI solutions can be difficult to detect by Raman spectroscopy. Especially when the low HA (0.1, 0.25, 0.5% w/v) and the constant WPI (5% w/v) concentrations are used. Before applying the machine learning techniques, all the collected data were divided into training and test sets in a ratio of 3:1. The performances of two ensemble methods, random forest (RF) and gradient boosting (GB), were evaluated on the Raman data, depending on the type of problem (regression or classification). The impact of noise reduction using principal component analysis (PCA) on the performance of the two machine learning methods was assessed. This procedure allowed us to reduce the number of features while retaining 95% of the explained variance in the data. Another application of these machine learning methods was to identify the WPI Raman bands that changed the most with the addition of HA. Both the RF and GB could provide feature importance data that could be plotted in conjunction with the actual Raman spectra of the samples. The results show that the addition of HA to WPI led to changes mainly around 1003 cm−1 (correspond to ring breath of phenylalanine) and 1400 cm−1, as demonstrated by the regression and classification models. For selected Raman bands, where the feature importance was greater than 1%, a direct evaluation of the effect of the amount of HA on the Raman intensities was performed but was found not to be informative. Thus, applying the RF or GB estimators to the Raman data with feature importance evaluation could detect and highlight small differences in the spectra of substances that arose from changes in the chemical structure; using PCA to filter out noise in the Raman data could improve the performance of both the RF and GB. The demonstrated results will make it possible to analyze changes in chemical bonds during various processes, for example, conjugation, to study complex mixtures of substances, even with small additions of the components of interest.

show abstract

“…Since some absorption characteristics of molecules in near-infrared spectroscopy are overlapped by other alcohols, spectral signals are obscured in methanol gasoline [16,22]. Raman spectroscopy can avoid these issues, making it particularly suitable for methanol gasoline qualitative or quantitative analysis, respectively [16,22,27].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol–Gasoline Blends by Raman Spectroscopy

Lu,

Wu,

Liu

et al. 2024

Molecules

View full text Add to dashboard Cite

Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

show abstract

Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy

Cited by 18 publications

References 31 publications

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images

Combination of Machine Learning and Raman Spectroscopy for Determination of the Complex of Whey Protein Isolate with Hyaluronic Acid

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol–Gasoline Blends by Raman Spectroscopy

Contact Info

Product

Resources

About