The potential for natural antimicrobial compounds extracted from true cinnamon (Cinnamomum zeylanicum Nees) to use as a food additive to extend the shelf life of fresh-cut apples was investigated. Several different extracts were prepared using cinnamon bark and powder to evaluate their antimicrobial activity on two marker microorganisms, Escherichia coli O157:H7 and Listeria innocua. An ethanolic extract of cinnamon bark (2% w/v) inhibited the growth of E. coli and L. innocua by 94 and 87%, respectively. When incorporated in a commercial antibrowning dipping solution, FreshExtend, the cinnamon bark extract (1% w/v) reduced significantly (P < 0.05) the microbial growth on apple slices stored for 12 days at 6C in comparison to the control. The cinnamon extract had no influence on the antibrowning properties of FreshExtend. Liquid chromatography mass spectrometry analysis showed that the major chemical constituent of this extract is cinnamic aldehyde. PRACTICAL APPLICATIONSThe consumer demand for convenient and nutritious, minimally processed produce like fresh-cut apples has been steadily increasing. Identification of natural antimicrobial agents that are acceptable to the consumer is a challenge to the fresh-cut industry. In this study, we discovered antimicrobial properties of a cinnamon extract and identified the principal antimicrobial compound as cinnamic aldehyde. For the first time, we demonstrated that this Mention of trade or firm names does not constitute an endorsement by the Nova Scotia Agricultural College over others of a similar nature not mentioned. * Corresponding
Understanding the relationship between root bulking and agroclimatological factors can aid in predicting the yield and quality of processing carrot (Daucus carota L.). Field trials (four field seasons) with selected cultivars at various seeding rates, seeding dates, and harvest dates were conducted for three carrot types, viz., baby, diced, and sliced, and yield components were monitored. The corresponding weather data, such as minimum and maximum temperature, solar radiation, and rainfall, were recorded. Data from the 2006, 2007, and 2009 field seasons were used for model development, while 2008 data were reserved for the validation. Following a forward‐stepwise regression procedure to identify highly correlated input factors, feed‐forward back‐propagated artificial neural network (ANN) and multiple linear regression (MLR) models were developed. After validation, the best performing models were identified based on a ranking system that weighed the root mean square error (RMSE) and the fitness of the model (R2). For baby carrots, the Sugarsnax‐based MLR model exhibited 23% lower RMSE than the ANN for the desirable yield component. In diced carrots, predictions from both models (ANN and MLR) exhibited a good fit, with high R2 values (0.80–0.90). For sliced carrots, Topcut‐based ANN models predicted the majority of the yield components consistently better than MLR models. When MLR and ANN models were compared, their efficiencies differed with carrot type and yield component. The MLR models underperformed in modeling processes that were inherently nonlinear compared with ANN. Nonetheless, ANN models suffered from overfitting and consequently at times failed to demonstrate extrapolation capabilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.