Prediction of mango quality during ripening stage using MQ-based electronic nose and multiple linear regression

Abstract

In recent years, consumers have shown interest in non-destructive methods to assess the fruit's internal quality during ripening. The objective of this study is to construct an E-nose system using low-cost MQ sensors and evaluate fruit quality, specifically soluble sugar content (SSC) and hardness of mango during ripening. The correlation test was performed to compare sensor readings with SSC and hardness, and multiple linear regression (MLR) was used to establish linear equations for mango quality indices based on sensor variation. Over the storage period, the hardness of mango was decreased from the value of 15.4 kgf/cm² to 12.25 kgf/cm². Similarly, the SSC for mangoes increased from 19.7 %Brix to a final value of 24.66 %Brix. The sensor values also showed positive correlation with SSC and negative correlation with hardness of mango, respectively. Using the MLR analysis, the hardness and SSC of mango during the ripening stage, the correlation coefficient (r) of 0.847, standard error of 1.49 kgf/cm² and 0.815, standard error of 1.696 %Brix for hardness and SSC prediction, respectively. These results indicate that MQ-based E-nose is the rapid and non-destructive method for predicting mango qualities during ripening stage.