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A multi-analytical approach is usually applied in provenance studies of archaeological marbles. However, for very similar marble varieties, additional techniques and approaches are required. This paper uses a case study to illustrate this with two Catalan marble districts (Gualba and Ceret) and three sets of archaeological marbles. The common multi-method approach is unable to discriminate between the two districts, and such distinction is only partially glimpsed using unsupervised multivariate data analyses on a transformed geochemical dataset of reference samples. In contrast, several supervised classification models have been successfully trained to discriminate between the quarries without any special data transformation. All the trained models agree to assign the three sets of archaeological samples to the Gualba quarry district. Additional outcomes of the paper are a comprehensive archaeometric characterization of the little-known marbles of Gualba and Ceret and the first archaeometrically supported evidence of the use of Gualba marble during Roman and Medieval times.

In many agronomic, environmental and nutritional studies it is of paramount importance to have the information about elemental composition of vegetation samples. In a previous publication we demonstrated the potential of suspension preparation and total reflection X-ray fluorescence (TXRF) as a simple and sustainable approach for multielemental analysis of vegetal foodstuff [1]. Vegetation samples were prepared by suspending 20 mg of the powdered sample in de- ionized water and the subsequent deposition of 10 L of the homogenized suspension on a reflective carrier. Based on the results obtained, the main aim of the present contribution was to get insight to the experimental parameters that mostly affect the precision and accuracy of the results obtained by suspension preparation and TXRF analysis. For that, two different experimental design statistical methods (Taguchi method [2] and Surface Response Method [3]) were tested. The studied variables and ranges were: suspension concentration (10- 50 mg/mL), disperser agent (de-ionized water and 1% Triton X- 100), sonication time (0-10 minutes) and sample deposition volume (5-20 L). In all cases, triplicate experiments were carried out for each variable combination. Obtained results showed that the most relevant parameters in the preparation of the vegetation suspensions were the concentration and the sample deposition volume on the reflective support. For most elements the best preparation conditions were to use medium suspension concentrations and deposition volumes (35 mg/mL and 15 μL), while for light elements such as K better results were obtained using low concentrations and deposition volumes (10 mg/mL and 5 μL). The quality of the results obtained was evaluated by analyzing certified reference materials with a vegetation matrix. Finally, the TXRF method was applied in the determination of some essential elements (Mn, Fe, Cu, Zn) in different medicinal plants and the results were compared with those obtained with other analytical approaches such as digestion and subsequent analysis using ICP-OES and FAAS.

Manganese is an essential dietary nutrient that plays an important role in many biochemical reactions in human body and it is mainly obtained from foodstuff. Therefore, its determination in different types of food samples is of special interest for nutritional studies. Usually, the determination of manganese in food is performed by acid digestion followed by detection using a sensitive spectroscopic analytical technique. In the last years, efforts have been made to incorporate new screening or quantitative low-cost analytical tools which are more in line with the green and sustainable analytical chemistry principles. Enzymatic hydrolysis extraction, based on the transfer of analyte from the solid food matrix to an aqueous phase, is a good sample treatment procedure to be explored. In the present contribution, a simple and green ultrasound assisted-enzyme based hydrolytic microextraction (UA-EH-ME) method has been developed and used in combination with total reflection X-ray spectrometry (TXRF) for the quantification of trace levels of Mn in different types of food samples. The main benefits of the proposed approach include the use of a small amount of innocuous reagents (mg level) and the speed of the whole method (30 min). Firstly, a carefully evaluation of the best extraction and measurement TXRF conditions was carried out, including also a -EDXRF study of the shape and element distribution of the deposited residue on the reflector. Different quantification approaches were also tested including internal standardization and empirical calibration using a set of food reference materials. The analytical capabilities of the UA-EH-ME-TXRF method were compared with those associated with other sample treatments, such as suspension and digestion, commonly used in solid sample analysis by TXRF. Finally, the developed method was applied to the analysis of several real food samples and the obtained results were compared with those determined by inductively coupled plasma optical emission spectroscopy (ICP-OES) after microwave digestion of the sample. No significant statistical differences were found between concentrations determined using both methodologies.

Coffee is the most commonly consumed functional beverage and the determination of the mineral composition of coffee beans can be of great interest to both consumers and producers. The mineral composition of coffee differs depending on the geographical origin and type of coffee. Zinc, copper, manganese, and iron are essential micronutrients that play an important role in many physiological processes. Since the human body cannot synthesize them, it is necessary to take them in with food or supplements.[1] In this paper, the composition of zinc, copper, manganese, and iron was determined using FAAS and EDXRF techniques. Although the FAAS technique is often used for the determination of heavy metal content, special attention is now paid to the use of analytical methods that take into account the principle of "green chemistry", i.e. primarily avoidance of time-consuming pretreatment by using concentrated acids for complete sample digestion, low amounts of reagents, as well as a fast, simple and safe procedure. In this sense, EDXRF becomes an important tool for qualitative and quantitative elemental analysis. The main objective of this work is to investigate the advantages and disadvantages of two different analytical techniques (FAAS and EDXRF) and different methods of sample preparation (wet digestion, dry digestion, powder sample, pellet) for the determination of the content of heavy metals (Zn, Cu, Mn, and Fe) in collected coffee samples that are commonly consumed. The specific objective of the study was to determine the possible relationship between the elemental composition of coffee samples and the coffee variety through chemometric data processing involving univariate and multivariate analysis. The univariate analysis showed that copper, manganese, and iron can be considered as the elements that account for the greatest difference between the samples studied. PCA analysis was performed for all coffee samples including the variables (elements analysed), and the loading diagram showed a close relationship between copper and manganese, copper and iron, manganese, and iron, and two significant factors related to the source of the elements in the coffee samples were determined. Cluster analysis was used to divide the samples into groups based on similarity of elemental composition. The resulting dendrogram shows the division of the coffee samples into four main groups, with similarity by coffee type among the samples within the same group, with one exception. A good correlation was found between the elemental composition and the coffee type, and the study shows that the variables studied can be used to distinguish the coffee types in terms of pure coffee and coffee with additives.