Predicting the Shelf Life of Cut Tuberose (Polianthes tuberosa) Using Support Vector Regression: Model Development, Benchmarking, and Practical Insights
DOI:
https://doi.org/10.5281/zenodo.17273369Keywords:
Postharvest quality, Shelf life prediction, Support vector regression, Tuberose, Vase lifeAbstract
The study developed and evaluated models to predict the shelf life of cut tuberose (Polianthes tuberosa) from routinely measured postharvest indicators. An experimental-style dataset (n=220) was assembled during 2023–24, comprising browning index, physiological loss in weight (PLW), moisture content, flower opening index, freshness index, microbial load ( CFU ), total phenols (mg GAE ), storage temperature, relative humidity, and pretreatment (Control, Sucrose+Germicide, Pulsing+STS). Data were standardized and one-hot encoded; support vector regression (SVR, RBF kernel) was tuned via five-fold cross-validation and benchmarked against ridge regression and a tuned random forest. Ridge exhibited the highest generalization (test , MAE=0.771 d, RMSE=0.991 d; mean CV- ), followed by SVR (test , MAE=0.922 d, RMSE=1.148 d; mean CV- ). A compact tuning sweep modestly improved random forest (test , MAE=1.039 d, RMSE=1.328 d; mean CV- ). Permutation-based importance indicated storage temperature and microbial load as principal determinants, with hydration metrics (PLW, moisture), freshness, and pretreatment contributing additional signal. It was concluded that day-level forecasts were feasible from a parsimonious measurement suite, and that temperature control, sanitation, hydration, and carbohydrate–germicide pretreatments were the most effective levers to extend marketable life.
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Copyright (c) 2025 Syed Mazar Ali (Author)
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