Reducing Browning of Fresh-cut ‘Maha’ Carambola with Chemical Additives and Low-oxygen Atmospheres

The greatest attraction to using carambola (Averrhoa carambola L.) in the fresh-cut market is the star shape that the fruit presents after a transverse cut. Carambola is well-suited for minimal processing, but cut surface browning is a main cause of deterioration. This problem is exacerbated as a result of mechanical injuries occurring during processing and is mainly induced by the leakage of phenolic compounds from the vacuole and subsequent oxidation by polyphenol oxidase (PPO) (Augustin et al., 1985). The use of browning inhibitors in processed fruits is restricted to compounds that are non-toxic, ‘wholesome’, and that do not adversely affect taste and flavour (Gil et al., 1998). In the past, browning was mainly controlled by the action of sulphites, but the use of this compound has declined due to allergic reactions in asthmatics (Weller et al., 1995). The shelf life of fresh-cut products may be extended by a combination of oxygen exclusion and the use of enzymatic browning inhibitors. The objectives of this work were to determine the effects of: (1) post-cutting chemical treatments of ascorbic, citric, oxalic acids, and EDTA-Ca; (2) atmospheric modification; and (3) combinations of the above, on the shelf life of carambola slices based on appearance, colour and polyphenol oxidase activity

Near infrared reflectance as a rapid and inexpensive surrogate measure for fatty acid composition and oil content in peanuts (Arachis hypogaea L.)

The fatty acid composition of ground nuts (Arachis hypogaea L.) commonly known as peanuts, is an important consideration when a new variety is being released. The composition impacts on nutrition and, importantly, self-life of peanut products. To select for suitable breeding material, it was necessary to develop a rapid, non-derstructive and cost-efficient method. Near infrared spectroscopy was chosen as that methodology. Calibrations were developed for two major fatty-acid components, oleic and linoleic acids and two minor components, palmitic and stearic acids, as well as total oil content. Partial least squares models indicated a high level of precision with a squared multiple correlation coefficient of greater than 0.90 for each constitutent. Standard errors for prediction for oleic, linoleic, palmitic, stearic acids and total oil content were 6.4%, 4.5%, 0.8%, 0.9% and 1.3% respectively. The results demonstrated that reasonable calibrations could be developed to predict oil composition and content of peanuts for a breeding programme.