NUTRITIONAL PROFILE OF PAPAYA FRUIT AND NON-DESTRUCTIVE PREDICTION OF RIPENING STAGES OF PAPAYA USING VOLATILE BIOMARKERS

Abstract

Papaya (Carica papaya) is a nutritionally and economically climacteric fruit. Papaya is the one of the most consumed fruit crops in the world. The ripe papaya fruits are in terms of nutrients and possesses an array of health-protective chemical constituents. Papaya fruit ripening is an irreversible genetically programmed coordinated process which includes several biochemical changes including changes in the accumulation pattern of primary- and secondary metabolites, tissue softening, physicochemical changes, and changes in the emission pattern of flavour volatile components. Till date, a systemic study on changes in the metabolite profile, physicochemical properties, and volatile profile of papaya fruit during on-tree fruit maturation and ripening and further during postharvest storage has not been conducted. This work aims to identify a series of changes happening in the metabolite and volatile profile and physicochemical properties of papaya fruit during preharvest on-tree ripening and during postharvest storage. Further this thesis aims to identify some volatile as non-invasive biomarker for non-destructive prediction of ripening and for prediction of fruit shelf-life during postharvest storage. The aim was achieved through gas chromatography-mass-spectrometry-based profile of volatile and non-volatile (primary and secondary metabolites) metabolites by employing multivariate chemometric and statistical analyses, pattern recognition and machine learning tools. In this study, the papaya cultivar 'Red lady' was used. The solid-phase micro-extraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS) analysis of VOCs emitted from the papaya fruit cv. 'Red Lady' at three ripening stages were studied, viz. green unripe (UR), yellowish-green intermediate ripe (IR), and yellow full ripe (FR). GC-MS analyses and the subsequent statistical studies identified a total of 35 VOCs. Partial least squares discriminant analysis (PLS-DA) of VOCs from the three ripening stages identified six biomarker VOCs, which can efficiently distinguish between ripening stages. Among the six ripening biomarkers, three VOCs (methyl hexanoate, 3-carene and longifolene) also showed a remarkable correlation with the ripening-associated changes in the fruit nutritional profile. The VOC biomarkers identified in this work could be used as a viable technology for non-invasive monitoring of ripening stages and the nutritional value of papaya fruits. Further, partially ripen papaya fruit is collected from tree at the harvest stage (8 weeks on tree maturation after anthesis with orange yellow skin colour with minor green patches) followed by analyses of changes in the metabolite profile, volatile profile and physicochemical properties of fruit during postharvest storage at the room temperature (26 ±2 oC). A significant change in the volatile profile of metabolites, volatiles, physicochemical properties and nutritional properties (free amino acid, protein, sugar, and ascorbic acid) were observed during postharvest storage. GC-MS analyses identified 18 major volatiles and 52 non-volatile metabolites including all the postharvest stages. The majority ofmetabolites showing differential changes during maturation belong to primary metabolites. The multivariate partial least squares-discriminant analysis (PLS-DA) together with variable importance in projection scores, identified the top volatile organic compounds are methylhexanoate, decamethylpentasiloxane, etc, and non-volatile metabolites are 4-hydroxybenzoic acid, hexanoic acid, etc. respectively from each stage which shows the maximum influence on the separation of postharvest stages. Furthermore, fruits were stored at low temperature (10 ±2 oC) and changes in the volatile profile, metabolite profile, physicochemical properties and nutritional properties were analyzed at four stages (0, 5, 10, and 15 days). Using GC-MS-based metabolomics analyses, a total of 17 and 55 volatile organic compounds and non-volatile metabolites were identified respectively, in papaya fruit including all postharvest storage stages at low temperature. PLS-DA was used to identify selected biomarker volatiles which can be used to distinguish between different storage stages. When papayas are stored at low temperatures during postharvest storage, volatile aromatic organic compounds can be used as non-destructive biomarkers to predict their storage life with their physicochemical and nutritional properties. Further statistical analyses were carried out to evaluate the potential of volatiles (VOCs) as biomarkers for predicting the storage-life of stored papaya using chemometrics, feature selection, and multivariate statistical analysis (partial least squares; PLS). Based on the PLS model, it was found that methyl-hexanoate can be used as a reliable biomarker to forecast the storage-life as well as their physicochemical and nutritional properties of papaya non-destructively during postharvest storage. A metal oxide semiconductor (MOS) based electronic nose sensor was developed for easy prediction of the fruit ripening stage as well as selected nutritional parameters of papaya fruit during ripening. The electronic nose system possesses an array of seven metal oxide semiconductors (MOS) that can be used as a non-invasive way to predict the ripening stage and physicochemical parameters of papaya. In the current study, four ripening stages, i.e., unripe (UR), intermediate ripe (IR), fully ripe (FR), and over-ripe (OR) of papaya cultivar, ‘Red lady’ were subjected to a low-cost MOS-based electronic nose for their sensory analysis with their quality parameters viz., total soluble solids (˚Brix), total soluble sugars (TSS), pH, and firmness at four different ripening stages. Using multivariate statistical analyses (principle component analysis; PCA, linear discriminant analysis; LDA, partial least squares; PLS, linear regression; LR), and machine learning tools (artificial neural network, ANN), MOS is used as a potential sensing device for prediction of ripening stage and physicochemical parameters of papaya. The PLS model with six components showed good classification ability (R2 = 0.962 and Q2 = 0.951; RMSECV = 0.061). The ANN model showed the best classification ability as well as used to predict physicochemical parameters (R2 = 0.999 and Q2 = 0.998; RMSECV = 0.055). ANN models showed good prediction ability. These results demonstrated that e-nose could be used as a reliable system for accurate prediction of the ripening stage and physicochemical parameters of papaya. Overall, these highly informative current data sets can be used to non-invasively predict fruit ripening stage and postharvest storage life of papaya fruits. E-nose system developed can be easily adopted by the farmers for proper harvesting of papaya at appropriate stage. Farmers in this mobile phone generation can easily adopt this technology.