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.