Ubonrat Siripatrawan

Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging.

A rapid method based on hyperspectral imaging for detection of Escherichia coli contamination in fresh vegetable was developed. E. coli K12 was inoculated into spinach with different initial concentrations. Samples were analyzed using a colony count and a hyperspectroscopic technique. A hyperspectral camera of 400-1000 nm, with a spectral resolution of 5 nm was employed to acquire hyperspectral images of packaged spinach. Reflectance spectra were obtained from various positions on the sample surface and pretreated using Sawitzky-Golay. Chemometrics including principal component analysis (PCA) and artificial neural network (ANN) were then used to analyze the pre-processed data. The PCA was implemented to remove redundant information of the hyperspectral data. The ANN was trained using Bayesian regularization and was capable of correlating hyperspectral data with number of E. coli. Once trained, the ANN was also used to construct a prediction map of all pixel spectra of an image to display the number of E. coli in the sample. The prediction map allowed a rapid and easy interpretation of the hyperspectral data. The results suggested that incorporation of hyperspectral imaging with chemometrics provided a rapid and innovative approach for the detection of E. coli contamination in packaged fresh spinach.

A novel method for shelf life prediction of a packaged moisture sensitive snack using multilayer perceptron neural network

A novel method for shelf life prediction was established for a packaged moisture sensitive snack. Artificial neural network (ANN) based on multilayer perceptrons (MLP) with back propagation algorithm was developed to predict the shelf life of packaged rice snack stored at 30^oC and 75% RH, 30^oC and 85% RH and 40^oC and 75% RH, comparable to tropical storage conditions. The MLP predicted shelf lives were then compared to the actual shelf lives. Using MLP algorithm, many factors could be incorporated into the model including food characteristics, package properties, and storage environments. The MLP neural network comprised an input layer, one hidden layer and an output layer. The network was trained using Lavenberg-Marquardt (LM) algorithm. The performance of a MLP neural network was measured using regression coefficient (R^2) and mean squared error (MSE). The MLP algorithm gave R^2 of 0.98, and MSE of 0.12. MLP offers several advantages over conventional digital computations, including faster speed of information processing, learning ability, fault tolerance, and multi-output ability.

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.

Active packaging from chitosan-titanium dioxide nanocomposite film for prolonging storage life of tomato fruit

The feasibility of active packaging from chitosan (CS) and chitosan containing nanosized titanium dioxide (CT) to maintain quality and extend storage life of climacteric fruit was investigated. The CT nanocomposite film and CS film were fabricated using a solution casting method and used as active packaging to delay ripening process of cherry tomatoes. Changes in firmness, weight loss, a*/b* color, lycopene content, total soluble solid, ascorbic acid, and concentration of ethylene and carbon dioxide of the tomatoes packaged in CT film, CS film, and control (without CT or CS films) were monitored during storage at 20°C. Classification of fruit quality as a function of different packaging treatments was visualized using linear discriminant analysis. Tomatoes packaged in the CT film evolved lower quality changes than those in the CS film and control. The results suggested that the CT film exhibited ethylene photodegradation activity when exposed to UV light and consequently delayed the ripening process and changes in the quality of the tomatoes.

Optimization of cinnamon oil nanoemulsions using phase inversion temperature method: Impact of oil phase composition and surfactant concentration

Essential oils, such as those isolated from cinnamon, are effective natural antimicrobial agents, but their utilization is limited by their low water-solubility. In this study, phase inversion temperature (PIT) was used to prepare cinnamon oil nanoemulsions. To this aim, it was hypothesized that cinnamon oil nanoemulsions could be fabricated by optimizing the oil phase composition and surfactant concentration of the system and their stability could be enhanced using a cooling-dilution method during the PIT. A mixture of cinnamon oil, non-ionic surfactant, and water was heated above the PIT of the system, and then rapidly cooled with continuous stirring, which led to the spontaneous generation of small oil droplets. The impact of oil phase composition and surfactant concentration on the formation and stability of the nanoemulsions was determined. Cinnamon oil nanoemulsions with the smallest mean droplet diameter (101 nm) were formed using 40:60 wt% of cinnamon oil and medium chain triglyceride (MCT) in the total lipid phase. Increasing surfactant concentration significantly decreased the mean droplet diameter of the nanoemulsions but did not alter their particle morphology. In addition, using the cooling-dilution method, the nanoemulsions were stable for at least 31 days when stored at 4 °C or 25 °C.

Simultaneous assessment of various quality attributes and shelf life of packaged bratwurst using hyperspectral imaging

A simultaneous evaluation of various quality attributes of packaged bratwurst using hyperspectral imaging (HSI) was developed. Changes in physicochemical (L*, a*, b* color values, pH and thiobarbituric acid (TBA)), microbiological (total viable counts (TVC) and lactic acid bacteria (LAB)) and sensory (color, odor and overall acceptability) characteristics of the packaged sausages were monitored during storage at 4 ± 1 °C. Reflectance spectra covering a wavelength range of 400-1000 nm of the samples were acquired using HSI. The relationships between the quality attributes and the spectroscopic reflectance were investigated using canonical correlation analysis. Among all quality attributes, L* color value, TBA, TVC, LAB, odor and overall acceptability appeared to be highly associated with the reflectance. To facilitate the HSI for rapid image acquisition and data processing, partial least squares regression (PLSR) analysis was employed for selection of optimal wavelengths. The selected wavelengths were then assembled into multispectral data and used as input variables to optimize the PLSR and artificial neural network models for the prediction of quality attributes of the sausage samples. The HSI technique can be used for rapid and nondestructive evaluation of the products quality and shelf life.