Savitzky-Golay Smoothing and Differentiation Filters for Damage Identification in Plates

Araújo dos Santos, J.V.; Lopes, H.

doi:10.1016/j.prostr.2024.01.121

Procedia Structural Integrity

2024

DOI: 10.1016/j.prostr.2024.01.121

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Savitzky-Golay Smoothing and Differentiation Filters for Damage Identification in Plates

J.V. Araújo dos Santos,

H. Lopes

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Cited by 2 publications

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Mapping the defect acceptance for dynamically loaded components by exploiting DIC-based full-field receptances

Zanarini

2024

Engineering Failure Analysis

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Mapping the defect acceptance for dynamically loaded components by exploiting DIC-based full-field receptances

Zanarini

2024

Engineering Failure Analysis

View full text Add to dashboard Cite

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy

Fan,

Liu,

Cui

et al. 2024

Foods

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Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940–1660 nm range. Various preprocessing techniques, including Savitzky−Golay (S−G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (Rp) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.

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Savitzky-Golay Smoothing and Differentiation Filters for Damage Identification in Plates

Cited by 2 publications

References 12 publications

Mapping the defect acceptance for dynamically loaded components by exploiting DIC-based full-field receptances

Mapping the defect acceptance for dynamically loaded components by exploiting DIC-based full-field receptances

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy

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