GC-MS analysis of headspace and liquid extracts for metabolomic differentiation of citrus Huanglongbing and zinc deficiency in leaves of 'Valencia' sweet orange from commercial groves.

INTRODUCTION: Citrus Huanglongbing (HLB) is considered the most destructive citrus disease worldwide. Symptoms-based detection of HLB is difficult due to similarities with zinc deficiency.
OBJECTIVE: To find metabolic differences between leaves from HLB-infected, zinc-deficient, and healthy 'Valencia' orange trees by using GC-MS based metabolomics.
METHODOLOGY: Analysis based on GC-MS methods for untargeted metabolite analysis of citrus leaves was developed and optimized. Sample extracts from healthy, zinc deficient, or HLB-infected sweet orange leaves were submitted to headspace solid phase micro-extraction (SPME) and derivatization treatments prior to GC-MS analysis.
RESULTS: Principal components analysis achieved correct classification of all the derivatized liquid extracts. Analysis of variance revealed 6 possible biomarkers for HLB, of which 5 were identified as proline, β-elemene, (-)trans- caryophyllene, and α-humulene. Significant (P < 0.05) differences in oxo-butanedioic acid, arabitol, and neo-inositol were exclusively detected in samples from plants with zinc deficiency. Levels of isocaryophyllen, α-selinene, β-selinene, and fructose were significantly (P < 0.05) different in healthy leaves only.
CONCLUSION: Results suggest the potential of using identified HLB biomarkers for rapid differentiation of HLB from zinc deficiency.