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Papetti P., Carelli A. (2013): Composition and sensory analysis for quality evaluation of a typical Italian cheese: influence of ripening period. Czech J. Food Sci., 31: 438-444.Caciottina massaggiata di Amaseno, a typical dairy product of the Lazio Region prepared from buffalo milk, was analysed for moisture, fat, protein, pH, and acidity content at different storage (0, 3, 6, and 9 months after production). Cheeses were also evaluated for various sensory attributes (taste, flavour, texture, and overall acceptability) during storage. We evaluated the flavour profile and acceptability of Caciottina cheeses of varying maturity using a descriptive analysis. Descriptive sensory profiles of selected cheeses were determined using a trained panel (n = 12) and an established cheese flavour sensory language. In quantitative descriptive analysis a trained panel rated the cheeses for colour, odour, taste and texture. In the affective tests the panellists evaluated the samples for overall quality. The results proved that months of production had significant effects on the sensory quality of the cheeses.

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory–based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity.