Carica papaya postharvest problems, such as diseases caused by fungi, generate huge economic losses for all those involved in an export chain. Thus, detection and identification of fungi species in the early stage are necessary and helpful for reducing the losses. Conventional methodologies are time-consuming, laborious, invasive, destructive, and can only be performed after the onset of symptoms in fruits . It has been proposed recently in our laboratory an alternative method to uncover the metabolites produced by papaya’s fungi species in vitro based on the volatile analysis by gas chromatography-mass spectrometry (GC-MS) and chemometrics. It was possible to determine some biomarkers that indicate the presence of fungi . In this work, it is being proposed a non-invasive and non-destructive methodology, based on volatile metabolites analysis by GC-MS coupled to chemometric tools, for the in vivo early detection of three fungi species (Alternaria alternata, Colletotrichum gloeosporioides, Lasiodiplodia theobromae) frequently found in Brazilian papaya.
Fruits were previously inoculated by depositing 5-mm Potato Dextrose Agar medium (PDA) plug, containing mycelium of fungus in active growth, onto small wounds made on papaya surface. The inoculated and the control papayas (fruits only with small wounds) were placed in hermetically closed glass bottles. The system was allowed to stand before the analysis for the accumulation of volatile organic compounds (VOCs). The VOCs were collected by exposing an SPME fiber in the bottle headspace, and, then, they were analyzed by GC-MS. The analysis was performed in four replicates (four inoculated and four non-inoculated papayas) four times a week.
Conventional principal component analysis (PCA) and analysis of variance – principal component analysis (ANOVA-PCA) were used to perform an initial exploratory analysis. In the ANOVA-PCA, the influence of three factors on the data variability — class (inoculated and control papayas), “day”, and “replicate” — and the interaction between two of them — class versus day— was investigated. Then, the PLS-DA method was used for the discrimination between the papayas inoculated with different fungi species and for the identification of the metabolites produced by each fungi species.
The distinction of the control and inoculated papayas was improved by the decomposition of the original matrix, according to the factors proposed in the experimental design, by ANOVA before applying the PCA. Some metabolites as a primary alcohol with five carbons and diethyl phthalate were identified in infected papaya, and other metabolites such as phenylmethanol were only produced by healthy papaya.
The developed method has proven to be a potential alternative for the early diagnosis of fungi disease with small false negative and false positive rates, in addition to an accurate discrimination of the pathogenic fungal species in the fruits during postharvest storage.