Abstract:

In order to explore the applicability of electronic nose technique for rapid and non-destructive evaluation of
kiwifruit quality, the volatile compounds of “Qinmei” kiwifruit during cold storage were studied by electronic nose. Multiple
linear regression (MLR), partial least-squares regression (PLS) and back-propagation (BP) network were applied to predict
the firmness, soluble solid content (SSC) and pH of kiwifruit based on the signal of electronic nose. The results showed that
the response values of sensors S1, S2, S3, S4, S7, S8, S9 and S10 were relatively high and changed significantly during 45
days of storage (P ＜ 0.05). In addition, aromatic benzene, nitrogen oxide, ammonia, hydrogen, hydrogen sulfide, ethanol,
organic sulphur compounds and aromatic alkane also exhibited a significant change during cold storage. Linear discriminant
analysis was able to better distinguish among different storage periods of kiwifruit than principal component analysis.
PLS, MLR and BP network were able to predict the firmness, soluble solid content and pH of kiwifruit during cold storage.
However, BP network led to more precise predictions than PLS and MLR. The results indicate that it is possible to use this
non-destructive technique for measuring quality characteristics of kiwifruit, and electronic nose technique provides a method
for rapid and non-destructive evaluation of kiwifruit quality.

Key words: kiwifruit, electronic nose, solid soluble content, firmness, pH