Small Sample Machine Learning for Predicting Composition Changes in Chemically Pretreated Kapok Fiber

In single-output modeling, cellulose was the most predictable component, with GBDT giving the strongest performance (<i>R</i>² = 0.782; RMSE = 4.185 percentage points), whereas hemicellulose and lignin showed weaker prediction. In wrapper-based multitarget modeling, GBDT improved simultaneous prediction of cellulose, hemicellulose and lignin, giving <i>R</i>² values of 0.909, 0.653, and 0.672, respectively.

Feature-importance analysis indicated that temperature was the dominant descriptor for polysaccharide-related composition changes, whereas the pH-associated pretreatment environment contributed more strongly to lignin-related variation. These findings position limited-data machine learning as a useful exploratory tool for modeling pretreatment–composition relationships in kapok fiber, while also highlighting the need for cautious validation when sample size is small.