Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut

Defining optimal mineral-salt concentrations for in vitro plant development is challenging, due to the many chemical interactions in growth media and genotype variability among plants. Statistical approaches that are easier to interpret are needed to make optimization processes practical. Response Surface Methodology (RSM) and the Chi-Squared Automatic Interaction Detection (CHAID) data mining algorithm were used to analyze the growth of shoots in a hazelnut tissue-culture medium optimization experiment. Driver and Kuniyuki Walnut medium (DKW) salts (NH₄NO₃, Ca(NO₃)₂·4H₂O, CaCl₂·2H₂O, MgSO₄·7H₂O, KH₂PO₄ and K₂SO₄) were varied from 0.5× to 3× DKW concentrations with 42 combinations in a IV-optimal design. Shoot quality, shoot length, multiplication and callus formation were evaluated and analyzed using the two methods. Both analyses indicated that NH₄NO₃ was a predominant nutrient factor. RSM projected that low NH₄NO₃ and high KH₂PO₄ concentrations were significant for quality, shoot length, multiplication and callus formation in some of the hazelnut genotypes. CHAID analysis indicated that NH₄NO₃ at ≤1.701× DKW and KH₂PO₄ at >2.012× DKW were the most critical factors for shoot quality. NH₄NO₃ at ≤0.5× DKW and Ca(NO₃)₂ at ≤1.725× DKW were essential for good multiplication. RSM results were genotype dependent while CHAID included genotype as a factor in the analysis, allowing development of a common medium rather than several genotype specific media. Overall, CHAID results were more specific and easier to interpret than RSM graphs. The optimal growth medium for Corylus avellana L. cultivars should include: 0.5× NH₄NO₃, 3× KH₂PO₄, 1.5× Ca(NO₃)₂.