Postharvest shelf life extension of blueberries using a biodegradable package.

Small berries are commonly packaged and sold to consumers in vented petroleum-based clamshell containers. Biodegradable and compostable packages may be used as an alternative package to reduce waste generation and landfill disposal. In addition, the current clamshell container design does not allow the development of a modified atmosphere that could prolong berry shelf life. Thus, in this study, a non-ventilated biodegradable container was evaluated as a possible alternative to the containers normally used in commercial distribution of small berries. To determine the potential of biodegradable containers for small berries, highbush blueberries were packaged in polylactide (PLA) containers and stored at 10°C for 18 days and at 23°C for 9 days. Commercial vented clamshell containers were used as controls. Physicochemical and microbiological studies were carried out in order to compare the efficacy of both packages. Results showed that the PLA containers prolonged blueberry shelf life at different storage temperatures.

A new technique to prevent the main post harvest diseases in berries during storage: inclusion complexes beta-cyclodextrin-hexanal.

Natural occurring volatiles such as hexanal have a well know antifungal capacity but limited post harvest use due to their volatility. Taking this into consideration, hexanal was inserted into beta-cyclodextrins (beta-CD) to develop a controlled release mechanism and then evaluated in vitro against Colletotrichum acutatum, Alternaria alternata and Botrytis cinerea, the three main causes of post harvest diseases in berries. Different concentrations of both pure volatile hexanal and its inclusion complexes (IC) were analyzed for their fungistatic and fungicidal effects for 7 days at 23 degrees C. Hexanal has fungistatic effect on all fungi tested, however, fungicidal activity was only observed on C. acutatum. Results showed that hexanal's effectiveness was greater against C. acutatum than A. alternata and B. cinerea. Concentrations of 1.1, 2.3 and 1.3 microL hexanal/L air respectively were necessary to prevent C. acutatum, A. alternata and B. cinerea growth. Lower concentrations reduced fungal growth depending on the included amount and type of fungus. Same amount of hexanal released from beta-cyclodextrin had a lower antifungal effect on C. acutatum. Thus, ICs beta-cyclodextrin-hexanal can be used to reduce or avoid post harvest berry diseases because of their capacity to provide an antifungal volatile during storage, distribution, and consumer purchasing.

Release of acetaldehyde from beta-cyclodextrins inhibits postharvest decay fungi in vitro.

Many naturally occurring plant volatiles are known to have antifungal properties. However, they have limited use because they diffuse rapidly in air. In this in vitro study, acetaldehyde was chosen as a prototype volatile in order to study the controlled release of antifungal volatiles from cyclodextrins (CD). The major postharvest pathogens Alternaria alternata, Botrytis cinerea, and Colletotrichum acutatum were exposed to the pure volatile for 7 days at 23 degrees C. Acetaldehyde was most effective against A. alternata, followed by C. acutatum, and B. cinerea, with 0.12, 0.56, and 1.72 microL/L in air being required to inhibit fungal growth, respectively, according to the bioassay developed. Second, the effectiveness of the new beta-CD-acetaldehyde release system was evaluated against A. alternata for 7 days at 23 degrees C. Sufficient volatile was released from 0.7 g of beta-CD-acetaldehyde to prevent fungal growth in vitro.

Detection of Escherichia coli in packaged alfalfa sprouts with an electronic nose and an artificial neural network.

A rapid method for the detection of Escherichia coli (ATCC 25922) in packaged alfalfa sprouts was developed. Volatile compounds from the headspace of packaged alfalfa sprouts, inoculated with E. coli and incubated at 10 degrees C for 1, 2, and 3 days, were collected and analyzed. Uninoculated sprouts were used as control samples. An electronic nose with 12 metal oxide electronic sensors was used to monitor changes in the composition of the gas phase of the package headspace with respect to volatile metabolites produced by E. coli. The electronic nose was able to differentiate between samples with and without E. coli. To predict the number of E. coli in packaged alfalfa sprouts, an artificial neural network was used, which included an input layer, a hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The network was shown to be capable of correlating voltametric responses with the number of E. coli. A good prediction was possible, as measured by a regression coefficient (R2 = 0.903) between the actual and predicted data. In conjunction with the artificial neural network, the electronic nose proved to have the ability to detect E. coli in packaged alfalfa sprouts.

Data visualization of Salmonella Typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network.

Class visualization of multi-dimensional data from analysis of volatile metabolic compounds monitored using an electronic nose based on metal oxide sensor array was attained using a Kohonen network. An array of 12 metal oxide based chemical sensors was used to monitor changes in the volatile compositions from the headspace of packaged fresh sprouts with and without Salmonella Typhimurium contamination. Kohonen׳s self-organizing map (SOM) was then created for learning different patterns of volatile metabolites. The Kohonen network comprising 225 nodes arranged into a two-dimensional hexagonal map was used to locate the samples on the map to facilitate sample classification. Graphical maps including the unified matrix, component planes, and hit histograms were described to characterize the relation between samples. The clustering of samples with different levels of S. Typhimurium contamination could be visually distinguishable on the SOM. The Kohonen network proved to be advantageous in visualization of multi-dimensional nonlinear data and provided a clearer separation of different sample groups than a conventional linear principal component analysis (PCA) approach. The sensor array integrated with the Kohonen network could be used as a rapid and nondestructive method to distinguish samples with different levels of S. Typhimurium contamination. Although the analyses were performed on samples with natural background microbiota of about 7 Log(CFU/g), this microbiota did not affect the S. Typhimurium detection. The proposed method has potential to rapidly detect a target foodborne pathogen in real-life food samples instantaneously without subsequently culturing stages.

An overview of polylactides as packaging materials.

Polylactide polymers have gained enormous attention as a replacement for conventional synthetic packaging materials in the last decade. By being truly biodegradable, derived from renewable resources and by providing consumers with extra end-use benefits such as avoiding paying the "green tax" in Germany or meeting environmental regulations in Japan, polylactides (PLAs) are a growing alternative as a packaging material for demanding markets. The aim of this paper is to review the production techniques for PLAs, summarize the main properties of PLA and to delineate the main advantages and disadvantages of PLA as a polymeric packaging material. PLA films have better ultraviolet light barrier properties than low density polyethylene (LDPE), but they are slightly worse than those of cellophane, polystyrene (PS) and poly(ethylene terephthalate) (PET). PLA films have mechanical properties comparable to those of PET and better than those of PS. PLA also has lower melting and glass transition temperatures than PET and PS. The glass transition temperature of PLA changes with time. Humidity between 10 and 95% and storage temperatures of 5 to 40 degrees C do not have an effect on the transition temperature of PLA, which can be explained by its low water sorption values (i.e. <100 ppm at Aw = 1). PLA seals well at temperatures below the melting temperature but an appreciable shrinking of the films has been noted when the material is sealed near its melting temperature. Solubility parameter predictions indicate that PLA will interact with nitrogen compounds, anhydrides and some alcohols and that it will not interact with aromatic hydrocarbons, ketones, esters, sulfur compounds or water. The CO2, O2 and water permeability coefficients of PLA are lower than those of PS and higher than those of PET. Its barrier to ethyl acetate and D-limonene is comparable to PET. The amount of lactic acid and its derivatives that migrate to food simulant solutions from PLA is much lower than any of the current average dietary lactic acid intake values allowed by several governmental agencies. Thus, PLA is safe for use in fabricating articles for contact with food.

Solid-phase microextraction, gas chromatography, and mass spectrometry coupled with discriminant factor analysis and multilayer perceptron neural network for detection of Escherichia coli.

This study was performed to investigate the ability of using discriminant factor analysis (DFA) and an artificial neural network (ANN) to identify and quantify the number of Escherichia coli (ATCC 25922) in nutrient media from data generated by analysis of E. coli volatile metabolic compounds using solid-phase microextraction (SPME) coupled with gas chromatography (GC) and mass spectrometry (MS). E. coli was grown in super broth and incubated at 37 degrees C for 2 to 12 h. Numbers of E. coli were followed using a colony counting method. An SPME device was used to collect the volatiles from the headspace above the samples, and the volatiles were identified using GC-MS. DFA was used to classify the samples from different incubation times. From DFA, it was possible to differentiate super broth from media containing E. coli when cell numbers were 10(5) CFU or more. The potential to predict the number of E. coli from the SPME-GC-MS data was investigated using a multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.996) between actual and predicted data.

Rapid method for prediction of Escherichia coli numbers using an electronic sensor array and an artificial neural network.

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37 degrees C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 10(5) CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography-mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.

Active packaging of fresh chicken breast, with allyl isothiocyanate (AITC) in combination with modified atmosphere packaging (MAP) to control the growth of pathogens.

Listeria monocytogenes and Salmonella typhimurium are major bacterial pathogens associated with poultry products. Ally isothiocyanate (AITC), a natural antimicrobial compound, is reportedly effective against these pathogenic organisms. A device was designed for the controlled release of AITC with modified atmosphere packaging (MAP), and then evaluated for its ability to control the growth of L. monocytogenes and S. typhimurium on raw chicken breast during refrigerated storage. In order to obtain controlled release during the test period, a glass vial was filled with AITC and triglyceride. It was then sealed using high-density polyethylene film. The release of AITC was controlled by the concentration (mole fraction) of AITC in the triglyceride and by the AITC vapor permeability through the film. The fresh chicken samples were inoculated with one or the other of the pathogens at 10(4) CFU/g, and the packages (with and without AITC-controlled release device) were flushed with ambient air or 30% CO(2)/70% N(2) before sealing, and then stored at 4 degrees C for up to 21 d. The maximum reduction in MAP plus AITC (compared to MAP alone) was 0.77 log CFU/g for L. monocytogenes and 1.3 log CFU/g for S. typhimurium. The color of the chicken breast meat was affected by the concentration of AITC. Overall, a release rate of 0.6 microg/h of AITC was found to not affect the color, whereas at 1.2 microg/h of AITC the surface of the chicken was discolored.

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable.

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10+2 degrees C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R(2)=0.99) between actual and predicted data.

Thermal Treatment of Cottage Cheese "In-Package" by Microwave Heating 1.

Small-curd cottage cheese was packaged in polystyrene tubs and subjected to heat treatments of 37-82.2°C using .5 and 2.8 kw microwave sources. Cottage cheese was also packed in flexible pouches (laminate structure of ethylene vinyl acetate/polyvinylidene chloride/ethylene vinyl acetate - EVA/PVDC/EVA) and polyethylene tubs and subjected to heat treatments of 37.0 - 82.2°C. Moisture content, syneresis, microbial population, pH and sensory properties were monitored until samples were considered no longer acceptable. The shelf life of samples ranged from 7 to 42 d. Optimum quality was observed when the packaged cheese was heated to 48.8°C using the low power source. Cheese packed in the flexible pouches had the longest shelf life.