Comparison of methodological proposal in sensory evaluation for Chinese mitten crab (Eriocheir sinensis) by data mining and sensory panel. (15th September 2021)

• Record Type: Journal Article
• Authors: Yang, Fang; Guo, Honghui; Gao, Pei; Yu, Dawei; Xu, Yanshun; Jiang, Qixing; Yu, Peipei; Xia, Wenshui
• Is Part Of: Food chemistry. Volume 356(2021)
• Publication Date: 2021-09-15
• Subjects: Chinese mitten crab -- Web crawler -- Big data -- Trained sensory panel -- Quantitative descriptive analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.129698
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A novel way of big data mining by web crawler was used for sensory evaluation. A sensory lexicon of Chinese mitten crabs was established. The sensory attributes of Chinese mitten crabs were mainly umami, sweet and fatty. Big data mining and sensory panel were complementary to each other. 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.