Résumé
The quality of processed products can be adversely affected by uncontrollable batches of mangoes, which exhibit heterogeneous characteristics. This study aimed to establish predictive models for sugar and organic acid contents (dependent variables) in processed products using the initial compositions of fresh mangoes. Three mango cultivars (cv. 'Kent', cv. 'Keo Romeat', and cv. 'Keo Chen') were classified as low-density and high-density groups. Each group of mangoes at the green-mature, mid-ripe, and ripe stages was processed into pasteurised purees, dried slices, and mango chips. Prediction models were established using a mix of simple linear regression (SLR) based on the initial content and Analysis of Variance (ANOVA) to identify the impact of qualitative variables (ripening stage, cultivar-density, and processing technique). In processed mangoes, 13% sucrose content was estimated to accumulate with the three qualitative variables, whereas glucose and fructose contents decreased from their initial levels by 10% and 7%, respectively. Processing techniques can predict the ratio of sugars/acids (S/A) in processed products, regardless of the ripening stage or cultivar-density. Similar to S/A, citric acid and malic acid contents in mango products were significantly increased by processing techniques. The initial content and processes were insufficient to predict the final contents of the same parameters in processed mangoes; therefore, some models need to include the effects of ripening stage and cultivar-density to improve the prediction. These relevant explanatory variables contributed significantly to the development of the models, resulting the accuracy of predictive models with normalised root mean square errors (NRMSEs) lower than 10%, except for malic acid (14.04%). In conclusion, it is feasible to estimate the sugar and acidity levels in processed mangoes, offering promising possibilities for ensuring consistent quality of mango-based products.