Sensory evaluation can provide integrated, direct measurements of the perceived
quality of food products. However, a sensory panel is subjective and suffers from
inconsistency and inaccuracy. In this paper, we propose a sensory evaluation simulation
model for Longjing tea （a Chinese brand of green tea）. The physiochemical quality
indicators of Longjing tea were determined by instrumental analysis, including color,
aroma, and taste. Meanwhile, the sensory quality of the tea was evaluated by an expert
sensory panel. An artificial neural network was conducted to approximay predict
sensory evaluation scores on the basis of physiochemical data. The results showed
that physiochemical factors, including hue, fluorescence peak 5, hue chromascale, b,
L, 3-（methylthio） propionaldehyde, α-terpineol, linalool, dimethyl sulfide, total aroma
value, caffeine, quinic acid, theanin, gallic acid and total catechins were best correlated
with sensory evaluation scores. Furthermore, physiochemical features that were chosen
according to important factor weights were used to classify Longjing tea into two grades.
Experimental results demonstrated that instrumental analysis could be complementarily
used in the evaluation and control of sensory quality by establishing a reasonable
sensory-instrument correlation and human-simulated predictive model.
