Angelo Fabbri

Evaluation of coffee roasting degree by using electronic nose and artificial neural network for off-line quality control.

UNLABELLEDnAn electronic nose (EN) based on an array of 10 metal oxide semiconductor sensors was used, jointly with an artificial neural network (ANN), to predict coffee roasting degree. The flavor release evolution and the main physicochemical modifications (weight loss, density, moisture content, and surface color: L*, a*), during the roasting process of coffee, were monitored at different cooking times (0, 6, 8, 10, 14, 19 min). Principal component analysis (PCA) was used to reduce the dimensionality of sensors data set (600 values per sensor). The selected PCs were used as ANN input variables. Two types of ANN methods (multilayer perceptron [MLP] and general regression neural network [GRNN]) were used in order to estimate the EN signals. For both neural networks the input values were represented by scores of sensors data set PCs, while the output values were the quality parameter at different roasting times. Both the ANNs were able to well predict coffee roasting degree, giving good prediction results for both roasting time and coffee quality parameters. In particular, GRNN showed the highest prediction reliability.nnnPRACTICAL APPLICATIONnActually the evaluation of coffee roasting degree is mainly a manned operation, substantially based on the empirical final color observation. For this reason it requires well-trained operators with a long professional skill. The coupling of e-nose and artificial neural networks (ANNs) may represent an effective possibility to roasting process automation and to set up a more reproducible procedure for final coffee bean quality characterization.

Freshness assessment of European hake (Merluccius merluccius) through the evaluation of eye chromatic and morphological characteristics

The most commonly used method for fish freshness determination is the sensory inspection; alternative sensory methods such as the Quality Index Method (QIM), based on the significant sensory parameters of one specific species, have been recently suggested. Considering that most of the sensory parameters are based on chromatic and morphological visual impression, the set-up of an objective method using computer vision techniques is very promising. The objective of this research was to characterize the changes in eye chromatic and morphological characteristics of European hake (Merluccius merluccius) during 13u202fdays of storage on ice, using a tailored computer vision technique and a 3D scanner. Results obtained by multivariate statistical analysis of the colour spectra of eye images and by the eye concavity index using a 3D scanner permitted to estimate fish unacceptability after 7u202fdays of storage, in agreement with results obtained by QIM sensory analysis. Moreover, 1H NMR was used to evaluate the production of trimethylamine (TMA) and the Ki index, confirming a good correlation with eye chromatic and morphological features. This preliminary study showed the high potentiality of the developed method as a non-destructive technique for raw fish freshness characterization / prediction, being a promising approach to create a robust portable instrument for the evaluation of fish freshness in real transport and marketing conditions.

Numerical models of mass transfer during ripening and storage of salami

Ripening, in the dry sausages manufacturing process, has an influence over the main physical, chemical and microbiological transformations that take place inside these products and that define the final organoleptic properties of dry sausages. A number of study about the influence of ripening conditions on the main chemical and microbiological characteristics of dry sausages is available today. All these studies indicate that the final quality and safety standards achieved by the sausage manufacturing process can be considered to be strictly dependent from the specific ripening conditions. The water diffusion inside a seasoned sausage is surely an aspect of primary importance with regard to the quality of final product. As a consequence the aim of this research was to develop two parametric numerical models, concerning the moisture diffusion physics, describing salami ripening and storage. Mass transfer equations inside the sausage volume were numerically solved using a finite element technique. A first model describes diffusion phenomena occurring inside the salami and the exchange phenomena involving the surface of the product and the environment. After the ripening, the salami are stored in waterproof packaging, consequently an additional model able to describe also the evaporation and condensation phenomena occurring between the salami surface and the air in the package, was developed. The moisture equilibrium between salami surface and conservation atmosphere is mainly ruled by the temperature changes during storage. Both models allow to analyze the history of the moisture content inside the salami and are parametrised on product size and maturation/storage conditions. The models were experimentally validated, comparing the numerical outputs of the simulations with experimental data, showing a good agreement.