Nano- and micro-sized carnauba wax emulsions-based coatings incorporated with ginger essential oil and hydroxypropyl methylcellulose on papaya: Preservation of quality and delay of post-harvest fruit decay.

Carnauba wax nano and micro-sized emulsions and hydroxypropyl methylcellulose coatings, alone or combined with ginger essential oils (GEO) were applied on papayas and evaluated under several storage conditions. In a first experiment, storage parameters were: 6 days at 22 °C, and 9 days at 13 °C followed by 5 days at 22 °C. In a second experiment, storage was: 5 days at 22 °C, and 10 days at 16 °C followed by 3 days at 22 °C. Coating effects were dependent on storage conditions. While fruits were in cold storage, there were few changes; however, at 22 °C, the differences between coatings became more evident. Nanoemulsions maintained papaya quality during storage by retarding firmness loss, color changes, and reducing respiration rates, resulting in delayed ripening. GEO exhibited some positive effect on fungal disease control. Nanoemulsion-based coatings improved shelf life by reducing weight loss, color development, and slowing ripening of papaya fruit.

Fruit quality parameters and volatile compounds from 'Palmer' mangoes with internal breakdown.

The internal breakdown (IB) is a premature and uneven mango pulp ripening physiological disorder that is noticed only when the fruit is sliced for consumption. Thus, there is a demand for analytical methods to detect IB in mangoes to avoid consumer dissatisfaction and reduce postharvest waste. In this work, physicochemical and volatile compounds were determined to evaluate the ability to predict pulp IB. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of the data show that color, firmness, and volatiles compounds are important to give some information about the physiological changes caused by IB. The volatile compounds methacrylic acid, ethyl ester, isopentyl ethanoate, limonene oxide, (E)-2-pentenal, tetradecane, and γ-elemene were identified as chemical markers of IB. Therefore, mango physical and chemical characteristics combined with PCA and PLS-DA were successfully employed for the identification of IB in mangoes, showing significant differences between healthy and IB fruits.

Monitoring of soluble pectin content in orange juice by means of MIR and TD-NMR spectroscopy combined with machine learning.

This study represents a rapid and non-destructive approach based on mid-infrared (MIR) spectroscopy, time domain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin content (SPC) changes in orange juice. Current reference methods of SPC in orange juice are laborious, requiring several extractions with successive adjustments hindering rapid process intervention. 109 fresh orange juices samples, representing different harvests, were analysed using MIR, TD-NMR and reference method. Unsupervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups. Analyses of variance of the two MIR and TD-NMR datasets show that only the MIR groups were different at 95% confidence for SPC average values. This approach allows build classification models based on MIR data achieving 85% and 89% of accuracy. Results demonstrate that MIR/ML can be a suitable strategy for the quick assessment of SPC trends in orange juices.