Correction to: Application of a Fourier Transform Infrared (FTIR) Principal Component Regression (PCR) Chemometric Method for the Quantification of Respirable Crystalline Silica (Quartz), Kaolinite, and Coal in Coal Mine Dusts from Australia, UK, and South Africa

Stacey, Peter; Clegg, Francis; Rhyder, Gary; Sammon, C

doi:10.1093/annweh/wxac027

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“…In the 1960s, Herman Wold introduced PCR, which transforms the original independent variables into a new set of principal components for model construction, effectively addressing the shortcomings of traditional PCA. Stacey et al [9] employed PCR and Fourier-transform infrared spectroscopy to measure the properties of respirable quartz, kaolinite, and coal in various mineral samples from different countries. Nonetheless, PCR considers only the variance of the explanatory variables without factoring in their relationships with the response variables.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Near-Infrared Spectroscopy Using the BEST-1DConvNet Model

Li,

Deng

2024

Processes

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In the quest for enhanced precision in near-infrared spectroscopy (NIRS), in this study, the application of a novel BEST-1DConvNet model for quantitative analysis is investigated against conventional support vector machine (SVM) approaches with preprocessing such as multiplicative scatter correction (MSC) and standard normal variate (SNV). We assessed the performance of these methods on NIRS datasets of diesel, gasoline, and milk using a Fourier Transform Near-Infrared (FT-NIR) spectrometer having a wavelength range of 900–1700 nm for diesel and gasoline and 4000–10,000 nm for milk, ensuring comprehensive spectral capture. The BEST-1DConvNet’s effectiveness in chemometric predictions was quantitatively gauged by improvements in the coefficient of determination (R2) and reductions in the root mean square error (RMSE). The BEST-1DConvNet model achieved significant performance enhancements compared to the MSC + SNV + 1D + SVM model. Notably, the R2 value for diesel increased by approximately 48.85% despite a marginal RMSE decrease of 0.92%. R2 increased by 11.30% with a 3.32% RMSE reduction for gasoline, and it increased by 8.71%, accompanied by a 3.51% RMSE decrease for milk. In conclusion, the BEST-1DConvNet model demonstrates superior predictive accuracy and reliability in NIRS data analysis, marking a substantial leap forward in spectral analysis technology. This advancement could potentially streamline their integration into various industrial applications and highlight the role of convolutional neural networks in future chemometric methodologies.

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Section: Introductionmentioning

confidence: 99%