Lycopene content (LC) and soluble solid content (SSC) are important quality indicators for cherry tomatoes. This study attempted simultaneous analysis of inner quality of cherry tomato by Electronic nose (E-nose) using multivariate analysis. E-nose was used for data acquisition, the response signals were regressed by multiple linear regression (MLR) and partial least square regression (PLS) to build predictive models. The performances of the predictive models were tested according to root mean square and correlation coefficient (R2) in the training set and prediction set. The results showed that MLR models were superior to PLS model, with higher value of R2 and lower values of for RMSE firmness, pH, SSC, and LC. Together with MLR, E-nose could be used to obtain firmness, pH, soluble solid and lycopene contents in cherry tomatoes.
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