Abstract:In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdown spectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration em… Show more
“…As shown in Figure 3 , there is no self-absorption or interruption of other emissions in the three Cd emission lines. So, the spectral intensities of the three Cd emission lines were used as input variables for univariate analysis, respectively, and these three lines were also discussed in the report of Yao et al [ 16 ].…”
Section: Resultsmentioning
confidence: 99%
“…Recently, there have also been some reports focusing on heavy metal detection in plants. Yao et al successively mixed cabbages with lead [ 15 ] and cadmium [ 16 ], then used LIBS to detect the heavy metals. The high determination coefficient and low root mean square error (RMSE) of the detection models indicate it is feasible to use LIBS to determine heavy metals in leafy vegetables.…”
Quick access to cadmium (Cd) contamination in lettuce is important to supervise the leafy vegetable growth environment and market. This study aims to apply laser-induced breakdown spectroscopy (LIBS) technology for fast determination of Cd content and diagnosis of the Cd contamination degree in lettuce. Emission lines Cd II 214.44 nm, Cd II 226.50 nm, and Cd I 228.80 nm were selected to establish the univariate analysis model. Multivariate analysis including partial least squares (PLS) regression, was used to establish Cd content calibration models, and PLS model based on 22 variables selected by genetic algorithm (GA) obtained the best performance with correlation coefficient in the prediction set Rp2 = 0.9716, limit of detection (LOD) = 1.7 mg/kg. K-Nearest Neighbors (KNN) and random forest (RF) were used to analyze Cd contamination degree, and RF model obtained the correct classification rate of 100% in prediction set. The preliminary results indicate LIBS coupled with chemometrics could be used as a fast, efficient and low-cost method to assess Cd contamination in the vegetable industry.
“…As shown in Figure 3 , there is no self-absorption or interruption of other emissions in the three Cd emission lines. So, the spectral intensities of the three Cd emission lines were used as input variables for univariate analysis, respectively, and these three lines were also discussed in the report of Yao et al [ 16 ].…”
Section: Resultsmentioning
confidence: 99%
“…Recently, there have also been some reports focusing on heavy metal detection in plants. Yao et al successively mixed cabbages with lead [ 15 ] and cadmium [ 16 ], then used LIBS to detect the heavy metals. The high determination coefficient and low root mean square error (RMSE) of the detection models indicate it is feasible to use LIBS to determine heavy metals in leafy vegetables.…”
Quick access to cadmium (Cd) contamination in lettuce is important to supervise the leafy vegetable growth environment and market. This study aims to apply laser-induced breakdown spectroscopy (LIBS) technology for fast determination of Cd content and diagnosis of the Cd contamination degree in lettuce. Emission lines Cd II 214.44 nm, Cd II 226.50 nm, and Cd I 228.80 nm were selected to establish the univariate analysis model. Multivariate analysis including partial least squares (PLS) regression, was used to establish Cd content calibration models, and PLS model based on 22 variables selected by genetic algorithm (GA) obtained the best performance with correlation coefficient in the prediction set Rp2 = 0.9716, limit of detection (LOD) = 1.7 mg/kg. K-Nearest Neighbors (KNN) and random forest (RF) were used to analyze Cd contamination degree, and RF model obtained the correct classification rate of 100% in prediction set. The preliminary results indicate LIBS coupled with chemometrics could be used as a fast, efficient and low-cost method to assess Cd contamination in the vegetable industry.
“…The LoD for Cd on lettuce leaf surfaces was 1.6 ng/g. Comparing with the detection ability of ordinary LIBS (mg/kg level 43 ), the results described above led us to conclude that adding metal nanoparticles to the material being analysed greatly improves the sensitivity of LIBS.Figure 5The quantitative curves of Cd concentrations. ( a ) The spectra of Cd at 214.4 nm; ( b ) The fitting curve of Cd element at 214.4 nm.…”
Residues of harmful chemicals in fruit and vegetables pose risks to human health. Ordinary laser-induced breakdown spectroscopy (LIBS) techniques are unsatisfactory for detecting harmful chemicals in fruit and vegetables. In this study, we applied metal nanoparticles to fruit and vegetables samples to improve the ability of LIBS to detect trace pesticide and heavy metal residues in the samples. The nanoparticle-enhanced LIBS technique gave pesticide residue detection limits for fruit and vegetables two orders of magnitude lower than achieved using standard LIBS and heavy metal detection limits markedly better than achieved using standard LIBS. We used the nanoparticle-enhanced LIBS technique to study the distributions of harmful chemicals in vegetable leaves. We found that heavy metals are distributed unevenly in edible plant leaves, the heavy metal concentrations being higher in the veins than in the mesophyll.
“…For example, the univariate model R 2 obtained using a single Cd line was 0.2316 for quantitative analysis of Cd in fresh vegetables. The band at 211.03-229.57 nm was extracted to increase the R 2 of calibration set to 0.98 and the R 2 of validation set to 0.99 with the partial least squares model [94]. When predicting the melamine contents in milk samples, researchers used an artificial neural network to increase the R 2 obtained from a univariate algorithm from 0.982 to 0.999.…”
Laser-induced breakdown spectroscopy (LIBS) is fast, on-line, causes little sample damage, and can be applied in remote field locations. In recent years, LIBS has been widely used in many fields of scientific research for element detection. Further application of LIBS is limited by the strong matrix effect, poor repeatability, and relatively weak detection sensitivity. The detection sensitivity is an important factor and needs to be improved for LIBS detection of minor or trace elements in samples. A variety of methods have been developed to improve detection sensitivity of LIBS. In this invited review paper, we discuss improvements in the LIBS detection sensitivity achieved with physical enhancement methods, chemical enhancement methods, mathematical methods, and combinations of multiple methods. We discuss the enhancement mechanisms, sensitivity improvements, configurations, and effects of key factors for various methods. The advantages, disadvantages, and real-time capabilities of these methods are reviewed. Finally, new trends and future perspectives for LIBS as an efficient analytical tool are discussed.
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