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, ß-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, ß-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.

Sap flow, xylem anatomy and photosynthetic variables of three Persea species in response to laurel wilt.

Laurel wilt, a lethal vascular wilt disease caused by the fungus Raffaelea lauricola, affects several tree species in the Lauraceae, including three Persea species. The susceptibility to laurel wilt of two forest tree species native to the southern USA, Persea borbonia and Persea palustris, [(Raf.) Sarg.] and avocado, Persea americana (Mill.) cv Waldin, was examined and related to tree physiology and xylem anatomy. Net CO2 assimilation (A), stomatal conductance (gs), leaf chlorophyll index (LCI), leaf chlorophyll fluorescence (Fv/Fm), xylem sap flow, theoretical stem hydraulic conductivity (Kh) and xylem vessel anatomy were assessed in trees of each species that were inoculated with R. lauricola and in control trees. Laurel wilt caused a reduction in A, gs, LCI, Fv/Fm and blockage of xylem vessels by tyloses formation that negatively impacted Kh and sap flow in all Persea species. However, disease susceptibility as indicated by canopy wilting and sapwood discoloration was less pronounced in P. americana cv Waldin than in the two forest species. Xylem vessel diameter was significantly smaller in P. borbonia and P. palustris than in P. americana cv Waldin. Differences in laurel wilt susceptibility among species appear to be influenced by physiological and anatomical tree responses.

Physicochemical and Sensory Properties of Cold Pressed Oils from Florida Hamlin and Valencia Oranges Affected by Huanglongbing.

Cold pressed oils from huanglongbing (HLB) symptomatic (SY) and asymptomatic (AS) Hamlin and Valencia oranges were assessed for 2 y (2014 to 2015 and 2015 to 2016 seasons) with 2 harvest dates for each orange variety per year. Physicochemical properties (optical rotation, aldehyde content, ultraviolet [UV] absorbance, refractive index, and specific gravity) mandated by the United States Pharmacopeia (USP) for orange oil quality were assessed. Hamlin and Valencia oils showed minor differences in physicochemical properties based upon disease stage. However, all Hamlin oils had aldehyde contents below the USP minimum and Valencia oil from late season SY oranges had specific gravities above the USP maximum. Significant differences based on harvest year were seen for aldehyde content, refractive index, optical rotation, and UV absorbance. While none of these changes led to an oil being out of USP specifications, they indicate a need to monitor the quality of oil every year to ensure a consistent product. Flavor taste panels were performed both years by adding 0.035% oil samples to a uniform orange juice base. Aroma panels were done by smelling pure oil. There were no significant differences between SY and AS oils for flavor, although panelist race was a significant factor in several of the panels. There were significant differences between the aroma of SY and AS oils for both 2015 to 2016 Hamlin Early and Valencia Late samples. Overall, these results show HLB can have an effect on the aroma and USP mandated physicochemical properties of Florida orange oils, although flavor may be unaffected by this plant disease.

Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves.

In recent years, Huanglongbing (HLB) also known as citrus greening has greatly affected citrus orchards in Florida. This disease has caused significant economic and production losses costing about $750/acre for HLB management. Early and accurate detection of HLB is a critical management step to control the spread of this disease. This work focuses on the application of mid-infrared spectroscopy for the detection of HLB in citrus leaves. Leaf samples of healthy, nutrient-deficient, and HLB-infected trees were processed in two ways (process-1 and process-2) and analyzed using a rugged, portable mid-infrared spectrometer. Spectral absorbance data from the range of 5.15-10.72 µm (1942-933 cm(-1)) were preprocessed (baseline correction, negative offset correction, and removal of water absorbance band) and used for data analysis. The first and second derivatives were calculated using the Savitzky-Golay method. The preprocessed raw dataset, first derivatives dataset, and second derivatives dataset were first analyzed by principal component analysis. Then, the selected principal component scores were classified using two classification algorithms, quadratic discriminant analysis (QDA) and k-nearest neighbor (kNN). When the spectral data from leaf samples processed using process-1 were used for data analysis, the kNN-based algorithm yielded higher classification accuracies (especially nutrient-deficient leaf class) than that of the other spectral data (process-2). The performance of the kNN-based algorithm (higher than 95%) was better than the QDA-based algorithm. Moreover, among different types of datasets, preprocessed raw dataset resulted in higher classification accuracies than first and second derivatives datasets. The spectral peak in the region of 9.0-10.5 µm (952-1112 cm(-1)) was found to be distinctly different between the healthy and HLB-infected leaf samples. This carbohydrate peak could be attributed to the starch accumulation in the HLB-infected citrus leaves. Thus, this study demonstrates the applicability of mid-infrared spectroscopy for HLB detection in citrus.