This study is one of the first using archaeometric data to evaluate Early Modern glazed ceramics from Prague. The study of Early Modern ceramic production in the Czech Republic, represented mainly by archaeological finds, is one of the rapidly developing topics. It is of course problematic to create an overall view or conclusion about glazed production in Central Europe because these sets are randomly examined. Present-day research is also focused on identifying fabrication centres of workshops and how they developed moreover, emphasis is placed on the manufacturing process or firing conditions. Glazed ceramics are often studied from the perspective of decoration techniques, the macroscopic description of materials or the identification of pigments. It was proved that all the glazes were medium- or high-lead content glazes and were coloured with ionic pigments.
The firing temperature of most of the vessels did not exceed 1000 ☌. The results of the performed analyses determined that the studied vessels were manufactured from raw materials with a high content of a plastic component and that the raw materials did not change significantly during the period from the 15th to 18th centuries. Based on the similarity of ceramic bodies and glazes, it was proved that the three technical ceramic vessels were made in the same workshop and were parts of one distillation apparatus. Since these vessels represent a rare finding, they were subjected to a detailed investigation. The archaeological finds include three technical ceramic vessels (a rectifier, a bowl and a jar), which together could have formed a distillation apparatus. The research was conducted with an emphasis on the context of the original use of the ceramic artefacts and the environment of the waste pits from which they were excavated. Attention was also paid to the identification of defects and corrosion products of the glazes. The main aim was to characterise ceramic materials and glazes used over two centuries. The set of Early Modern Age archaeological glazed ceramics contained tableware, kitchenware and
XLSTAT JAR SERIES
In addition, the significance of the penalties can be tested by performing a simple t-test comparing the liking scores from consumers in the TL or TM category to those of consumers in the JAR category.A series of scientific methods (X-ray analyses, optical and electron microscopy, Raman spectroscopy and thermal analyses) was used to research the ceramic bodies and glazes of forty vessels from Renaissance Prague. An example of a penalty analysis graph is provided below. The net penalty is obtained by multiplying the TL or TM mean drop by the proportion (not the percentage) of consumers who scored the attribute TL or TM. Some users also calculate what is known as the net penalty. A penalty is usually not computed if the % of consumers in the TL or TM categories is less than 20%. Results are most often represented graphically by plotting the penalty or mean drops against the percentage of consumers for all the JAR scale attributes of the product. The percentage of consumers in each of the 3 categories is calculated and corresponding mean liking ( L TL, L JAR, L TM) scores for the “Too Little” (TL), “JAR” and “Too Much” ™ categories estimated. Consumer are first grouped in one of three groups depending on the response given to a JAR attribute (i.e. PA is usually a product specific analysis.
The procedure for PA is summarized as follows: It can be a graphical technique to reveal the possible penalty paid by the product in terms of reduced overall liking for not being “just about right” on a characteristic and the penalty is often called mean drop on overall liking. PA provides a prioritized list of critical product characteristics that are most-penalizing product performance. For the latter, The 9-point Hedonic Scale is frequently used. In addition, liking data needs to be collected. The scales used to acquire the data are known as Just About Right Scales. Penalty analysis (PA) has been used extensively by practitioners in the industry to assist in identifying decreases in acceptability associated with sensory attributes not at optimal levels in a product.