High-sensitivity determination of cadmium and lead in rice using laser-induced breakdown spectroscopy.

Stability and sensitivity of toxic elements determination is still unsatisfactory in agricultural product using laser-induced breakdown spectroscopy (LIBS). A simple and low cost sample pretreatment method named solid-liquid-solid transformation method was proposed in this work. The target analytes of cadmium (Cd) and lead (Pb) from rice samples were prepared through ultrasound assisted extraction in hydrochloric acid solution. The solution was dropped on the glass slide after centrifuging process and was further dried on a heater. Finally, the glass slide contained the analytes was carried out for LIBS determination. Compare with conventional pellet method, the spectral intensity of Cd and Pb element were enhanced significantly using LIBS. The limits of detection were 2.8 and 43.7 µg/kg, respectively. The limits of quantification were 9.3 and 145.7 µg/kg, respectively. The results demonstrated that LIBS coupled with ultrasound assisted extraction should be a promising tool to detect toxic elements in rice.

Classification accuracy improvement of laser-induced breakdown spectroscopy based on histogram of oriented gradients features of spectral images.

To improve the classification accuracy of laser-induced breakdown spectroscopy (LIBS), image histogram of oriented gradients (HOG) features method (IHFM) for materials analysis was proposed in this work. 24 rice (Oryza sativa L.) samples were carried out to verify the proposed method. The results showed that the classification accuracy of rice samples by the full-spectra intensities method (FSIM) and IHFM were 60.25% and 81.00% respectively. The classification accuracy was obviously improved by 20.75%. Universality test results showed that this method also achieved good results in the plastics, steel, rock and minerals classification. This study provides an effective method to improve the classification performance of LIBS.

Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Determination of antimony in soil using laser-induced breakdown spectroscopy assisted with laser-induced fluorescence.

Antimony (Sb) in soil is attracting attention in the research community due to its potential toxicity and carcinogenicity. Traditional methods of detecting Sb lack the ability of rapid and nondigestion analysis, which hinders their development and application. Moreover, it is still a challenge for laser-induced breakdown spectroscopy (LIBS) to detect Sb in soil due to the weak intensities and intense interference of spectral lines. Here, LIBS, assisted with laser-induced fluorescence (LIBS-LIF), was used to selectively enhance the Sb's characteristic spectral lines under optimal parameters. The quantitative analysis performance was notably improved with a determination coefficient (R2) of 0.991, the limit of detection of 0.221 µg/g, and root mean square error of cross validation of 3.592 µg/g. These results demonstrate that LIBS-LIF has the potential to realize the rapid and accurate analysis of Sb in soil.

Determination of potassium in ceramic raw materials using laser-induced breakdown spectroscopy combined with profile fitting.

The determination of potassium (K) content in ceramic raw materials provides important references for ceramic sintering. To realize rapid, in situ, and real-time analysis detection, a laser-induced breakdown spectroscopy (LIBS) system was set up to analyze K content in ceramic raw materials. However, the self-absorption was the serious influence on the accuracy of K element analysis. In this work, a method of profile fitting with Lorentz function was proposed to reduce the self-absorption effect in LIBS. After Lorentz fitting, the determination coefficient (R2 factor) for K element improved from 0.993 to 0.998, the root mean square error of cross-validation reduced from 0.458 wt. % to 0.145 wt. %, and the average relative error reduced from 13.769% to 5.121%. The results indicate that the Lorentz fitting can effectively reduce the self-absorption effect, and improve the accuracy of quantitative analysis for K element. According to the results, the proposed approach can be a promising method for determination of elements that suffer from self-absorption in LIBS.

Analytical-performance improvement of laser-induced breakdown spectroscopy for the processing degree of wheat flour using a continuous wavelet transform.

Quality and safety of food are two of the most important matters in our lives. Wheat is one of the most important products in the modern agricultural processing industry. Issues of mislabeling and adulteration are of increasingly serious concern in the grain market. They threaten the credibility of producers and traders and the rights of the consumers. Therefore, it is very significant to guarantee the processing degree of wheat flour. In this work, two different spectral peak recognition methods, i.e., artificial spectral peak recognition and automatic spectral peak recognition, are carried out to study the adulteration problem in the food industry. Three grades of the processing degree of wheat flour from northern China are classified by laser-induced breakdown spectroscopy (LIBS). To search for an automatic classification model, continuous wavelet transform is used for the automatic recognition of the LIBS spectrum peak. Principal component analysis is used to reduce the collinearity of LIBS spectra data. First, 20 principal components were selected to represent the spectral data for the following discrimination analysis by a support vector machine. The results showed that the classification accuracies of automatic spectral peak recognition are better than those of artificial spectral peak recognition. The classification accuracies of artificial spectral peak recognition and automatic spectral peak recognition are 95.33% and 98.67%; the fivefold cross-validation classification accuracies are 94.67% and 96.67%; and the operation times were 240 min and 2 min, respectively. It can be concluded that LIBS can provide simpler and faster classification without the use of any chemical reagent, which represents a decisive advantage for applications dedicated to rapidly detecting the processing degree of wheat flour and other cereals.

Multielemental self-absorption reduction in laser-induced breakdown spectroscopy by using microwave-assisted excitation.

The self-absorption effect seriously affects the accuracy of determination in laser-induced breakdown spectroscopy (LIBS). In this work, we proposed to reduce multielemental self-absorption within a wide spectral range (200-900 nm) by using microwave-assisted excitation in LIBS (MAE-LIBS). Self-absorption reduction of sodium (Na), potassium (K), aluminum (Al), silicon (Si), and calcium (Ca) in potassium feldspar using MAE-LIBS was investigated. The mechanisms of self-absorption reduction in MAE-LIBS were also investigated. The results show that the serious self-absorption of spectral lines (Na and K) was reduced. The full widths at half maximum (FWHMs) of Na I 589.0 nm, Na I 589.6 nm, K I 766.5 nm, and K I 769.9 nm in potassium feldspar were reduced by 43%, 43%, 53%, and 47%, respectively. MAE-LIBS also has a little FWHM reduction for spectral lines with weak self-absorption. The results demonstrate that MAE-LIBS can simultaneously reduce multielemental self-absorption.

Spatially selective excitation in laser-induced breakdown spectroscopy combined with laser-induced fluorescence.

Spatially selective excitation was proposed to improve excitation efficiency in laser-induced breakdown spectroscopy combined with laser-induced fluorescence (LIBS-LIF). Taking chromium (Cr) and nickel (Ni) elements in steels as examples, it was discovered that the optimal excitation locations were the center of the plasmas for the matrix of the iron (Fe) element but the periphery for Cr and Ni elements. By focusing an excitation laser at the optimal locations, not only excitation efficiency but also the analytical accuracy and sensitivity of quantitative LIBS-LIF were better than those with excitation at the plasma center in conventional LIBS-LIF. This study provides an effective way to improve LIBS-LIF analytical performance.

Multiphoton electron extraction spectroscopy and its comparison with other spectroscopies for direct detection of solids under ambient conditions.

Multiphoton electron extraction spectroscopy (MEES) is an analytical method for direct analysis of solids under ambient conditions in which the samples are irradiated by short UV laser pulses and the photocharges emitted are recorded as a function of the laser wavelength. The method is very sensitive, and many peaks are observed at wavelengths that are in resonance with the surface molecules. The analytical capabilities of MEES have recently been demonstrated, and here we perform a systematic comparison with some traditional spectroscopies that are commonly applied to material analysis. These include absorption, reflection, excitation and emission fluorescence, Raman, Fourier transform IR, and Fourier transform near-IR spectroscopies. The comparison is conducted for powders and for thin films of compounds that are active in all spectroscopies tested. Besides the obvious spectral parameters (signal-to-noise ratio, peak density, and resulting limits of detection), we introduce two additional variables-the spectral quality and the spectral quality density-that represent our intuitive perception of the analytical value of a spectrum. It is shown that by most parameters MEES is a superior analytical tool to the other methods tested for both sample morphologies.

Detection and mapping of trace explosives on surfaces under ambient conditions using multiphoton electron extraction spectroscopy (MEES).

Multiphoton electron extraction spectroscopy (MEES) is an analytical method in which UV laser pulses are utilized for extracting electrons from solid surfaces in multiphoton processes under ambient conditions. Counting the emitted electrons as a function of laser wavelength results in detailed spectral features, which can be used for material identification. The method has been applied to detection of trace explosives on a variety of surfaces. Detection was possible on dusty swabs spiked with explosives and also in the standard dry-transfer contamination procedure. Plastic explosives could also be detected. The analytical limits of detection (LODs) are in the sub pmole range, which indicates that MEES is one of the most sensitive detection methods for solid surface under ambient conditions. Scanning the surface with the laser allows for its imaging, such that explosives (as well as other materials) can be located. The imaging mode is also useful in forensic applications, such as detection of explosives in human fingerprints.

Fluoridized iron phosphate as a novel adsorbent for selective separation/isolation of cytochrome c.

Fluoridized iron phosphate (F-FePO) is prepared via a hydrothermal protocol and characterized by means of (57)Fe Mössbauer spectra, Fourier transform infrared, and surface charge analysis. For the first time, F-FePO is used as an adsorbent for the adsorption of proteins, which exhibits favorable selectivity toward cytochrome c (cyt-c) in the presence of acidic and neutral proteins under controlled experimental conditions. At pH 10.5, an adsorption efficiency of 100% is achieved for 60 mg L(-1) cyt-c in 1.0 mL of sample solution using 6.0 mg F-FePO. The adsorption behavior is consistent with the Langmuir adsorption model, corresponding to a theoretical adsorption capacity of 37.59 µg mg(-1). The retained cyt-c on F-FePO could be readily collected by 0.1 mol L(-1) Na(2)CO(3)-NaHCO(3) buffer (pH 10.5), deriving a recovery of 100%. Circular dichroism spectra indicate no conformational change for cyt-c after the adsorption and desorption processes, demonstrating the favorable biocompatibility of the fluoridized iron phosphate. F-FePO is employed for the selective isolation of cyt-c from a spiked human whole blood, achieving satisfactory results by assay with SDS-polyacrylamide gel electrophoresis and native polyacrylamide gel electrophoresis.