Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review

Differentiating between normal and inflammatory blood serum samples using spectrochemical analytical techniques and chemometrics

Inflammation detection in blood serum samples is commonly performed using clinical analyzers, which are expensive and complex and require specific labels or markers. Spectrochemical analytical techniques, such as laser-induced breakdown spectroscopy (LIBS) and laser-induced fluorescence (LIF), have emerged as alternative methods for qualitative and non-destructive analysis in various fields. This study explores applying LIBS and LIF techniques for label-free discrimination between normal and inflammatory blood serum samples. In the LIBS analysis, the serum samples are deposited on ashless filter paper and exposed to a high-power Nd:YAG laser source to induce plasma emission. The emitted light is dispersed in a spectrometer and an ICCD camera that captures the spectral lines. The LIF technique utilizes a diode-pumped solid-state laser source to excite the blood serum sample placed in a quartz cuvette. The resulting emission spectra are collected and analyzed using a spectrometer equipped with a CCD detector. The obtained spectroscopic data from both techniques is subjected to principal component analysis (PCA) and graph theory for classification and clustering. The PCA classified the two classes with a data variance of 85.4% and 92.8% based on the first two principal components (PCs) for LIBS and LIF spectra. The graph theory clustered the two classes with an accuracy of 76% and 100% based on LIBS and LIF spectra. The statistical methods effectively discriminate between normal and inflammatory serum samples, providing satisfactory results. The proposed spectrochemical methods offer several advantages over traditional clinical analyzers. They are cost-effective and rapid, making them suitable for the fast and reliable identification of serum samples in laboratories. The non-destructive nature of these techniques eliminates the need for specific labels or markers, further streamlining the analysis process.

Novel Methods Developed in Bioequivalence Assays: Patent Review

This study examines advancements in bioequivalence (BE) assessment methods, with a focus on in vitro-in vivo correlation (IVIVC) and dissolution testing technologies. A systematic patent search was conducted via Espacenet, following PRISMA criteria and the study objectives, revealing 216 relevant patents, of which 28 were selected based on their contributions to novel BE methodologies. Analysis indicates a rapid increase in patent filings from 2021 to 2022, with a significant concentration of contributions from China. Key innovations include enhancements in dissolution testing apparatus, application of physiologically based pharmacokinetic (PBPK) modeling for IVIVC, and advanced statistical approaches for BE assessment. In dissolution testing, ƒ1 and ƒ2 factors remain essential metrics for assessing similarity, especially in solid oral dosage forms. These innovations enhance the efficiency (streamline) of BE evaluations, optimizing the biowaiver process and minimizing the need for extensive clinical trials while ensuring greater precision and reliability. The dissolution test, particularly when combined with PBPK models, allows for predictive evaluation of formulation changes and population-specific responses, fostering efficiency in drug development. Overall, these novel BE assessment approaches provide a framework for regulatory compliance, cost-effective production, and assurance of therapeutic equivalence in generic formulations. While they may not always be implemented in practice, they contribute significantly to innovation in the field, driving advancements in bioequivalence evaluation. This review highlights the evolving landscape of BE and IVIVC methodologies and underscores the importance of incorporating innovative testing approaches to advance pharmaceutical science and regulatory practices.

Simple and sensitive determination of Cr (III), Cu (II) and Pb (II) in tea infusions using AgNPs-modified resin combined with laser-induced breakdown spectroscopy

The detection of heavy metals in tea infusions is important because of the potential health risks associated with their consumption. Existing highly sensitive detection methods pose challenges because they are complicated and time-consuming. In this study, we developed an innovative and simple method using Ag nanoparticles-modified resin (AgNPs-MR) for pre-enrichment prior to laser-induced breakdown spectroscopy for the simultaneous analysis of Cr (III), Cu (II), and Pb (II) in tea infusions. Signal enhancement using AgNPs-MR resulted in amplification with limits of detection of 0.22 μg L-1 for Cr (III), 0.33 μg L-1 for Cu (II), and 1.25 μg L-1 for Pb (II). Quantitative analyses of these ions in infusions of black tea from various brands yielded recoveries ranging from 83.3% to 114.5%. This method is effective as a direct and highly sensitive technique for precisely quantifying trace concentrations of heavy metals in tea infusions.

Investigating the mechanistic impact of pork soft tissue preparation techniques on the classification precision of laser-induced breakdown spectroscopy

The investigation of the mechanism underlying the impact of biological soft tissue sample preparation methods on laser-induced breakdown spectroscopy (LIBS) signals can enhance the stability of LIBS signals. Our study focused on four specific preparation methods applied to pork samples: rapid freezing, fresh slicing, drying, and pressing. The influence of various preparation techniques on the signal-to-noise ratio and fluctuation of Ca, Na, Mg, and CN bands within the sample spectra was assessed. The signal-to-noise ratios for samples that were dried and pressed notably improved. And the pressing method effectively mitigated the uneven distribution of pork tissue components, displaying superior spectral line stability. To explain this phenomenon, we used the Saha-Boltzmann diagram to estimate the plasma temperature. Remarkably, there was a significant reduction in plasma temperature fluctuations across four pressed samples, with a standard deviation of 108.53. Furthermore, we undertook a classification analysis employing support vector machine models to corroborate the generalization efficacy of the sample preparation technique. Dried and pressed samples demonstrated notably higher classification accuracy, precision, and recall (all >93%) compared to frozen and fresh samples, where these metrics remained below 86%. The performance of the SVM model was ultimately evaluated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC). The AUC for the frozen, fresh, dried, and pressed samples was 0.854, 0.907, 0.989, and 0.996, respectively. The findings revealed that the pressing method exhibited superior performance, followed by drying, fresh slicing, and freezing, in descending order of effectiveness.

Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.

Effect of target thickness and laser irradiance on the back-reflection-enhanced laser-induced breakdown spectroscopy signal in glass

In the work that is being presented here, the effect of sample thickness and laser irradiance on the reduction of the signal-to-background ratio SBG and the plasma parameters, specifically electron temperature and electron density, is being investigated using back-reflection-enhanced laser-induced breakdown spectroscopy (BRELIBS). Copper and silver discs that had been highly polished were attached to the back surface of the glass target, and the Nd-YAG laser beam that was focused on the front surface of the target was tuned to its fundamental wavelength. The thicknesses of the transparent glass samples that were analysed were 1 mm, 3 mm, and 6 mm. One is able to achieve a range of different laser irradiance levels by adjusting the working distance that exists between the target sample and the focusing lens. The end result of this is that the signal-to-background ratio in the BRELIBS spectra of thicker glass samples is significantly lower as compared to the ratio in the spectra of thinner glass samples. In addition, a significant influence of modifying the laser irradiance (by increasing the working distance on the SBG ratio) is seen at various glass thicknesses for both BRELIBS and LIBS, with BRELIBS having a better SBG. Nevertheless, the laser-induced plasma parameter known as the electron temperature has not been significantly impacted by the decrease in the glass thickness.

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in Fritillaria thunbergii. A total of three machine learning algorithms were utilized, including a gradient boosting machine (GBM), partial least squares regression (PLSR) and support vector regression (SVR). Three promising wavelength selection methods were evaluated for comparison, namely, a competitive adaptive reweighted sampling method (CARS), a random frog method (RF), and an uninformative variable elimination method (UVE). Compared to full wavelengths, the selected wavelengths produced excellent results. Overall, RC2, RV2, RP2, RSMEC, RSMEV and RSMEP for the selected variables are as follows: 0.9967, 0.8899, 0.9403, 1.9853 mg kg−1, 11.3934 mg kg−1, 8.5354 mg kg−1; 0.9933, 0.9316, 0.9665, 5.9332 mg kg−1, 18.3779 mg kg−1, 11.9356 mg kg−1; 0.9992, 0.9736, 0.9686, 1.6707 mg kg−1, 10.2323 mg kg−1, 10.1224 mg kg−1 were obtained for Cd Cu and Pb, respectively. Experimental results showed that all three methods could perform variable selection effectively, with GBM-UVE for Cd, SVR-RF for Pb, and GBM-CARS for Cu providing the best results. The results of the study suggest that LIBS coupled with wavelength selection can be used to detect heavy metals rapidly and accurately in Fritillaria by extracting only a few variables that contain useful information and eliminating non-informative variables.