A statistical model to predict titratable acidity of pineapple during fruit developing period responding to climatic variables

• Published in: Scientia horticulturae 2016-10, Vol.210, p.19-24
• Main Authors: Dorey, Elodie, Fournier, Patrick, Léchaudel, Mathieu, Tixier, Philippe
• Format: Article
• Language: English
• Subjects: Ananas comosus (L.) Merr; Fruit quality; Rainfall; Statistical model; Temperature; Total radiation
• ISSN: 0304-4238; 1879-1018
• DOI: 10.1016/j.scienta.2016.07.014

•A statistical model defines periods of climatic variables that affect fruit acidity.•Temperature, total radiation and rainfall affect acidity of pineapple at harvest.•The model accurately predicts titratable acidity of pineapple at harvest.•The model can be linked to a crop model to manage fruit quality. Acidity greatly affects the nutritional properties and organoleptic properties of fruits. Pineapple acidity is strongly affected by climatic factors during fruit growth but there is a lack of knowledge about their period of influence. A statistical model was developed to: (i) identify the periods (referred to as “integration periods”) during the flowering–harvest interval during which climatic variables (rainfall, global radiation and temperature) affect titratable acid content (TA) of pineapple fruit at harvest; and (ii) to predict TA at harvest on Reunion Island. The model was developed in two steps. In Step 1, those integration periods that were best correlated with TA were identified for each climatic variable. In Step 2, a complete linearized mixed-effect model (GLM) was built to predict TA based on all candidate variables. Temperature greatly affected TA in early growth periods, whereas total radiation had a considerable effect in late growth periods. Rainfall greatly affected TA in both early and late growth periods. In the complete GLM, the climatic variables and the interaction between temperature and rainfall were significantly correlated with TA at harvest and explained 60% of the variance. Comparison of model predictions and observations from 14 pineapple fields indicated that the model accurately predicted TA (RRMSE=0.08). This model should help farmers to select the planting date and flowering induction date in order to optimize TA in pineapple fruit on Reunion Island.