Data fusion strategy based on ultraviolet–visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water

Xu, Zhuopin; Li, Xiaohong; Cheng, Weimin; Zhao, Gongpu; Tang, Liwen; Yang, Yang; Wu, Yuejin; Zhang, Pengfei; Wang, Qi

doi:10.1016/j.saa.2023.123007

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…Three decision-level fusion strategies based on binary linear regression, entropy weight method and trend line slope weight method were adopted, which achieved better results compared with full-spectrum and feature-level fusion strategies. Based on the fusion of UV-Vis and NIR spectral data, Xu, et al proposed an alternative approach for simultaneous detection of chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) detection in surface water [16]. With the introduced data fusion strategy, the RMSEP of the three parameters can reach 6.95, 0.195 and 0.466, respectively, which is decreased by 2.96%, 11.3% and 4.23% compared with single-spectroscopic-based models.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Quantitative Determination of Low-Concentration Preservatives and Heavy Metals in Tricholoma Matsutakes Based on SERS and FLU Spectral Data Fusion

Jin,

Li,

Huang

et al. 2023

Foods

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As an ingredient of great economic value, Tricholoma matsutake has received widespread attention. However, heavy metal residues and preservatives in it will affect the quality of Tricholoma matsutake and endanger the health of consumers. Here, we present a method for the simultaneous detection of low concentrations of potassium sorbate and lead in Tricholoma matsutakes based on surface-enhanced Raman spectroscopy (SERS) and fluorescence (FLU) spectroscopy to test the safety of consumption. Data fusion strategies combined with multiple machine learning methods, including partial least-squares regression (PLSR), deep forest (DF) and convolutional neural networks (CNN) are used for model training. The results show that combined with reasonable band selection, the CNN prediction model based on decision-level fusion achieves the best performance, the correlation coefficients (R2) were increased to 0.9963 and 0.9934, and the root mean square errors (RMSE) were reduced to 0.0712 g·kg−1 and 0.0795 mg·kg−1, respectively. The method proposed in this paper accurately predicts preservatives and heavy metals remaining in Tricholoma matsutake and provides a reference for other food safety testing.

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Section: Introductionmentioning

confidence: 99%