LIBS as a novel tool for the determination of the imidization degree of polyimides.

Due to their outstanding chemical and physical properties, polyimides are widely used in industrial applications. The degree of imidization of polyimides significantly influences their properties, making it an important factor in tailoring the material for specific applications. Imidization refers to the process of converting a precursor poly(amic acid) by removing water, and it is essential to analyze this process in detail to tune the final structure and properties of the material. Conventional techniques for this task include Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), or differential scanning calorimetry (DSC), but they lack the possibility of spatially and/or depth-resolved analysis or do not enable in-line monitoring capabilities. To overcome these limitations, we propose laser-induced breakdown spectroscopy (LIBS) as a powerful tool for the monitoring of the imidization reaction. To establish a measurement method, a total of 130 in-house prepared, self-synthesized samples were thermally cured to exhibit varying imidization degrees. IR spectroscopy served as a reference technique during method development, and a novel formula for calculating the degree of imidization, based on the C2 and H signal trends, was introduced. The calculated imidization degrees of model thin films based on LIBS were in good accordance with the IR reference method although minor differences between the two methods were expected due to varying information depth and the size of the sampled area. Additionally, the robustness of the procedure was demonstrated by depth profiling of a stacked model polymer, spiking with commercially available additives and, ultimately, by analyzing industry-relevant polymer samples.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

Assessment of the bony resection margin distance in bone-invasive oral cancer using laser-induced breakdown spectroscopy.

OBJECTIVES: Inadequate resection margins of less than 5 mm impair local tumor control. This weak point in oncological safety is exacerbated in bone-infiltrating tumors because rapid bone analysis procedures do not exist. This study aims to assess the bony resection margin status of bone-invasive oral cancer using laser-induced breakdown spectroscopy (LIBS). MATERIALS AND METHODS: LIBS experiments were performed on natively lasered, tumor-infiltrated mandibular cross-sections from 10 patients. In total, 5,336 spectra were recorded at defined distances from the tumor border. Resection margins < 1 mm were defined as very close, from 1-5 mm as close, and > 5 mm as clear. The spectra were histologically validated. Based on the LIBS spectra, the discriminatory power of potassium (K) and soluble calcium (Ca) between bone-infiltrating tumor tissue and very close, close, and clear resection margins was determined. RESULTS: LIBS-derived electrolyte emission values of K and soluble Ca as well as histological parameters for bone neogenesis/fibrosis and lymphocyte/macrophage infiltrates differ significantly between bone-infiltrating tumor tissue spectra and healthy bone spectra from very close, close, and clear resection margins (p < 0.0001). Using LIBS, the transition from very close resection margins to bone-infiltrating tumor tissue can be determined with a sensitivity of 95.0%, and the transition from clear to close resection margins can be determined with a sensitivity of 85.3%. CONCLUSIONS: LIBS can reliably determine the boundary of bone-infiltrating tumors and might provide an orientation for determining a clear resection margin. CLINICAL RELEVANCE: LIBS could facilitate intraoperative decision-making and avoid inadequate resection margins in bone-invasive oral cancer.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Quantitative Analysis of Pb in Soil Using Laser-Induced Breakdown Spectroscopy Based on Signal Enhancement of Conductive Materials.

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning.

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

Recent advances and applications of laser-based imaging techniques in food crops and products: a critical review.

To meet the growing demand for food quality and safety, there is a pressing need for fast and visible techniques to monitor the food crop and product production processing, and to understand the chemical changes that occur during these processes. Herein, the fundamental principles, instruments, and characteristics of three major laser-based imaging techniques (LBITs), namely, laser-induced breakdown spectroscopy, Raman spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry, are introduced. Additionally, the advances, challenges, and prospects for the application of LBITs in food crops and products are discussed. In recent years, LBITs have played a crucial role in mapping primary metabolites, secondary metabolites, nanoparticles, toxic metals, and mineral elements in food crops, as well as visualizing food adulteration, composition changes, pesticide residue, microbial contamination, and elements in food products. However, LBITs are still facing challenges in achieving accurate and sensitive quantification of compositions due to the complex sample matrix and minimal laser sampling quantity. Thus, further research is required to develop comprehensive data processing strategies and signal enhancement methods. With the continued development of imaging methods and equipment, LBITs have the potential to further explore chemical distribution mechanisms and ensure the safety and quality of food crops and products.

Multimodal feedback systems for smart laser osteotomy: Depth control and tissue differentiation.

OBJECTIVES: The study aimed to improve the safety and accuracy of laser osteotomy (bone surgery) by integrating optical feedback systems with an Er:YAG laser. Optical feedback consists of a real-time visual feedback system that monitors and controls the depth of laser-induced cuts and a tissue sensor differentiating tissue types based on their chemical composition. The developed multimodal feedback systems demonstrated the potential to enhance the safety and accuracy of laser surgery. MATERIALS AND METHODS: The proposed method utilizes a laser-induced breakdown spectroscopy (LIBS) system and long-range Bessel-like beam optical coherence tomography (OCT) for tissue-specific laser surgery. The LIBS system detects tissue types by analyzing the plasma generated on the tissue by a nanosecond Nd:YAG laser, while OCT provides real-time monitoring and control of the laser-induced cut depth. The OCT system operates at a wavelength of 1288 ± 30 nm and has an A-scan rate of 104.17 kHz, enabling accurate depth control. Optical shutters are used to facilitate the integration of these multimodal feedback systems. RESULTS: The proposed system was tested on five specimens of pig femur bone to evaluate its functionality. Tissue differentiation and visual depth feedback were used to achieve high precision both on the surface and in-depth. The results showed successful real-time tissue differentiation and visualization without any visible thermal damage or carbonization. The accuracy of the tissue differentiation was evaluated, with a mean absolute error of 330.4 µm and a standard deviation of ±248.9 µm, indicating that bone ablation was typically stopped before reaching the bone marrow. The depth control of the laser cut had a mean accuracy of 65.9 µm with a standard deviation of ±45 µm, demonstrating the system's ability to achieve the pre-planned cutting depth. CONCLUSION: The integrated approach of combining an ablative laser, visual feedback (OCT), and tissue sensor (LIBS) has significant potential for enhancing minimally invasive surgery and warrants further investigation and development.

Monitoring of minerals level in dialysate during hemodialysis treatment of kidney patients using LIBS technique.

Chronic kidney patients may have difficulties in blood filtration to eliminate the waste from the blood and to maintain the level of the minerals in the blood. Hemodialysis (HD) is an artificial way to remove the waste from the blood of a kidney patient and to maintain the proper mineral level in the blood. Reverse osmosis (RO) water having less total dissolved solids (TDS) < 50 ppm is used to prepare dialysis fluid/dialysate in dialysis with two chemicals (electrolytes and salts such as sodium bicarbonate). To check the purity of RO water from various RO machines used to prepare dialysate, which is vital for the safety of the patient, the laser-induced breakdown spectroscopy (LIBS) technique has been used. Also, the amount of replacement of electrolytes/minerals between the blood of the patient and dialysate after dialysis has been checked/determined for two different machines: hemodialysis machine (HDM) and on-line hemo-dia filtration machine (on-line HDF machine). It is observed that the proper amount of electrolytes (Na, Ca, and Mg) are maintained/normalized (depending upon patient's need), excess K is removed, and excess urea and creatinine is removed continuously from the blood of a patient during dialysis treatment which is essential for the better health of a kidney patient. Our results show that the RO water used in on-line HDF machine is purer (i.e., ultrapure); therefore, the quality of life of a kidney patient may be better if dialysis is performed with the on-line HDF machine. The experimental results also show that the filtration capacity of the dialyzer decreases after reusing it many times for dialysis treatment.

Characterization of Functional Coatings on Cork Stoppers with Laser-Induced Breakdown Spectroscopy Imaging.

Evaluating the efficiency of surface treatments is a problem of paramount importance for the cork stopper industry. Generically, these treatments create coatings that aim to enhance the impermeability and lubrification of cork stoppers. Yet, current methods of surface analysis are typically time-consuming, destructive, have poor representativity or rely on indirect approaches. In this work, the use of a laser-induced breakdown spectroscopy (LIBS) imaging solution is explored for evaluating the presence of coating along the cylindrical surface and in depth. To test it, several cork stoppers with different shaped areas of untreated surface were analyzed by LIBS, making a rectangular grid of spots with multiple shots per spot, to try to identify the correspondent shape. Results show that this technique can detect the untreated area along with other features, such as leakage and holes, allowing for a high success rate of identification and for its performance at different depths, paving the way for future industry-grade quality control solutions with more complex surface analysis.

A Simple Laser-Induced Breakdown Spectroscopy Method for Quantification and Classification of Edible Sea Salts Assisted by Surface-Hydrophilicity-Enhanced Silicon Wafer Substrates.

Salt, one of the most commonly consumed food additives worldwide, is produced in many countries. The chemical composition of edible salts is essential information for quality assessment and origin distinction. In this work, a simple laser-induced breakdown spectroscopy instrument was assembled with a diode-pumped solid-state laser and a miniature spectrometer. Its performances in analyzing Mg and Ca in six popular edible sea salts consumed in South Korea and classification of the products were investigated. Each salt was dissolved in water and a tiny amount of the solution was dropped and dried on the hydrophilicity-enhanced silicon wafer substrate, providing homogeneous distribution of salt crystals. Strong Mg II and Ca II emissions were chosen for both quantification and classification. Calibration curves could be constructed with limits-of-detection of 87 mg/kg for Mg and 45 mg/kg for Ca. Also, the Mg II and Ca II emission peak intensities were used in a k-nearest neighbors model providing 98.6% classification accuracy. In both quantification and classification, intensity normalization using a Na I emission line as a reference signal was effective. A concept of interclass distance was introduced, and the increase in the classification accuracy due to the intensity normalization was rationalized based on it. Our methodology will be useful for analyzing major mineral nutrients in various food materials in liquid phase or soluble in water, including salts.

Construction, Spectral Modeling, Parameter Inversion-Based Calibration, and Application of an Echelle Spectrometer.

We have developed a compact, asymmetric three-channel echelle spectrometer with remarkable high-spectral resolution capabilities. In order to achieve the desired spectral resolution, we initially establish a theoretical spectral model based on the two-dimensional coordinates of spot positions corresponding to each wavelength. Next, we present an innovative and refined method for precisely calibrating echelle spectrometers through parameter inversion. Our analysis delves into the complexities of the nonlinear two-dimensional echelle spectrogram. We employ a variety of optimization techniques, such as grid exploration, simulated annealing, genetic algorithms, and genetic simulated annealing (GSA) algorithms, to accurately invert spectrogram parameters. Our proposed GSA algorithm synergistically integrates the strengths of global and local searches, thereby enhancing calibration accuracy. Compared to the conventional grid exploration method, GSA reduces the error function by 22.8%, convergence time by 2.16 times, and calibration accuracy by 7.05 times. Experimental validation involves calibrating a low-pressure mercury lamp, resulting in an average spectral accuracy error of 0.0257 nm after performing crucial parameter inversion. Furthermore, the echelle spectrometer undergoes a laser-induced breakdown spectroscopy experiment, demonstrating exceptional spectral resolution and sub-10 ns time-resolved capability. Overall, our research offers a comprehensive and efficient solution for constructing, modeling, calibrating, and applying echelle spectrometers, significantly enhancing calibration accuracy and efficiency. This work contributes to the advancement of spectrometry and opens up new possibilities for high-resolution spectral analysis across various research and industry domains.

Impact of Glass Irradiation on Laser-Induced Breakdown Spectroscopy Data Analysis.

Increased absorption of optical materials arising from exposure to ionizing radiation must be accounted for to accurately analyze laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on two examples that mimic the diagnostics placed within novel nuclear reactor designs. The analysis is performed on LIBS data measured with 1% Xe gas in an ambient He environment and 1% Eu in a molten LiCl-KCl matrix, along with the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. Significant changes in the number of laser shots required to reach a 3σ detection level are observed for the Eu data, increasing by two orders of magnitude after exposure to a 1.7 × 1017 n/cm2 neutron fluence. For all cases examined, the spectral dependence of absorption results in the introduction of systematic errors. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation leads to statistically significant changes in the temperatures calculated from the Xe II lines and Eu II lines, lowering them from 8000 ± 610 K to 6900 ± 810 K and from 15,800 ± 400 K to 7200 ± 800 K, respectively, for exposure to the 1.7 × 1017 n/cm2 fluence. The temperature range required for a 95% confidence interval for the calculated temperature is also broadened. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation attenuation is relatively small, or through analysis using the iterative Saha-Boltzmann method.

Depth-Resolved Elemental Analysis on Moving Electrode Foils with Laser-Induced Breakdown Spectroscopy.

In this study, a new method for the inline measurement of depth profiles on a continuously moving sample with laser-induced breakdown spectroscopy is presented. The ablation profile is generated by ablating the sample with a burst of laser pulses, where the emission spectrum of each laser-induced plasma is analyzed on a spectrometer. A Q-switched Nd:YAG laser at 1064 nm with 10 mJ pulse energy, 6 ns pulse duration and 100 Hz repetition rate was used. The focusing lens for the pulsed laser and a deflection mirror are mounted on a moving stage, which is precisely aligned in height and orientation to the movement of a conveyor belt transporting the sample. The stage speed is actively synchronized to the speed of the moving sample by a wheel encoder to assure that all laser pulses hit the same position at the sample. The feasibility for depth-resolved elemental analysis on moving samples is shown for coatings of electrode foils for lithium-ion batteries. The coating homogeneity was measured at a speed up to 17 m/min. For a 100 µm coating, 10 laser pulses were needed to measure a full depth profile.

Comparative Long-Wave Infrared Laser-Induced Breakdown Spectroscopy Employing 1-D and 2-D Focal Plane Array Detectors.

Long-wave infrared (LWIR) emissions of laser-induced plasma on solid potassium chloride and acetaminophen tablet surfaces were studied using both a one-dimensional (1-D) linear array detection system and, for the first time, a two-dimensional (2-D) focal plane array (FPA) detection system. Both atomic and molecular infrared emitters in the vicinity of the plasma were identified by analyzing the detected spectral signatures in the infrared region. Time- and space-resolved long-wave infrared emissions were also studied to assess the temporal and spatial behaviors of atomic and molecular emitters in the plasma. These pioneer temporal and spatial investigations of infrared emissions from laser-induced plasma would be valuable to the modeling of plasma evolutions and the advances of the novel LWIR laser-induced breakdown spectroscopy (LIBS). When integrated both temporally (≥200 µs) and spatially using a 2-D FPA detector, the observed intensities and signal-to-noise-ratio (SNR) of single-shot LWIR LIBS signature emissions from intact molecules were considerably enhanced (e.g., with enhancement factors up to 16 and 3.76, respectively, for a 6.62 µm band of acetaminophen molecules) and, in general, comparable to those from the atomic emitters. Pairing LWIR LIBS with conventional ultraviolet-visible-near infrared (UV/Vis/NIR) LIBS, a simultaneous UV/Vis/NIR + LWIR LIBS detection system promises unprecedented capability of in situ, real-time, and stand-off investigation of both atomic and molecular target compositions to detect and characterize a range of chemistries.

Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams.

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.

Explorative Data Analysis Methods: Application to Laser-Induced Breakdown Spectroscopy Field Data Measured on the Island of Vulcano, Italy.

One of the strengths of laser-induced breakdown spectroscopy (LIBS) is that a large amount of data can be measured relatively easily in a short time, which makes LIBS interesting in many areas, from geomaterial analysis with portable handheld instruments to applications for the exploration of planetary surfaces. Statistical methods, therefore, play an important role in analyzing the data to detect not only individual compositions but also trends and correlations. In this study, we apply two approaches to explore the LIBS data of geomaterials measured with a handheld device at different locations on the Aeolian island of Vulcano, Italy. First, we use the established method, principal component analysis (PCA), and second we adopt the principle of the interesting features finder (IFF), which was recently proposed for the analysis of LIBS imaging data. With this method it is possible to identify spectra that contain emission lines of minor and trace elements that often remain undetected with variance-based methods, such as PCA. We could not detect any spectra with IFF that were not detected with PCA when applying both methods to our LIBS field data. The reason for this may be the nature of our field data, which are subject to more experimental changes than data measured in laboratory settings, such as LIBS imaging data, for which the IFF was introduced first. In conclusion, however, we found that the two approaches complement each other well, making the exploration of the data more intuitive, straightforward, and efficient.

Agarose Film-Based Liquid-Solid Conversion for Heavy Metal Detection of Water Samples by Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) shows promising applications in the analysis of environmental heavy metals. However, direct analysis in water by LIBS faces the problems of droplet splashing and laser energy decay. In this study, a novel liquid-solid conversion method based on agarose films is proposed to provide an easy-to-operate and sensitive detection of heavy metals. First, the water samples were converted into semi-solid hydrogels with the aid of agarose and then dried into agarose films to make the signal intensities stronger. The calibration curves of Cd, Pb and Cr were constructed. The proposed method was validated by standard heavy metal solutions and real water samples. The results showed that the values of R2 were 0.990, 0.989 and 0.975, and the values of the LOD were 0.011, 0.122 and 0.118 mg L-1 for Cd (I) 228.80, Pb (I) 405.78 and Cr (I) 427.48 nm, respectively. The RMSEs of validation were 0.068 (Cd), 0.107 (Pb) and 0.112 mg·L-1 (Cr), and the recovery values were in the range of 91.2-107.9%. The agarose film-based liquid-solid conversion method achieved the desired ease of operation and sensitivity of LIBS in heavy-metal detection, thereby, showing good application prospects in heavy metal monitoring of water.

Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy.

The adulteration of olive oil is a crucial matter for food safety authorities, global organizations, and consumers. To guarantee olive oil authenticity, the European Union (EU) has promoted the labeling of olive oils with the indices of Protected Designation of Origin (PDO) and Protected Geographical Identification (PGI), while food security agencies are also interested in newly emerging technologies capable of operating reliably, fast, and in real-time, either in situ or remotely, for quality control. Among the proposed methods, photonic technologies appear to be suitable and promising for dealing with this issue. In this regard, a laser-based technique, namely, Laser-Induced Breakdown Spectroscopy (LIBS), assisted via machine learning tools, is proposed for the real-time detection of olive oil adulteration with lower-quality oils (i.e., pomace, soybean, sunflower, and corn oils). The results of the present work demonstrate the high efficiency and potential of the LIBS technique for the rapid detection of olive oil adulteration and the detection of adulterants.

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis.

A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.