The objective of this study was to investigate the application of different instrumental methods for measuring the hardness of fresh and cooked parsnip. Research was carried out on two previously prepared commercial parsnip samples, both fresh and cooked for 5, 10, 15 and 20 minutes. Hardness was measured using a TA.XTPlus Texture Analyzer, equipped with different fixtures and load cells. Penetration test was performed on the parsnip discs, while the instrument was equipped with a 5 kg load cell and a 2 mm diameter stainless steel flat cylinder probe. Shearing test was conducted on the whole parsnip roots with a 30 kg load cell and Warner-Bratzler (WB) flat knife blade. The obtained values of the coefficients of variation for both methods indicated that these methods are more suitable for measuring the hardness of fresh parsnip. The results of analysis of variance showed that penetration test generally expressed better differentiation between the samples A and B. The greatest change of firmness during cooking was observed after the first 5 minutes, as measured by both methods. Parsnip firmness progressively decreased with increasing cooking time, with less pronounced differences between the cooking times obtained by shearing test. Further experiments should include more representative parsnip samples and more repetitions within the sample in order to make general conclusions. All results should be verified with sensory evaluation of parsnip hardness by an expert panel or a panel of trained assessors.

Sensory analysis is the best mean to precisely describe the eating quality of fresh foods. However, it is expensive and time-consuming method which cannot be used for measuring quality properties in real time. The aim of this paper was to contribute to the study of the relationship between sensory and instrumental data, and to define a proper model for predicting sensory properties of fresh tomato through the determination of the physicochemical properties. Principal Component Analysis (PCA) was applied to the experimental data to characterize and differentiate among the observed genotypes, explaining 73.52% of the total variance, using the first three principal components. Artificial neural network (ANN) model was used for the prediction of sensory properties based on the results obtained by basic chemical and instrumental determinations. The developed ANN model predicts the sensory properties with high adequacy, with the overall coefficient of determination of 0.859.

ABSTRACT:This work was focused on the performance of trained and untrained panel in evaluating the texture of nine commercially produced wheat spaghetti. Several sensory methods were applied in order to investigate the performance of different panel groups. In order to avoid the loss of information obtained by non-parametric methods, data were scaled according to contingency tables. This analysis showed that significant differences existed between the two panels for the given products. On the basis of these results, it can be concluded that the used panels cannot be a good alternative to each other in providing sensory texture profiling of commercial spaghetti, except in the case when the properties of spaghetti were evaluated using the control sample.

Quality parameters and the possibility of successful placement of buckwheat-enriched wheat bread on the national market are presented in this paper. Analysis of the market position of buckwheat-enriched wheat bread includes demands, offer and competition. Elements that affect the overall retail price of buckwheat-enriched wheat bread are given in details, along with SWOT analysis and marketing plan including target market, market supply and product marketing mix. According to all performed analyses it could be concluded that this product should be positioned on the national market, especially for people with special needs and requirements.