Abstract

This research describes the application of portable field Raman spectroscopy combined with a statistical analysis of the resulting spectra, employing principal component analysis (PCA) and linear discriminant analysis (LDA), in which we determine that this method provides a high degree of reliability in the early detection of Huanglongbing (HLB) on Sweet Orange, disease caused by the bacteria Candidatus Liberibacter asiaticus. Symptomatic and asymptomatic plant samples of Sweet Orange (Citrus sinensis), Persian Lime (C. latifolia), and Mexican Lime (C. aurantifolia) trees were collected from several municipalities, three at Colima State and three at Jalisco State (HLB presence). In addition, Sweet Orange samples were taken from two other Mexican municipalities, one at San Luis Potosí and the other at Veracruz (HLB absent). All samples were analyzed by real-time PCR to determine its phytosanitary condition, and its spectral signatures were obtained with an ID-Raman mini. Spectral anomalies in orange trees HLB-positive, were identified in bands related to carbohydrates (905 cm⁻¹, 1043 cm⁻¹, 1127 cm⁻¹, 1208 cm⁻¹, 1370 cm⁻¹, 1272 cm⁻¹, 1340 cm⁻¹, and 1260–1280 cm⁻¹), amino acids, proteins (815 cm⁻¹, 830 cm⁻¹, 852 cm⁻¹, 918 cm⁻¹, 926 cm⁻¹, 970 cm⁻¹, 1002 cm⁻¹, 1053 cm⁻¹, and 1446 cm⁻¹), and lipids (1734 cm⁻¹, 1736 cm⁻¹, 1738 cm⁻¹, 1745 cm⁻¹, and 1746 cm⁻¹). Moreover, PCA–LDA showed a sensitivity of 86.9 % (percentage of positives, which are correctly identified), a specificity of 91.4 % (percentage of negatives, which are correctly identified), and a precision of 89.2 % (the proportion of all tests that are correct) in discriminating between orange plants HLB-positive and healthy plants. The Raman spectroscopy technique permitted rapid diagnoses, was low-cost, simple, and practical to administer, and produced immediate results. These are essential features for phytosanitary epidemiological surveillance activities that may conduct a targeted selection of highly suspicious trees to undergo molecular DNA analysis.

© 2016 The Author(s)

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