| Literature DB >> 33831826 |
Fang Yang1, Honghui Guo2, Pei Gao2, Dawei Yu2, Yanshun Xu2, Qixing Jiang2, Peipei Yu2, Wenshui Xia3.
Abstract
Chinese mitten crab (Eriocheir sinensis) needs sensory evaluation for grading. This study compared data mining (DM) and sensory panel evaluation (SPE), using data visualization (DV) and quantitative descriptive analysis (QDA), respectively. Results showed that Yangcheng Lake Crab (YLC) was the most welcomed for "umami" and "sweet" according to DV; and QDA (7-scale) showed similar results of the highest "aroma-sweet" (Average Score 4.5) and "taste-umami" (Average Score 4.6) in YLC. The difference was that, DV was fast based on big data (1.4 million words); while QDA quantified detailed attributes (principle components > 85.3% averagely) based on highly-trained sensory panel of good distinguishing- and repeating- ability that F value showed 76.4% of all attributes > 5% for panelist averagely, and mean square error < 0.500 except one panelist. In conclusion, DM was quick but qualitative; while SPE was laborious but informative.Entities:
Keywords: Big data; Chinese mitten crab; Quantitative descriptive analysis; Trained sensory panel; Web crawler
Year: 2021 PMID: 33831826 DOI: 10.1016/j.foodchem.2021.129698
Source DB: PubMed Journal: Food Chem ISSN: 0308-8146 Impact factor: 7.514