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Manual labour in slaughterhouses is hazardous work. Workers suffer from injuries and occupational illnesses resulting from repetitive movements with sharp knives. There is a need for a robotic tool which can perform versatile tasks with a high level of precision. This knife must be able to imitate the same primary cuttings of a professional butcher and produce meat products which are acceptable to the end-market. This paper reports the results of a world-wide assessment of the fresh pork meat cuts as a performance indicator of knives specifically adapted for automated operation. These knives included Victorinox knife, bespoke double bladed Uddeholm knife, vibrating knife and novel smart knife with built-in sensing mechanism that detects in real time the contact with meat and cut depth. Physical appearances of cuts were assessed anonymously by independent responders with different backgrounds. All knives were deemed acceptable in terms of cutting quality. There was also no discernible difference of opinion between manual and robot cuts. This indicates that the new knife for robot-assisted cutting is acceptable by market.

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.

A screening experiment consisting of 18 batches of meat loaves were made using a fractional factorial design with six factors including two production replicates i.e. testing of several factors in relatively few trials. The rancidity was detected by TBARS, volatile compounds by GC-MS and sensory analysis by a sensory panel. The results were presented graphically as formal statistical testing is limited. To do this the multivariate results from GC-MS and sensory analyses were made univariate by statistical tools. Pre-salting meat and storing it in frozen air, will result in rancid meat, and regardless pre-salting or not ascorbic acid will act as a pro-oxidant when added into already rancid meat and stored frozen. The three methods used to detect rancidity in meat loaves resulted sometimes in discrepancy in the results. Further research has to be done to verify if these findings were due to development of different oxidation products due to the combinations of factors or if these were outliers. [ABSTRACT FROM AUTHOR]