Laser-induced breakdown spectroscopy determination of toxic metals in fresh fish

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural networks in revealing adulteration of similarly looking fish fillets

Fish is an essential source of many nutrients necessary for human health. However, the deliberate mislabeling of similar fish fillet types is common in markets to make use of the relatively high price difference. This is a type of explicit food adulteration. In the present work, spectrochemical analysis and chemometric methods are adopted to disclose this type of fish species cheating. Laser-induced breakdown spectroscopy (LIBS) was utilized to differentiate between the fillets of the low-priced tilapia and the expensive Nile perch. Furthermore, the acquired spectroscopic data were analyzed statistically using principal component analysis (PCA) and artificial neural network (ANN) showing good discrimination in the PCA score plot and a 99% classification accuracy rate of the implemented ANN model. The recorded spectra of the two fish indicated that tilapia has a higher fat content than Nile perch, as evidenced by higher CN and C2 bands and an atomic line at 247.8 nm in its spectrum. The obtained results demonstrated the potential of using LIBS as a simple, fast, and cost-effective analytical technique, combined with statistical analysis for the decisive discrimination between fish fillet species.

Emission Spectrochemical Analysis of Soft Samples Including Raw Fish by Employing Laser-Induced Breakdown Spectroscopy with a Subtarget at Low-Pressure Helium Gas

Laser-induced breakdown spectroscopy (LIBS) to detect the light elements such as lithium (Li) and boron (B) and heavy elements such as copper (Cu) and lead (Pb) in raw fish samples is reported in this work. This is made possible by understanding that the soft target absorbs recoil energy and as a result, the ablated atoms gushing from the soft target do not acquire sufficient speed to form a shock wave. In order to overcome this problem, we set a subtarget on the back of the soft target so as to produce the repulsion force by which the gushing speed of the ablated atoms is increased, yielding a sufficiently high plasma temperature or sufficiently large thermal energy needed for the excitation of the ablated atoms. Excellent spectral qualities of various soft samples such as margarine, butter, peanut butter, strawberry jam, raw tuna, raw gindara, and raw salmon are presented. Furthermore, a linear calibration curve with a zero intercept is also obtained for Li, Cu, and Pb. The detection limit of Li, Cu, and Pb is found to be around 0.1 mg/L. This modification of LIBS for soft samples by using a subtarget effect clearly promises a rapid and in situ soft sample analysis since there is practically no sample digestion in the analysis.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.