A new nanometrological strategy for titanium dioxide nanoparticles screening and confirmation in personal care products by CE-spICP-MS

Single particle inductively coupled plasma mass spectrometry and its variations for the analysis of nanoparticles

Single particle inductively coupled plasma mass spectrometry (spICPMS) can count and weigh metal-containing nanoparticles (NPs), enabling their sizing if their geometry, density, and composition are known. With a nebulizer and a spray chamber for sample introduction, both the sample uptake rate and the transport efficiency must be determined when calibrating with solutions. In contrast, flow injection (FI) and mono-segmented flow analysis (MSFA) coupled to spICPMS do not need determination of the transport efficiency and sample uptake rate for accurate NP mass measurement. Correcting for the significant settling time on some instruments is also discussed, as well as calibration through signal integration instead of averaging, which eliminates the need to measure the transport efficiency when seeking NP mass. Nitrogen added to the outer plasma gas can reduce the background for the determination of P, S, Ca and Fe. Infrared heating of the sample introduction system provides 100% transport efficiency, enabling accurate particle mass and concentration measurements without measurement of transport efficiency.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

A Novel Method for the Background Signal Correction in SP-ICP-MS Analysis of the Sizes of Titanium Dioxide Nanoparticles in Cosmetic Samples

We discuss the features involved in determining the titanium dioxide nanoparticle (TiO₂NP) sizes in cosmetic samples via single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) in the millisecond-time resolution mode, and methods for considering the background signal. In the SP-ICP-MS determination of TiO₂NPs in cosmetics, the background signal was recorded in each dwell time interval due to the signal of the Ti dissolved form in deionized water, and the background signal of the cosmetic matrix was compensated by dilution. A correction procedure for the frequency and intensity of the background signal is proposed, which differs from the known procedures due to its correction by the standard deviation above the background signal. Background signals were removed from the sample signal distribution using the deionized water signal distribution. Data processing was carried out using Microsoft Office Excel and SPCal software. The distributions of NP signals in cosmetic product samples were studied in the dwell time range of 4-20 ms. The limit of detection of the NP size (LOD_size) with the proposed background signal correction procedure was 71 nm. For the studied samples, the LOD_size did not depend on the threshold of the background signal and was determined by the sensitivity of the mass spectrometer.

Contributions of Capillary Electrophoresis in Analytical Nanometrology: A Critical View

An overview on the increasing role of capillary electrophoresis in characterization and direct analysis of nanomaterials is herein presented. The niche of electrophoretic approaches in nanometrology is so relevant that nonmetallic, metal, metal oxide nanoparticles, and quantum dots have been analyzed to be targeted via capillary electrophoresis with conventional detection systems or coupling arrangements aimed at increasing selectivity and sensitivity toward either pristine or conjugated nanoparticles. Moreover, parameters altering intrinsic properties of nanoparticles may be optimized to gather the desired results and identify nanomaterials according to their size, shape, or associations with binding agents. The usefulness and quickness of capillary electrophoresis for quantifying or screening ultrasmall-sized particles enables this technique to set an example for analysis of standards or previously synthesized nanostructures in research or routine laboratories. Abundant evidence of the suitability of electrophoretic approaches for characterization and direct determination of nanomaterials in actual samples has been provided in this review, together with a discussion about hyphenation with state-of-the art detectors and comparison between capillary electrophoresis with other separation approaches. This permits scientific community to be optimistic in the short term.