640 Hauswirtschaft und Familie
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Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.
The paper broaches the issue of unfair trading practices (UTPs) at the expense of, economically spoken, weaker actors among the food supply chain in context of the EU. For illustrating the concept of UTPs and delivering a theoretical basis for scrutinizing the term of fairness in respective trading practices the paper suggests the three variables 1) bargaining power, 2) market power/anti competitive practices and 3) unequal gain distribution. Subsequently the article presents selected national food-specific legislative based reactions towards UTPs evolved in context of the three variables. Ultimately the paper presents a qualitatively generated hypothesis which presumes that legislative food-specific measurements focussing on protecting suppliers lead to a beneficial monetary share for farmers, by means of influencing the producer price to a monetarily advantageous extent. The hypothesis was generated unprejudiced in the run-up to the paper. The research design which led to the hypothesis mentioned will be presented.