Reconstructing Ancient Egyptian Diet through Bone Elemental Analysis Using LIBS (Qubbet el Hawa Cemetery)

The ambiguous relationship between food and health across the centuries

Since the Palaeolithic Age food has been closely linked to the development of the human species, meeting our energy needs and fuelling our cell metabolism. Without food there can be no life. However, over the centuries food and our eating habits have also had a damaging effect, whether through deficiencies, excesses, direct toxic effects or as a vector of pathogenic agents. The human species has known two major food revolutions: one at the start of the Neolithic Age and the other very recently in the years following the Second World War. In this article we will be looking at the ambiguous relationship between food and human health as well as the health of our planet.

Sr/Ca ratios indicate frugivory versus folivory in primates: a case study using handheld XRF in Kibale National Park, Uganda

Researchers often use trace element concentrations, including strontium-calcium ratios (Sr/Ca), to reconstruct paleodiets. While most commonly used as a proxy for meat consumption, a more appropriate application may be to differentiate frugivory from folivory. Sr/Ca ratios in animal tissue reflect the Sr/Ca ratios of the highest calcium components of that animal's diet. Because plants have much higher concentrations of calcium than meat, meat consumption signals are often overwhelmed by the variation in Sr/Ca ratios coming from different plant parts. This study uses faunal and plant data from Kibale National Park, a protected forest in southwestern Uganda home to numerous primate species (for example, common chimpanzees and baboons), to assess the reliability of Sr/Ca ratios to differentiate between primate dietary groups. We find that leaves consistently have higher strontium and calcium concentrations than fruits and that this is mirrored in higher Sr/Ca ratios in folivorous primates compared to frugivorous primates. Plant species differ widely in both their overall Sr/Ca ratios and the differences between their fruit and leaf Sr/Ca ratios, but this variation does not overwhelm the dietary signal separating frugivores and folivores. Furthermore, this research demonstrates that non-destructive and portable X-ray florescence (XRF) methods are an effective means of gathering Sr/Ca data from plant and faunal material, increasing the opportunities to apply such methods to fossil material in the future.

Medical application of laser-induced breakdown spectroscopy (LIBS) for assessment of trace element and mineral in biosamples: Laboratory and clinical validity of the method

BACKGROUND

Biomedical application is based on the use of LIBS-derived data on chemical contents of tissues in diagnosis of diseases, forensic investigation, as well as a mechanism for providing online feedback for laser surgery. Although LIBS has certain advantages, the issue of correlation of LIBS-derived data on chemical element content in different human and animal tissues with other methods, and especially ICP-MS, remains pertinent. The objective of the present review was to discuss the application of laser-induced breakdown spectroscopy (LIBS) for elemental analysis of human biosamples or tissues from experimental models of human diseases.

METHODS

A systematic search in the PubMed-Medline, Scopus, and Google Scholar databases using the terms laser-induced breakdown spectroscopy, LIBS, metals, trace elements, minerals, and names of particular chemical elements was performed up through 25 February, 2023. Of all extracted studies only those dealing with human subjects, human tissues, in vivo animal and in vitro cell line models of human diseases were reviewed in detail.

RESULTS

The majority of studies revealed a wide number of metals and metalloids in solid tissues including teeth (As, Ag, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Ni, P, Pb, Sn, Sr, Ti, and Zn), bones (Al, Ba, Ca, Cd, Cr, K, Mg, Na, Pb, Sr), and nails (Al, As, Ca, Fe, K, Mg, Na, P, Pb, Si, Sr, Ti, Zn). At the same time, LIBS was also used for estimation of trace element and mineral content in hair (Ca, Cu, Fe, K, Mg, Na, Zn), blood (Al, Ca, Co, Cd, Cu, Fe, Mg, Mn, Ni, Pb, Si, Sn, Zn), cancer tissues (Ca, Cu, Fe, Mg, K, Na, Zn) and other tissues. Single studies revealed satisfactory correspondence between quantitative LIBS and ICP-OES/MS data on the level of As (81-93 %), Pb (94-98 %), Cd (50-94 %) in teeth, Cu (97-105 %), Fe (117 %), Zn (88-117 %) in hair, Ca (97-99 %), Zn (90-95 %), and Pb (61-82 %) in kidney stones. LIBS also estimated specific patterns of trace element and mineral content associated with multiple pathologies, including caries, cancer, skin disorders, and other systemic diseases including diabetes mellitus type 2, osteoporosis, hypothyroidism, etc. Data obtained from in situ tissue LIBS analysis were profitably used for discrimination between tissue types.

CONCLUSIONS

Taken together, the existing data demonstrate the applicability of LIBS for medical studies, although further increase in its sensitivity, calibration range, cross-validation, and quality control is required.

Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores' selection in the metallurgical industry.