Spectral diagnosis of health hazardous toxins in face foundation powders using laser induced breakdown spectroscopy and inductively coupled plasma-optical emission spectroscopy (ICP-OES)

Mass-Spectrometry-Based Research of Cosmetic Ingredients

Cosmetic products are chemical substances or mixtures used on the skin, hair, nails, teeth, and the mucous membranes of the oral cavity, whose use is intended to clean, protect, correct body odor, perfume, keep in good condition, or change appearance. The analysis of cosmetic ingredients is often challenging because of their huge complexity and their adulteration. Among various analytical tools, mass spectrometry (MS) has been largely used for compound detection, ingredient screening, quality control, detection of product authenticity, and health risk evaluation. This work is focused on the MS applications in detecting and quantification of some common cosmetic ingredients, i.e., preservatives, dyes, heavy metals, allergens, and bioconjugates in various matrices (leave-on or rinse-off cosmetic products). As a global view, MS-based analysis of bioconjugates is a narrow field, and LC- and GC/GC×GC-MS are widely used for the investigation of preservatives, dyes, and fragrances, while inductively coupled plasma (ICP)-MS is ideal for comprehensive analysis of heavy metals. Ambient ionization approaches and advanced separation methods (i.e., convergence chromatography (UPC2)) coupled to MS have been proven to be an excellent choice for the analysis of scented allergens. At the same time, the current paper explores the challenges of MS-based analysis for cosmetic safety studies.

Automated, real-time material detection during ultrashort pulsed laser machining using laser-induced breakdown spectroscopy, for process tuning, end-pointing, and segmentation

The rapid, high-resolution material processing offered by ultrashort pulsed lasers enables a wide range of micro and nanomachining applications in a variety of disciplines. Complex laser processing jobs conducted on composite samples, require an awareness of the material type that is interacting with laser both for adjustment of the lasering process and for endpointing. This calls for real-time detection of the materials. Several methods such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and energy dispersive X-Ray spectroscopy (EDS) can be used for material characterization. However, these methods often need interruption of the machining process to transfer the sample to another instrument for inspection. Such interruption significantly increases the required time and effort for the machining task, acting as a prohibitive factor for many laser machining applications. Laser induced breakdown spectroscopy (LIBS) is a powerful technique that can be used for material characterization, by analyzing a signal that is generated upon the interaction of laser with matter, and thus, it can be considered as a strong candidate for developing an in-situ characterization method. In this work, we propose a method that uses LIBS in a feedback loop system for real time detection and decision making for adjustment of the lasering process on-the-fly. Further, use of LIBS for automated material segmentation, in the 3D image resulting from consecutive lasering and imaging steps, is showcased.

Applications of mass spectrometry in cosmetic analysis: An overview

Mass spectrometry (MS) is a crucial tool in cosmetic analysis. It is widely used for ingredient screening, quality control, risk monitoring, authenticity verification, and efficacy evaluation. However, due to the diversity of cosmetic products and the rapid development of MS-based analytical methods, the relevant literature needs a more systematic collation of information on this subject to unravel the true potential of MS in cosmetic analysis. Herein, an overview of the role of MS in cosmetic analysis over the past two decades is presented. The currently used sample preparation methods, ionization techniques, and types of mass analyzers are demonstrated in detail. In addition, a brief perspective on the future development of MS for cosmetic analysis is provided.

Quantitative analysis of human hairs and nails

Hair and nails are human biomarkers capable of providing a continuous assessment of the concentrations of elements inside the human body to indicate the nutritional status, metabolic changes, and the pathogenesis of various human diseases. Laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF) spectrometry are robust and multi-element analytical techniques able to analyze biological samples of various kinds for disease diagnosis. The primary objective of this review article is to focus on the major developments and advances in LIBS and XRF for the elemental analysis of hair and nails over the last 10-year period. The developments in the qualitative and quantitative analyses of human hair and nail samples are discussed in detail, with special emphasis on the key aspects of elemental imaging and distribution of essential and non-essential elements within the hair and nail tissue samples. Microchemical imaging applications by LIBS and XRF (including micro-XRF and scanning electron microscopy, SEM) are also presented for healthy as well as diseased tissue hair and nail samples in the context of disease diagnosis. In addition, main challenges, prospects, and complementarities of LIBS and XRF toward analyzing human hair and nails for disease diagnosis are also thoroughly discussed here.

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.

Insertable, Scabbarded, and Nanoetched Silver Needle Sensor for Hazardous Element Depth Profiling by Laser-Induced Breakdown Spectroscopy

Sensing of hazardous metals is urgent in many areas (e.g., water pollution and meat products) as heavy metals threaten people's health. Laser-induced breakdown spectroscopy (LIBS), as a rapid, in situ, and multielemental analytical technique, has been widely utilized in rapid hazardous heavy metal sensing. However, loose and water-containing samples (e.g., meat, plant, and soil) are hard to analyze by LIBS directly, and heavy metal depth profiling for bulk samples remains suspenseful. Here, inspired by the Needle, the sword of Arya Stark in Game of Thrones, we propose an insertable, scabbarded, and nanoetched silver (NE-Ag) needle sensor for rapid hazardous element sensing and depth profiling. The NE-Ag needle sensor features a micro-nanostructure surface for inserting into the bulk sample and absorbing hazardous analytes. For accurate elemental depth profiling, we design a stainless-steel scabbard to wrap and protect the NE-Ag needle from pollution (unexpected contaminant absorption) during the needle insertion and extraction process. The results for cadmium (Cd) show that the relative standard deviation equals to 6.7% and the limit of detection reaches 0.8 mg/L (ppm). Furthermore, the correlations (Pearson correlation coefficient) for Cd and chromium (Cr) depth profiling results are no less than 0.96. Furthermore, the total testing time could be less than 1 h. All in all, the insertable and scabbarded NE-Ag needle senor has high potential in rapid hazardous heavy metal depth profiling in different industries.

FROZEN! Intracellular multi-electrolyte analysis measures millimolar lithium in mammalian cells

Lithium salts are commonly used as medication for Bipolar Disorder (BD) and depression. However, there are limited methods to quantify intracellular lithium. Most methods to analyze intracellular electrolytes require tedious sample processing, specialized and often expensive machinery, sometimes involving harmful chemicals, and a bulk amount of the sample. In this work, we report a novel method (FROZEN!) based on cell isolation (from the surrounding medium) through rapid de-ionized water cleaning, followed by flash freezing for preservation. SKOV3 cells were cultured in normal medium and a medium containing 1.0 mM lithium. Lithium and other intracellular electrolytes in the isolated and preserved cells were simultaneously analyzed with laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). Key electrolytes such as sodium, potassium, and magnesium, along with lithium, were detectable at the single-cell level. We found that cells cultured in the lithium medium have an intracellular lithium concentration of 0.5 mM. Concurrently, the intracellular concentrations of other positively charged electrolytes (sodium, potassium, and magnesium) were reduced by the presence of lithium. FROZEN! will greatly facilitate research in intracellular electrolyte balance during drug treatment, or other physiological stresses. In particular, the cell isolation and preservation steps can be easily performed by many laboratories worldwide, after which the samples are sent to an analytical laboratory for electrolyte analysis.

Application of Laser-Induced Breakdown Spectroscopy in Detection of Cadmium Content in Rice Stems

The presence of cadmium in rice stems is a limiting factor that restricts its function as biomass. In order to prevent potential risks of heavy metals in rice straws, this study introduced a fast detection method of cadmium in rice stems based on laser induced breakdown spectroscopy (LIBS) and chemometrics. The wavelet transform (WT), area normalization and median absolute deviation (MAD) were used to preprocess raw spectra to improve spectral stability. Principal component analysis (PCA) was used for cluster analysis. The classification models were established to distinguish cadmium stress degree of stems, of which extreme learning machine (ELM) had the best effect, with 91.11% of calibration accuracy and 93.33% of prediction accuracy. In addition, multivariate models were established for quantitative detection of cadmium. It can be found that ELM model had the best prediction effects with prediction correlation coefficient of 0.995. The results show that LIBS provides an effective method for detection of cadmium in rice stems. The combination of LIBS technology and chemometrics can quickly detect the presence of cadmium in rice stems, and accurately realize qualitative and quantitative analysis of cadmium, which could be of great significance to promote the development of new energy industry.