Abstract
In this study, electronic nose (EN) combined with a 433 MHz surface acoustic wave resonator (SAWR) was used to determine Kiwi fruit quality under 12-day storage. EN responses to Kiwi samples were measured and analyzed by principal component analysis (PCA) and stochastic resonance (SR) methods. SAWR frequency eigen values were also measured to predict freshness. Kiwi fruit sample's weight loss index and human sensory evaluation were examined to characteristic its quality and freshness. Kiwi fruit's quality predictive models based on EN, SAWR, and EN combined with SAWR were developed, respectively. Weight loss and human sensory evaluation results demonstrated that Kiwi fruit's quality decline and overall acceptance decrease during the storage. Experiment result indicated that the PCA method could qualitatively discriminate all Kiwi fruit samples with different storage time. Both SR and SAWR frequency analysis methods could successfully discriminate samples with high regression coefficients (R = 0.98093 and R = 0.99014, respectively). The validation experiment results showed that the mixed predictive model developed using EN combined with SAWR present higher quality prediction accuracy than the model developed either by EN or by SAWR. This method exhibits some advantages including high accuracy, non-destructive, low cost, etc. It provides an effective way for fruit quality rapid analysis.