Evaluation of potential application of artificial intelligent control aided by LF-NMR in drying of carrot as model material
Qing Sun, Min Zhang, Arun S. Mujumdar
Abstract
As a new sensing technology for rapid nondestructive detection, low field nuclear magnetic resonance technology (LF-NMR) is being widely applied in many fields of food science and technology. The purpose of this study was to evaluate the potential of the LF-NMR technique to detect in the endpoint in drying off carrots as a model food material. Taking relative standard deviation (RSD) as the evaluation index, the research examined the effect of sample quantity and moisture content (MC) on the precision of detection. The results showed that adjusting the number of scans (NS) during drying and sample quantity can help improve the detection performance of LF-NMR. The MC level of 20% was observed to be the key point for adjustment of the detection parameter while the sample quantity at the drying endpoint is recommended to be 1.0-1.5g to achieve good precision.