Detection and discrimination of Carica papaya fungi through the analysis of volatile metabolites by gas chromatography and analysis of variance‐principal component analysis

Abstract

AbstractConventional detection and identification of the fungi causing postharvest diseases in fruits are time‐consuming, laborious, and can only be performed after the manifestation of symptoms. In this work, an alternative method based on headspace analysis, and which allows the early detection of fungi species frequently found papaya fruit, is presented. Volatile compounds of four in vitro fungi cultures (Alternaria alternata, Colletotrichum gloeosporioides, Fusarium solani, and Lasiodiplodia theobromae) were extracted using solid‐phase microextraction (SPME) and analyzed by gas chromatography coupled with mass spectrometry. The resulting chromatographic fingerprints were explored by conventional principal component analysis (PCA) and analysis of variance (ANOVA)–PCA. Decomposition of the original matrix, according to the factors proposed in the experimental design, by ANOVA before applying the PCA improved the distinction of the control and fungi samples. The main chromatographic peaks referring to the metabolites produced by each species were successfully identified using the proposed analysis. A primary alcohol with five carbons and phenylethyl alcohol were observed in all fungi species and so could be used as an indicative of postharvest disease. Although unique metabolites were detected for all fungi species, only those from C. gloeosporioides and another from F. solani could be surely identified, such as thymolmethyl and 3,6‐dimethylhept‐6‐en‐4‐yn‐3‐ol, respectively. The in vitro results obtained are promising, and it is expected that the biomarkers detected in this work will be useful in future development of methods for the early detection and classification of papaya fungi species.