Formation and prediction of heterocyclic amines and N-nitrosamines in smoked sausages using back propagation artificial neural network

BACKGROUND

Heterocyclic amines (HAs) and N-nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP-ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat-to-lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP-ANN model.

RESULTS

With an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP-ANN model was 5-11-2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance.

CONCLUSION

The contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP-ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network

The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.

A procedure for the sensory evaluation of Salama da sugo, a typical fermented sausage produced in the Emilia Romagna Region, Italy

BACKGROUND

Salama da sugo is a fermented sausage from the Ferrara tradition (Italy, Emilia-Romagna region), subjected to a long ripening period (4-6 months) and characterised by a high content of wine and spices in the mixture. It can be consumed after cooking and it is served with its sugo, i.e. the liquid extracted by cooking process. The aim of this work was to set up a method for the sensory profile of Salama da sugo as a request for Protected Geographical Indication (PGI) has been made to the European Commission.

RESULTS

A system of sample preparation that provides for the precooking in an autoclave and cooking in boiling water was set up. A specific sheet for sensory evaluation of Salama da sugo has been created and reports 23 descriptors identified during the lexicon development. The differences in sensory profile of four samples were evaluated and principal component analysis highlighted the more discriminant parameters, i.e. odour intensity, wine odour, spicy aroma, fat/lean connection, sweet, bitter, juiciness, chewiness and pricking.

CONCLUSIONS

The proposed method allows the standardisation of sensory profiling of Salama da sugo, and is also to verify compliance with the specification PGI.