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.

This review collects information about the history of avocado and the economically important disease, avocado sunblotch, caused by the avocado sunblotch viroid (ASBVd). Sunblotch symptoms are variable, but the most common in fruits are irregular sunken areas of white, yellow, or reddish color. On severely affected fruits, the sunken areas may become necrotic. ASBVd (type species Avocado sunblotch viroid, family Avsunviroidae) replicates and accumulates in the chloroplast, and it is the smallest plant pathogen. This pathogen is a circular single-stranded RNA of 246–251 nucleotides. ASBVd has a restricted host range and only few plant species of the family Lauraceae have been confirmed experimentally as additional hosts. The most reliable method to detect ASBVd in the field is to identify symptomatic fruits, complemented in the laboratory with reliable and sensitive molecular techniques to identify infected but asymptomatic trees. This pathogen is widely distributed in most avocado-producing areas and causes significant reductions in yield and fruit quality. Infected asymptomatic trees play an important role in the epidemiology of this disease, and avocado nurseries need to be certified to ensure they provide pathogen-free avocado material. Although there is no cure for infected trees, sanitation practices may have a significant impact on avoiding the spread of this pathogen.

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Avocado sunblotch viroid (ASBVd) was detected in the exocarp and mesocarp of symptomatic and asymptomatic 'Hass' avocado fruit. Macroscopic symptoms caused by the ASBVd in fruits are the result of anatomical and chemical changes in the structure of the exocarp and mesocarp cells, which are typified by marked cellular disorganization, accumulation of phenolic compounds in the cytoplasm and cell walls, and reduction of cytoplasmic content resulting in cell collapse and death. The mesocarp of symptomatic fruits showed a reduction of 28-38% in chlorophyll A and 23-28% in chlorophyll B content, as well as an increase of up to 50-62% in phenolic compounds compared to asymptomatic fruit. The phloem cells of symptomatic fruit showed hyperplasia and the xylem elements were occluded in the mesocarp. Additionally disorganization and cell collapse was observed leading to groove formation in symptomatic fruit. Chlorophyll reduction and increase in phenolic compounds probably leads to the development of yellow or red symptoms in the rind. Asymptomatic fruits did not show any anatomical change despite the presence of the ASBVd. The PCR and dot-blot hybridization membranes showed relatively higher signal intensity in symptomatic fruits in contrast to the asymptomatic ones.

Avocado sunblotch viroid (ASBVd) is an economically important pathogen that reduces the yield and quality standards of infected avocado trees. There are no reports on the effects of the viroid in the postharvest quality of avocado fruits. The present study has focused on three phenotypic classes: asymptomatic fruits harvested from vigorous trees (AF-V), asymptomatic fruits harvested from trees of regular vigor (AF-R) and symptomatic fruits harvested from trees expressing the disease (SF-S). The water loss was similar in the three fruit classes. The abnormal firmness condition, skin color and size reduction occurred only in SF-S fruits, which did not ripe uniformly due to reduced CO 2 and ethylene production. The proximal analyses showed no significant differences in the variables analyzed, except for the lipid content and dry matter, which was lower in SF-S and AF-R fruits. ASBVd affects physiology and postharvest quality of symptomatic avocado fruits. In contrast, the ripening process of AF-V and AF-R fruits was normal and their physical characteristics and nutrient input were not altered, therefore, these fruits were classified as Supreme Quality, Extra Size (221-265 g) and Premium (172-210 g) according to the NMX-FF-016-SCFI-2006 standards.

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Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.