Laser-induced breakdown spectroscopy (LIBS) for the detection of exogenous contamination of metal elements in lily bulbs

Zhao, Qian; Yu, Yang; Cui, Pengdi; Hao, Nan; Liu, Chagnqing; Miao, Peiqi; Li, Zheng

doi:10.1016/j.saa.2022.122053

Cited by 11 publications

(5 citation statements)

References 41 publications

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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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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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show abstract

“…), calibration-free LIBS (CF-LIBS) [30,31], and multivariate analysis [32] have been used and have obtained improved results. Zhao et al [33] detected five metal elements in lily bulbs using partial least squares regression (PLSR) by combining various data preprocessing and selection methods to build the best-fitting model. In comparison, Su et al [34] adopted a framework that removed noise and low-intensity variables and then combined it with PLSR to simultaneously and quantitatively measure several toxic metals in Sargassum fusiforme.…”

Section: Introductionmentioning

confidence: 99%

Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Chen,

Li,

et al. 2024

Foods

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Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater–spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater–spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater–spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

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“…correlation coefficient (PCC), mutual information (MI), least absolute shrinkage and selection operator (LASSO) and random forest (RF). Li et al proposed a hybrid variable selection method based on MI to predict the Nemerow index of oily sludge, which consists of a preliminary selection and further screening processes [21]. Compared with multivariate analysis based on the full spectrum, it improved the determination coefficients of prediction ( ) from 0.956 to 0.968.…”

Section: Introductionmentioning

confidence: 99%

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

LYU 吕,

SONG 宋,

HOU 侯

et al. 2024

Plasma Sci. Technol.

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Nowadays, laser-induced breakdown spectroscopy (LIBS) has become a widely used atomic spectroscopic technique for rapid coal analysis. While the vast spectral information in LIBS contains signal uncertainty, which can impact its quantification performance. In this work, we proposed a hybrid variable selection method to improve the performance of LIBS quantification. Important variables are first identified using Pearson’s correlation coefficient (PCC), mutual information (MI), least absolute shrinkage and selection operator (LASSO) and random forest (RF), and then filtered and combined with empirical variables related to fingerprint elements of coal ash content. Subsequently, these variables are fed into partial least squares regression (PLSR). Additionally, in some models, certain variables unrelated to ash content were removed manually to study the variable deselection’s impact on model performance. The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method. It is significantly better than the variable selection only based on empirical knowledge and in most cases outperforms the baseline method. The results showed that on all three datasets, hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest RMSEP values of 1.605, 3.478 and 1.647, respectively, which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables, which are 1.959, 3.718 and 2.181, respectively. The EMP-LASSO-PLSR model with 20 selected variables exhibited a significant improving performance after variable deselection, with RMSEP values dropping from 1.635, 3.962, 1.647 to 1.483, 3.086, 1.567, respectively. Such results demonstrate that using empirical knowledge as a support to data-driven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

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Laser-induced breakdown spectroscopy (LIBS) for the detection of exogenous contamination of metal elements in lily bulbs

Cited by 11 publications

References 41 publications

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

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

Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

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