An archaeological and archaeometric study of Late Punic–Roman Pottery from the Tas-Silġ Sanctuary and the Żejtun Villa, Malta

The Early Neolithic (EN) period (5500–4800 BCE) marks the first sedentary settlement of the Maltese Islands by farmers. Despite advances in petrographic and archaeometric studies of pottery in Maltese archaeology, such methods have rarely been applied to Neolithic assemblages. This study addresses the lack of a systematic, published analysis of EN pottery from Malta, focusing on the Għar Dalam and Skorba facies. Thirty-two sherds from the sites of Skorba (Malta) and Santa Verna (Gozo) were analysed using polarised light microscopy, supplemented by additional characterisation techniques (X-ray diffraction, scanning electron microscopy with energy dispersive X-ray analysis). Three distinct fabric groups were identified for each facies, primarily differentiated by the nature of their temper. All primary raw materials appear to have been locally sourced, with no direct evidence of imported pottery. The results suggest similarities in production techniques between the two facies, such as paste preparation, alongside differences – particularly in firing regimes. Finally, the use of calcitic tempers reflects a technological tradition consistent with broader Mediterranean practices during the Neolithic.

Studies on original mortars can greatly assist archaeological interpretations, as elucidating the composition of such mortars gives clues on the origin of raw materials, manufacturing technology, and the construction phases of a site. This article presents the multi-analytical characterisation of 24 mortars and plasters from the Żejtun Roman Villa, Malta, to support archaeological hypotheses on the history of the construction of the site. The samples, belonging to at least three distinct phases included in the stratigraphy of the Żejtun archaeological site, were analysed using polarised light microscopy (PLM), scanning electron microscopy (SEM–EDS), X-ray diffraction (XRD), thermogravimetry (TGA/DSC), X-ray fluorescence (XRF), ion chromatography (IC), and stable isotope analysis (13C and 18O). The assessment of the results through correlations with archaeological evidence identifies five types of mortars with varying degrees of hydraulicity. These are associated with different development phases of the site and distinctive uses and were mainly produced using local resources, except in the Early Roman period when natural pozzolanic raw materials started being used. As there are no natural pozzolans on the Maltese Islands, it is hypothesised that the pozzolanic materials used as aggregate in the mortars were imported to the Islands from neighbouring volcanic regions. This volcanic aggregate was especially abundant in one of the mortar types, which was used mainly as a bedding mortar for floors.

The Chalcolithic period in the Lisbon region, Portugal, is usually divided into three phases chronologically: the Early Chalcolithic, characterized by cylindrical corrugated cups, Full Chalcolithic by so-called acacia-leaf decoration, and Late Chalcolithic by Bell Beaker pottery. The aim of this research is to determine if Chalcolithic ceramic raw materials and production techniques have remained the same over time and whether the pottery is locally produced. Regarding the Lisbon region, 149 ceramic samples from four Chalcolithic settlements (Vila Nova de São Pedro, Penedo do Lexim, Espargueira and Baútas) were evaluated concerning textural, chemical and mineralogical compositions. Textural analysis was performed using optical microscopy, chemical characterization was achieved using micro-energy dispersive X-ray fluorescence spectrometry and mineralogical characterization was undertaken using X-ray powder diffraction and petrographic microscopy as main techniques. Results suggest that production techniques may have remained similar throughout all the Chalcolithic period, with firing temperatures between 700 and 800 °C. Multivariate analysis of results from chemical and mineralogical analyses suggests that multiple sources of raw materials must have been used in the manufacture of the pottery collected at the four Chalcolithic settlements.

The determination of impurities in raw materials intended for the production of pharmaceutical products is important to guarantee the quality of the final product, as well as to avoid damage to health. Metallic impurities can exhibit toxic effects even at low concentrations and so permissible levels are defined by the regulatory agencies and pharmacopeias. However, few methods are presented in official compendia in Brazil. In this sense, fast, sensitive, and precise techniques such as the energy dispersive X-ray fluorescence technique (EDXRF) must be evaluated for the analysis of metals in materials for pharmaceutical use. This way, therefore, there is the need to investigate the presence of contaminants and their concentration levels. The major goal of this research work was to validate a method for using the Energy Dispersive X-Ray Fluorescence (EDXRF) technique to identify and quantify the chemical composition of raw materials and pharmaceutical products. The methodology used was based on the selection of a microcrystalline cellulose matrix, which was spiked with two classes of contaminant elements, Class 1 (Cd, Pb, As, Hg) and Class 2A (Co, V, Ni) as defined by ICH guideline Q3D. The qualitative and quantitative analyses were carried out using the EDXRF technique, which proved to be quite effective and met all the validation parameters required in the mandatory official compendia (Resolution of the Collegiate Board (RDC) of Brazilian Health Regulatory Agency (Anvisa) nº 166, July 24, 2017), such as selectivity, linearity, precision, detection limit, quantification limit and robustness. This study showed that EDXRF can be used as a technique for detection and quantification of elemental impurities belonging to Class 1 and Class 2A.

Applications of Raman spectroscopy in art and archaeology

A unique collection of high-quality late Medieval (15-16th century) glazed stove tiles from the northern part of the Carpathian Basin is of great interest to archaeologists and art historians. Tiles of identical style, motifs and sizes were found in twenty-eight different localities. It is yet to be determined whether these products were made in a single workshop, perhaps in Besztercebánya/Banská Bystrica (in present-day Slovakia) or in multiple workshops in the region. In the present study, the ceramic body of ninety-four tile fragments from six sites (Besztercebánya/Banská Bystrica, Fülek/Fiľakovo and Csábrág/Čabraď in Slovakia; Salgó, Eger and Szécsény in Hungary) was investigated using polarizing microscopy and X-ray diffraction analysis to determine the raw materials and production techniques used. Based on the petrographic characteristics and phase composition the tiles can be classified into four main groups. Samples from the Hungarian sites and Fülek/Fiľakovo – apart from one tile from Eger – are comprised of similar raw materials. Use of intentionally tempered clay is probable in the largest subgroup, in which large angular rock fragments are dominant. By contrast, stove tiles from Besztercebánya/Banská Bystrica contain variable rock fragments (granitoid, quartzite, limestone). Tiles from Csábrág/Čabraď form an independent group due to the presence of large amount of volcanic rock fragments with strong hydrothermal alterations. Based on the preliminary study of the ceramic body the tiles analysed were likely made in several workshops. Nevertheless, the use of identical motifs indicates a possible connection between the workshops.

Chapter 49 - X-Ray Fluorescence Analysis of Trace Elements in Coal and Solvent Refined Coal

The most common scientific analysis of archaeological ceramics aims to determine the raw material source and/or production technology. Scientists and archaeologists widely use XRF-based techniques as a tool in a provenance study. After conducting XRF analysis, the results are often analyzed using multivariate analysis in addition to interpretation and conclusions. Various multivariate techniques have already been applied in archaeological ceramics provenance studies to reveal different raw material sources, identify imported pieces, or determine different production recipes. This study aims to evaluate the results of multivariate analysis in the provenance study of ceramics that belong to three cultures that settled in the same area during various prehistoric periods. Portable energy-dispersive X-ray fluorescence spectrometry (pEDXRF) was used to determine the elemental composition of the ceramic material. The ceramic material was prepared in two different ways. The ceramic body material was ground into powder, homogenized, and then pressed into tablets. After that, the same fragments are polished in suitable places. Quantitative and qualitative analyses were performed on the tablets and polished pieces. The results were subjected to both unsupervised and supervised multivariate analysis. Based on the results, it was concluded that qualitative analysis of the well-prepared shards' surface using EDXRF spectrometry could be utilized in provenance studies, even when the ceramic assemblages were made of similar raw materials.

Elemental analysis using energy-dispersive X-ray fluorescence on extra virgin olive oils and seed oils revealed the presence of two major concentration related clusters, one containing elements of pedological origin, whilst the other consisted of heavy metals. Seed oils were found to contain a higher concentration of titanium when compared to extra virgin olive oils, whilst extra virgin olive oils derived from the Maltese Islands had a significantly higher concentration of barium and phosphorus on using the Kruskal–Wallis one-way ANOVA (p-value < 0.05 for both elements). Application of stepwise linear canonical discriminate analysis proved to be highly superior to PCA, as it was able to distinguish between seed oils from extra virgin olive oils and distinguish between foreign and locally produced extra virgin olive oils.

This report with recommendations is the result of an expert panel meeting on PAT applications in food industry that was organized by the M3C Section of the European Society of Biochemical Engineering Science (ESBES) at the 10th ESBES Symposium. The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for wider applications of PAT in food industry. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided in this report. It concludes with a number of recommendations to facilitate further developments and a wider application of PAT in food industry. Process Analytical Technologies (PAT) [1] (European Medicines Agency EMA-FDA pilot program for parallel assessment of Quality by Design applications; Document EMA/172347/2011) have been extensively discussed in literature, particularly with respect to (bio)pharmaceutical process modelling, monitoring and control [2]. Table 1 provides an historic overview of the PAT development in the context of food applications. Although successful applications within food industry are increasingly being reported [3, 4], the session on PAT in food industries at the ESBES-IFIBiop 2014 in Lille highlighted significant challenges and opportunities for further development in this area. This position paper briefly reviews the current state-of-the-art, industrial needs and opportunities as well as scientific challenges to be addressed in order to extend the use of PAT methodology in the food industries. Currently, quality and safety control are still based mainly on discontinuous analysis with traditional analytical methods in the lab or, at best, at-line measurements. This is no longer sufficient to fulfill the needs of the food industry. Due to higher safety and quality standards and demands and high throughput of production facilities, the number of samples to be analyzed is increasing. Rapid analysis methods and PAT are required to address these needs along the complete production chain leading to a better understanding and control of raw materials, intermediate products in the production process as well as the final products to be packaged and delivered. The goal is to achieve real time analysis in order: • to avoid usage of any out-of-specification raw material and to detect adulteration, substitution, tampering and counterfeiting leading to non-authentic products; • to be able to intervene and stop/change processes in order to secure the target quality; • to assess the final quality to avoid out-of-specification products being packaged and shipped, thus leading to undesirable customer dissatisfaction and costs associated with the resolution of complaints. Over a hundred years ago (on May 22nd, 1913) the first patent on a PAT device ("Pfeiffenanalysator" for measuring the ratio of H2 and N2 gas for ammonia synthesis) was granted to Paul Gmelin from Badische Anilin- und Soda-Fabrik, BASF (Patentschrift Nr. 281157, Klasse 42/. Gruppe 4). Since then, PAT found broad application in chemical industry, which is dominated by highly automated, continuous processes. Today in chemical processes, such as the synthesis of isocyanates, e.g. hexamethylendiisocyanate (HDI), typically 60-130 PAT measurements are collected. In contrast, in sectors like (bio)pharmaceutical or food industry, the application of PAT is significantly less frequent today, especially, in terms of on-line analytics, where the measurement takes place in or close to the production step (or unit operation). One reason for this is the reduced degree of automation of the processes in these industries, which are dominated by unit operations, single production steps consecutively executed after each other, resulting in production batches. Analytics of a production batch takes place mostly off-line in analytical laboratories during hold-up times of the process intermediate between the different unit operations. Regulatory requirements represent an equally important driver for extensive online analytics. With the 2004 FDA's Quality by Design initiative, regulatory agencies demand systematic risk based process development and understanding of the processing space. This also allows for flexible process adjustments within the explored space for producing the desired product quality. In order to achieve the required understanding, more extensive analytical data is required, ideally obtained in-time on raw materials, process intermediates and ideally also on product quality attributes. In food industry, where typically high product titers are obtained, product analytics is sometimes possible by in-line measurements, such as spectroscopy (section 4). In biopharmaceutical processes, product titers are usually very low, the products are proteins with high molecular weight, and quality attributes are challenging to analyse. In these situations, online sampling with automated sample preparation and analytics may open up a solution [5]. But, identifying and implementing analytical methods, which are fast enough for delivering results on product quality attributes (e.g. protein glycosylation) in time, during the unit operation for allowing process modulation, remains a challenge. The overall typical goals in industry are high safety, high and stable product quality, high yield, low consumption of resources (materials, energy, room, time, and people), reduced influence of variability in raw materials as well as an increased shelf life of products which holds also in food processes. However, compared to the chemical and biopharmaceutical industries, food industry has to deal with certain characteristics, which renders PAT a formidable challenge in this context. The characteristic features in food industry are: • Raw materials are not pure substances: they are complex combinations of pure substances with varying compositions • Raw materials are soft, variable size, fragile and slippery • Physical properties of raw materials depend heavily on temperature, pressure, moisture and harvesting, and storage conditions • Raw materials undergo usually a phase transformation during processing and hence change their physical properties during processing • Micro processes (physical, (bio)-chemical, microbiological) are frequently not known • Highly perishable products. Here the challenge is to produce good quality, which maintains for a long time (shelf life) • High demand for hygiene These characteristics lead to a challenge for the application of sensors in the food industry. For important quality and process variables, such as sensory assessment, micro flora or spoilage, reliable and robust sensors are not yet available. Moreover, the sensors which are available are typically used only in isolated applications and frequently provide insufficient reliability. They are often not integrated in a common data management infrastructure. The materials of construction do not always consider suitability of contact with food and the solutions developed within advanced research projects frequently demand high care and maintenance. One reason why PAT is not as common as in other areas is that in food industry knowledge from various subject areas is necessary (such as physics, chemistry, biology, mathematics, informatics, engineering, nutritional science). For unit operations mathematical models are available in principle, but frequently they are too complicated to adapt. One of the biggest challenges in food industries is the dynamic nature of the processes. Changes in geometry, porosity, microstructure, solubility as well as mass and energy transfer coefficients must be addressed. Mostly gradients of temperature and moisture have to be considered as well as changes in kinetic parameters during process run. End product qualities like color, smell and 'brokens' also strongly influence the process. Therefore, food processes can be considered to be significantly more complex then chemical and bio-pharmaceutical processes. To achieve more stable processes PAT applications are necessary to check the quality of the raw and processed materials and their relationship to each other. If the quality is changing, then control actions resulting in online parameter changes are required in order to maintain constant product quality. Measurement systems are required, which guarantee that the process is in accordance with recipe and formulation. Furthermore, disturbances must be compensated for using control actions to reduce process variability and to conform to food regulations. As Glassey [6] argues, PAT methodology can aid in product design and testing as well as in ensuring full compliance with the HACCP and ISO 22000:2005 requirements during processing. As demonstrated below, PAT methodologies have the potential to aid the identification of critical control points and their critical limits, their effective monitoring and control, but also effective communication with suppliers and customers. Documentation which requires measurements of important variables is especially important so that traceability can be guaranteed. Here PAT has to deliver the corresponding measurement systems. Clearly a high demand for PAT in food industry is evident as is the need for further developments in the science and technology that help address the specific challenges posed by the characteristics of the food industry highlighted above. The complexity of raw materials that are typically soft and easily damageable is arguably one of the major scientific and technological challenges in food industry. During storage and processing the quality of such raw materials can decline due to oxidation processes, pressure and temperature effects. In addition, the visual impression of the final product (i.e. its appearance) is much more important than in other industries due to the fact that this will influence the purchase decision of the consumer. During the whole processing from raw material to the storage of the final product, hygiene is of utmost importance. Therefore non-invasive sensor systems are required in these applications. Measurement systems based on optical principles are particularly suitable from this perspective. Such measurement systems, including near infrared (NIR), Raman and fluorescence spectroscopy as well as computer-assisted image-based systems, potentially have a number of advantages in food process supervision and automation. Spectroscopic methods and imaging devices are well suited for PAT purposes because they are fast, non-destructive, provide multiple chemical information, allowing remote in-process analysis via fiber optics or instruments mounted directly on-line. Fluorescence spectroscopy is the most sensitive spectroscopic technique. Recently, many applications have been developed using fluorescence techniques [3, 7, 8]. Raw materials, the supervision of processing as well as product quality and the contamination of the equipment can be monitored by fluorescence. For example, Everard et al. [3] presented a method for detection of fecal contamination on spinach leaves. They coupled three hyperspectral imaging (HSI) configurations with two multivariate image analysis techniques and compared fluorescence imaging in the visible region with ultra violet and violet excitation sources, and reflectance imaging in the visible to near-infrared regions. They showed, that both fluorescence configurations had 100% detection rates for fecal contamination up to 1:10 dilution level and violet HSI had 99% and 87% detection rates for 1:20 and 1:30 levels, respectively. Everard et al. emphasized that on-line detection of fecal contamination on leaves has the potential to reduce the cases of food borne illnesses and their associated costs. A similar approach is presented by Lee et al. [7] where bovine faeces on Romaine lettuce and baby spinach leaves were investigated. They pointed out that two-band ratios using bands at 665.6 nm and 680.0 nm for lettuce and at 660.8 nm and 680.0 nm for spinach effectively differentiated all contamination spots applied. Grote et al. [8] described a fluorescence measurement technique to monitor a sourdough fermentation process. For the prediction of pH value and acidity during rye sourdough fermentations they applied partial least squares regression and principal component regression models for prediction and compared them with an evaluation where principal component analysis was combined with artificial neural networks. Depending on process operation and evaluation technique the average percentage root mean square errors of prediction for pH values were between 2.5 and 5.1%. For the prediction of the acidity level, the best results were between 6.0 and 8.1%. Liu et al. [9] used the Hoffman reaction to convert acrylamide to a compound which shows strong fluorescence emission at 480 nm. They showed good correlation of acrylamide in the range of 0.015 μg/mL to 20 μg/mL. Using this technique the food security will be increased. A fluorescence imaging device to detect deli residues on deli slicers were used by Beck et al. [10] processing four cheeses and four processed meats. The authors suggested that a fluorescence imaging device can be applied for routine use even in delicatessens. Fig. 1 clearly shows that the application of fluorescence for the monitoring of food processes increased from less than 10 before 2003 to more than 60 a year since 2013. This figure includes all papers from a search containing the key words "Food" and "Fluorescence". Although most of these papers describe laboratory applications rather than industrial PAT applications, they indicate potential future applications in this technology in food industry. Number of published papers obtained from the Scopus database searching for the words "Food" and "Image analysis" or "Raman" or "Near infrared spectroscopy" or "Fluorescence spectroscopy". Raman spectroscopy can only be applied if no fluorescence occurs in the corresponding excitation range. He et al. [11] applied the surface enhanced Raman scattering spectroscopy to detect banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. They concluded, that Raman spectroscopy and chemometric evaluation techniques can be used to identify banned food additives to ensure food safety. Ilaslan et al. [12] presented a method based on Raman spectroscopy to provide a rapid method for evaluating the quantitative analysis of glucose, fructose, and sucrose in soft drinks. Wang et al. [13] applied a Raman spectrometer as a process analyzer to monitor the wine fermentation. They demonstrated that sugar, ethanol and glycerol can be measured on-line with high correlation (higher than 0.98) to the HPLC reference measurements. Nache et al. [14] investigated Raman spectra from pork meat to monitor the early postmortem lactate accumulation and pH decline. They suggested that the locally weighted regression applied to the standard normal variate (SNV) normalized Raman spectra provide one of the most accurate and robust models with a cross-validated coefficient of determination (r2cv) of 0.97 for pH and lactate, a cross-validated root mean square error (RMSECV) of 4.5 mmol/kg for the lactate prediction and 0.06 pH-units for the pH prediction. These results demonstrate significant potential of combining chemometrics and Raman spectroscopy for on-line meat quality control applications. Fig. 1 shows the number of papers recorded in Scopus (search words "Food" and "Raman"). Compared to fluorescence, the number of papers describing Raman spectroscopy is twice as high in recent years with a significant rate of increase, demonstrating the increasing interest in this method for food process monitoring. Computer vision systems enable one of the main aspects of consumer preference – the appearance of a product – to be inherently considered. Therefore, computer vision systems for the supervision of food processing also gained importance over the years. An overview of several examples is given by Sun [15]. Especially the supervision of food drying processes is discussed by Aghbashlo at al. [16]. They pointed out that there is a large unexploited potential in the image data captured during various food processing operations. More informative feature extraction algorithms or novel pattern recognition procedures have to be developed. Paquet-Durand et al. [4] described a system for the supervision of the baking process. They demonstrated, by using the Viola–Jones-algorithm as well as neural networks, that the pastry can be identified and the volume increase as well as color development can be monitored. To detect defective apples Zhang et al. [17] used a computer vision system, which was combined with an automatic lightness correction system. For 160 samples they showed a 95% overall detection accuracy. For the evaluation they used a weighted relevance vector machine classifier. Fig. 1 shows the number of papers over time from Scopus searching for "Food" and "Image analysis". Here the applications start earlier compared to the spectroscopic methods, the number of applications per year is higher than in the case of spectroscopic applications; except in the year 2014, where more reports of Raman applications were published. Moreover imaging systems using visible and NIR region are available to perform quality checks based on the spectroscopic information derived from each point in the image. One example is the on-line analysis of the widely varying fat distribution in salmon for sorting purposes [18]. Near infrared (NIR) is a well-established method for rapid analysis of food raw materials and products [18], either on-/in-line, at-line or off-line. In the lab or at-line multiple components such as fat, protein, moisture and many more, can be analyzed without any sample preparation in all kinds of liquid, solid and semi-solid samples. Raw materials can be verified for identity and further characterized regarding composition already in the goods reception. The composition, freshness and adulteration of edible oils [19], e.g. olive oil [20], can be analyzed rapidly before a truck is unloaded. Many other sample types like meat, grains, flour, dairy products [21] and others can be analyzed. Even mixtures like vitamin premixes can be analyzed to make sure that the correct material was delivered and can be used in production. There are several approaches and technologies for PAT available, ranging from simple filter based devices over dispersive diode array spectrometers to Fourier-Transform (FT)-NIR instruments. Samples can be analyzed in-line by fiber optic probes or by contactless systems (Fig. 2). Smaller instruments can be directly attached to pipes, chutes or in other installations. A) NIR reflection probe in a fluid bed dryer. (B) Probe head for contactless measurement of grain and other solids. Applications cover a broad range from a simple monitoring of moisture content of a product on a conveyor belt to more complex control situations. In dairy industry there are demands to control milk powder production by monitoring the feed of the spray tower and the powder after fluid bed drying. Other important production steps to control are the standardization of milk, cream, whey and concentrates [22]. In frying processes with big volumes of oil an in-line monitoring of the oil quality with regards to acid value, anisidine value and the content of polar and polymerized components [23] are of particular interest. Another example of high volume processes where monitoring of protein, moisture and ash is important is the milling of grain to flour and the production of cereals of any kind. Finally there are more and more fermentation processes controlled by in-line NIR to optimize the conditions and follow the feeding and consumption cycles during biomass buildup [24]. In 2013 roughly 120 papers were published, however the recent increase is not as steep as in the case of Raman spectroscopy (Fig. 1). The economic benefits due to PAT can be attributed to higher product quality and yield as well as decreased product variation. This will reduce the overall production costs significantly and increase competitiveness. Furthermore, the knowledge and understanding of the process will increase. However, the level of automation in industrial food processes is significantly lower compared to the chemical and pharmaceutical processes. This is partly due to the complexity of food processes. On the other hand, the lack of reliable sensor systems to determine important process parameters and variables contributed to this in the past. Sensors represent a fundamentalpart of all automation systems, although they only represent a part of the requirements for wider application of PAT in food industry. The areas of further development and recommendations enabling more extensive use of the PAT within this industry also include: • Further industrial case studies and wider dissemination of the positive impact of reliable sensor technology in raw material and product quality as well as process monitoring. • Operational and maintenance requirements of sensor technologies will remain a significant aspect of future sensor technology uptake in this industry. • Data management and analysis software will play a significant role in extending the PAT application within the food industry. Platform solutions, preferably supporting wireless data transfer, at competitive pricing levels are required. • Cost benefit analysis studies are particularly important given the business drivers of this industry. • The impact of PAT is likely to be more pronounced in the new processes, although it is important to continue to encourage the application of PAT approaches within established food processes to enhance the process economics and compliance with the standards ensuring safe food supply chains. • PAT can significantly contribute to the stringent traceability and documentation requirements, although issues of compatibility and data standardization will impact upon data management systems employed by the companies within the supply chain. The authors declare no financial or commercial conflict of interest.

The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO2 and CaCO3 concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples.

A mass of Longquan porcelain shards carved with ‘Guan’ or the dragon patterns were unearthed in the early Ming Dynasty layer of the Fengdongyan kiln site at Dayao County. These celadon shards were fired in the Hongwu and Yongle eras of the Ming Dynasty. In order to research the raw materials and firing technology of the imperial porcelain, 85 typical shards were analysed by energy‐dispersive X‐ray fluorescence (EDXRF). The results indicate that the contents of TiO2 and Fe2O3 in the body vary in the Hongwu and Yongle eras. Compared with Longquan glazes in the Southern Song Dynasty, the average values of K2O, Fe2O3 and TiO2 are higher, but that of CaO is lower in early Ming imperial porcelain glazes. Principal components analysis (PCA) shows that different degrees of elutriation of the same raw materials are the main reason for this difference in the Hongwu and Yongle periods. However, the raw materials of imperial porcelain glazes show no obvious changes and have inherited the earlier tradition. The production and firing technology of imperial porcelain reached a higher level and had not declined in the Early Ming Dynasty.

Sulfur dioxide is used to ensure the safety of fruit raw materials and semi-finished products used for the production of confectionery. This preservative has allergenic properties. In accordance with the Technical Regulations of the Customs Union TR CU 022/2011 "Food products in terms of their labeling" the content of sulfur dioxide must be specified when labeling confectionery products, if its content exceeds 10 mg per kg. The definition of this preservative in raw materials, semi-finished products and confectionery in accordance with the current GOST 26811 “Confectionery. The iodometric method for determining the mass fraction of total sulfuric acid is often difficult because of the complexity of confectionery products, the formation of intensive staining of the solutions under study and the ability of sulfur dioxide to react with other components of the objects under study. Therefore, the task of developing methods with the optimization of the sample preparation stage of the samples under study, which allow one to fully determine sulfur dioxide in confectionery products, semi-finished products and raw materials, is relevant. The purpose of this work was to develop methods for determining sulfur dioxide in raw materials, semi-finished products and various names of confectionery products, as well as assessing the impact of these types of raw materials and semi-finished products on the content of sulfur dioxide in confectionery products. The results showed that sulfur dioxide is present in many raw materials. In mg per 1 kg of sugar in white sand from 1 to 9, molasses from 25 to 52, wheat flour from 9 to 15, starch from 3 to 17. In fruit raw materials from 5 to 545 mg per kg. In confectionery: marshmallow and marshmallow from 8 to 29, gingerbread from 6 to 25, biscuits from 0 to 16, chocolate from 8 to 13. Based on the data obtained, it can be concluded that the content of sulfur dioxide in raw materials and semi-finished confectionery products is a very wide range, which makes it necessary to control the quality of all raw materials entering the production. Sulfur dioxide, used as a preservative for fruit raw materials, can significantly increase the content of this allergen in confectionery made using such raw materials. Ensuring the level of sulfur dioxide less than 10 mg / kg is achieved using raw materials with low content of sulfur dioxide and technological methods.

In present study, the structural and trace element were preliminary survey the potentiality to use as filler material for produced the biocomposite films. The first raw material is chitosan and the second raw material is chicken eggshell. Both raw material were investigated the structural and morphological with X-ray Diffraction (XRD) and Scaning Electron Microscope (SEM), respectively. While, Fourier transform infrared spectroscopy (FT-IR) the Energy Dispersive X-ray Fluorescence (EDXRF) were used to investigate functional group and trace element, respectively. The result show that, both material have crystalline structural. The chicken eggshell have calcite structure while the chitosan have amorphos-crystalline structure. The chemical composition in both material compose of P, Ca, Fe similarly but the concentration have been different.

The performance of solid wood and wood-based materials is generally increased by applying chemical and physical treatments; however they may compromise the recyclability of the products reaching their end life. Among the wide range of chemicals applicable to wood-based materials some are source of concern and a few (e.g. Chromate Copper Arsenate) are considered to be dangerous waste. This work focuses on the elemental analysis of wood residues developing a survey on contamination levels of wooden materials, intended either for quantifying wood not polluted that may be re-used as “virgin” raw material, or as a preliminary step of an automated sorting method using Energy Dispersive X-ray Fluorescence (ED-XRF). The analyses are referred to elemental concentration reported for natural solid wood and to those thresholds set by law. In a sampling carried out mainly in north Italy, 336 wood waste specimens were collected, classified using descriptor fields and analyzed with ED-XRF technique, implementing a validation method for short scan time. Roughly 84 % of the specimens comply with the EU decision (Ecolabel) on heavy metals, ranging from 94 to 63 % (fibreboard, hardwood, softwood, plywood, particleboard recycled and particleboard). Most detected heavy metals originate from furniture and building materials, whereas packaging and specimens of unknown origin presented no major concern. Very high concentrations of Cl, Pb and Cr were found. The technique has been proved to be effective with certain limitations. Great attention should be taken to manage wood waste critically polluted in some cases and possible source of clean raw material in others.