Data fusion of Laser-induced breakdown spectroscopy and Near-infrared spectroscopy to quantitatively detect heavy metals in lily

Zhao, Qian; Yu, Yang; Hao, Nan; Miao, Peiqi; Li, Xiaoli; Liu, Changqing; Li, Zheng

doi:10.1016/j.microc.2023.108670

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…The CARS-GAF-CNN model based on the RF voting mechanism, in which the R 2 and RMSE were 0.9934 and 0.0795 mg•kg −1 , exhibited the best performance in quantitatively analyzing the lead element. Compared with other detection methods of heavy metals in agricultural and sideline products based on spectroscopy and microwave technology, the method in this study improves the detection accuracy [13,38,39]. It can be found that decision-level fusion reduces the impact of weak sensors on the overall model performance by adjusting the weight of results obtained from different sources.…”

Section: Modeling and Analysis Of Decision-level Data Fusionmentioning

confidence: 94%

“…Data fusion techniques have been widely used in the quantitative analysis of multiple indicators [10][11][12]. Zhao, et al used near-infrared (NIR) and laser-induced breakdown spectroscopy (LIBS) to quantitatively analyze the heavy metals in lily [13]. The introduction of near-infrared spectroscopy makes up for the inability of LIBS to accurately quantify complex matrix samples.…”

Section: Introductionmentioning

confidence: 99%

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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: Modeling and Analysis Of Decision-level Data Fusionmentioning

confidence: 94%

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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show abstract

“…Laser-induced breakdown spectroscopy (LIBS) technology has gradually emerged in the technical field of heavy metal detection owing to its rapid detection and green advantages [49]. Q. Zhao et al [50] brought forth a new idea that constructed a heavy metal content prediction model using near-infrared (NIR) and LIBS spectral data, with simultaneous multi-element detection and prediction accuracy as high as 0.90. The coefficients of determination in the optimal prediction models for Zn, Cu, and Pb were 0.9858, 0.9811, and 0.9460, respectively, and the root mean square errors of prediction were 4.3047, 4.9592, and 8.3881 mg•kg −1 , respectively, which provided good reproducibility for the rapid detection of heavy metals in lilies.…”

Section: Common Fluorescence Spectroscopy Detection Methodsmentioning

confidence: 99%

Recent Progress on Fluorescent Probes in Heavy Metal Determinations for Food Safety: A Review

et al. 2023

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One of the main challenges faced in food safety is the accumulation of toxic heavy metals from environmental sources, which can sequentially endanger human health when they are consumed. It is invaluable to establish a practical assay for the determination of heavy metals for food safety. Among the current detection methods, technology based on fluorescent probes, with the advantages of sensitivity, convenience, accuracy, cost, and reliability, has recently shown pluralistic applications in the food industry, which is significant to ensure food safety. Hence, this review systematically presents the recent progress on novel fluorescent probes in determining heavy metals for food safety over the past five years, according to fluorophores and newly emerging sensing cores, which could contribute to broadening the prospects of fluorescent materials and establishing more practical assays for heavy metal determinations.

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“…Taking advantage of LIBS spectroscopy's capabilities in detecting metallic elements, Zhao et al. (2023) explored a prediction model for the heavy metal content of Lily by fusing LIBS and NIR data. They employed a PLSR model with MLDF and achieved excellent prediction results for Zn, Cu, and Pb with R 2 values of 0.9858, 0.9811, and 0.9460, respectively.…”

Section: Recent Advances In Spectral Data Fusion In Food Analysismentioning

confidence: 99%

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

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In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low‐level, mid‐level, and high‐level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra‐to‐spectra, spectra‐to‐machine vision, spectra‐to‐electronic nose, and spectra‐to‐nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real‐world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone‐based multi‐sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

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Data fusion of Laser-induced breakdown spectroscopy and Near-infrared spectroscopy to quantitatively detect heavy metals in lily

Cited by 11 publications

References 36 publications

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

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

Recent Progress on Fluorescent Probes in Heavy Metal Determinations for Food Safety: A Review

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

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