Search

Metal sub-microparticles (SMPs) and nanoparticles (NPs) presence in food is attributable to increasing pollution from the environment in raw materials and finished products. In the present study, a multifaceted analytical strategy based on Environmental Scanning Electron Microscopy and High-Angle Annular Dark-Field—Scanning Transmission Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (ESEM-EDX, HAADF-STEM-EDX) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was proposed for the detection and characterization of metal and metal-containing SMPs and NPs in durum wheat samples, covering a size measurement range from 1 nm to multiple µm. ESEM-EDX and ICP-MS techniques were applied for the assessment of SMP and NP contamination on the surface of wheat grains collected from seven geographical areas characterized by different natural and anthropic conditions, namely Italy, the USA, Australia, Slovakia, Mexico, Austria, and Russia. ICP-MS showed significant differences among the mean concentration levels of metals, with the USA and Italy having the highest level. ESEM-EDX analysis confirmed ICP-MS concentration measurements and measured the highest presence of particles < 0.8 µm in size in samples from Italy, followed by the USA. Less marked differences were observed when particles < 0.15 µm were considered. HAADF-STEM-EDX was applied to a selected number of samples for a preliminary assessment of internal contamination by metal SMPs and NPs, and to expand the measurable particle size range. The multifaceted approach provided similar results for Fe-containing SMPs and NPs. ICP-MS and ESEM-EDX also highlighted the presence of a significant abundance of Ti- and Al-containing particles, while for STEM-EDX, sample preparation artifacts complicated the interpretation. Finally, HAADF-STEM-EDX results provided relevant information about particles in the low nm range, since, by applying this technique, no particles smaller than 50 nm were observed in accordance with ESEM-EDX. [ABSTRACT FROM AUTHOR]

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices. [ABSTRACT FROM AUTHOR]

Dental calculus is a valuable resource for the reconstruction of dietary habits and oral microbiome of past populations. In 2020 the remains of Duke Alessandro Farnese and his wife Maria D'Aviz were exhumed to get novel insights into the causes of death. This study aimed to investigate the dental calculus metabolome of the noble couple by untargeted metabolomics. The pulverized samples were decalcified in a water-formic acid mixture, extracted using methanol/acetonitrile and analyzed by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) using a reversed-phase separation followed by electrospray ionization and full scan in positive and negative ion mode. Waters Synapt-G2-Si High-Definition hybrid quadrupole time-of-flight mass spectrometer was used. Significant features were then identified using MSE acquisition mode, recording information on exact mass precursor and fragment ions within the same run. This approach, together with data pre-treatment and multivariate statistical analysis allowed for the identification of compounds able to differentiate between the investigated samples. More than 200 metabolites were identified, being fatty acids, alcohols, aldehydes, phosphatidylcholines, phosphatidylglycerols, ceramides and phosphatidylserines the most abundant classes. Metabolites deriving from food, bacteria and fungi were also determined, providing information on the habits and oral health status of the couple. [ABSTRACT FROM AUTHOR]

The safety of food contact materials is a hot topic since chemicals can migrate from packaging into food, thus raising health concerns about and/or producing changes in the organoleptic properties of foodstuffs. Migration tests are required to demonstrate the compliance with current regulations and to investigate the transferred compounds. In this context, mass spectrometry is the analytical technique of choice for the detection and quantitation of both intentionally added substances, such as antioxidants, stabilizers, processing aids, and non-intentionally added substances (NIAS). Untargeted strategies represent a major analytical challenge, providing a comprehensive fingerprinting of the packaging material and migrating components, allowing for NIAS identification. Hyphenated mass spectrometry-based techniques have been devised for screening the presence of migrating contaminants and for quantitation purposes. Both low-resolution (LRMS) and high-resolution (HRMS) methods were screened, with a special emphasis on the latter because of its capability to directly characterize food contact materials with minimal/no sample preparation, avoiding chromatographic separation, and reducing sample handling, analysis costs, and time. Examples related to the migration of contaminants from existing or newly developed bioplastic materials will be discussed, providing an overview of the most used MS-based methods, covering the state-of-the-art approaches from 2012 up to 2022. [ABSTRACT FROM AUTHOR]

An innovative and very selective solid-phase microextraction coating synthesized by sol--gel technology was developed for the determination of environmental pollutants such as aromatic hydrocarbons at trace levels in air, water, and soil samples. The obtained fibers, composed of quinoxaline-bridged cavitand units, were characterized in terms of film thickness, morphology, thermal stability, and pH resistance. Fibers, characterized by an average thickness of 56 [+ or -] 6 [micro]m, exhibited an excellent thermal stability until 400 [degrees]C and a very good fiber-to-fiber and batch-to-batch repeatability with RSD lower than 6%. Finally, the capabilities of the developed coating for the selective sampling of aromatic hydrocarbons were proved, obtaining LOD values in the subnanogram per liter range. Extraction efficiency at least 2-fold higher than that obtained using commercial devices was proved for chlorobenzenes sampling in fiver water, obtaining extraction recoveries ranging from 87.4 [+ or -] 2.6% to 94.7 [+ or -] 1.9%. The selective desorption of benzene in the presence of high amounts of other airborne pollutants was also demonstrated.