Pasting behaviour, textural properties and freeze-thaw stability of wheat flour-crude malva nut (Scaphium scaphigerum) gum system

The effect of replacement of crude malva nut gum (CMG) at 0%, 2.5%, 5%, 7.5% and 10% w/w on pasting behaviour, textural properties and freeze-thaw stability of wheat flour was investigated. Replacement of wheat flour by CMG significantly elevated (p < 0.05) the peak viscosity (128-669 RVU), hot paste viscosity (77-363 RVU), breakdown (51-306 RVU) and final viscosity (157-557 RVU) of wheat flour pastes. Pasting temperature (59-85 °C) of the flour decreased with increasing CMG content. The textural parameters including hardness, springiness, cohesiveness, gumminess and chewiness of the mix gels decreased with higher level of CMG. Freeze-thaw stability measurement revealed that wheat gel mixtures containing higher level (7.5% and 10%) of CMG decreased syneresis more than 80% after 3 freeze-thaw cycles, when compared to non-CMG sample. The rate of syneresis depended on CMG concentration and number of freeze-thaw cycles. The results demonstrated that higher viscosity, softer texture and lower syneresis of wheat gel could be attained using CMG.     

Artificial neural network approach to simultaneously predict shelf life of two varieties of packaged rice snacks

Actual storage shelf life test by storing a packaged product under typical storage conditions is costly and time consuming. A new approach using an artificial neural network (ANN) algorithm for shelf life prediction of two varieties of moisture-sensitive rice snacks packaged in polyethylene and polypropylene bags and stored at various storage conditions was established. The ANN used to predict the shelf life was based on multilayer perceptron with back propagation algorithm. The ANN algorithm employed the data of product characteristics, package properties and storage conditions. The neural network comprised an input, one hidden and one output layers. The network was trained using Bayesian regularisation. The performance of ANN was measured using regression coefficient ( R ² = 0.23–0.28) and root mean square error (RMSE = 0.96–0.99). The ANN-predicted shelf lives agreed very well with actual shelf life data. ANN could be used as an alternative method for shelf life prediction of moisture-sensitive food products as well as product/package optimisation.     

Physical properties and antioxidant activity of an active film from chitosan incorporated with green tea extract

An active film from chitosan incorporated with aqueous green tea extract (GTE) was developed. The effects of GTE concentrations including 2, 5, 10 and 20% (w/v) of green tea in the film-forming solution on the film properties were determined by measuring physical properties, total polyphenolic content and antioxidant activity of the active films. Fourier Transform Infrared (FTIR) spectrometry was carried out to observe the potential modifications of the chitosan films when incorporated with GTE. The results suggested that incorporation of GTE into chitosan films improved mechanical and water vapor barrier properties and enhanced polyphenolic content and antioxidant activity of the films. Changes in the FTIR spectra of the chitosan films were observed when GTE was incorporated, suggesting some interactions occurred between chitosan and the polyphenols from GTE. This study showed the benefits of incorporation of GTE into chitosan films and the potential for using the developed film as an active packaging.     

An alternative freshness index method for modified atmosphere packaged abalone using an artificial neural network

An alternative freshness index method for abalone (Haliotis asinina) muscle packaged under atmospheric air (Air) and modified atmosphere (MA) of 40% CO₂: 30% O₂: 30% N₂ packaging conditions and stored at 2 ± 1 °C was developed. Biochemical indices covering pH, total volatile basic nitrogen (TVB-N), trimethylamine (TMA) and nucleotide degradation products, as well as instrumental texture and color of the packaged abalones, were determined. Sensory characteristics including odor, color and appearance were evaluated and then summarized into overall freshness scores (freshness index). The biochemical and instrumental analyses were then calibrated with the freshness index, using an artificial neural network algorithm. The neural network was shown to be capable of correlating biochemical and instrumental analyses with the freshness index. A useful prediction was possible, as measured by a low mean square error (MSE = 0.092) and a regression coefficient (R² = 0.98) between true and predicted data.     

Effect of cold pre‐treatment duration before freezing on frozen bread dough quality

The effect of cold pre‐treatment (CT) duration prior to freezing on the quality of a standard bread dough was investigated. Doughs held at 0 °C or 10 °C for 1 h or 3 h before air‐blast freezing were compared with standard dough frozen after 0.5 h at 0 °C (0 °C/0.5 h) and fresh (unfrozen) dough. Cumulative gas production measured in a risograph was used to quantify the dough quality after storage at ?18 ± 0.1 °C for 1, 7 or 17 days. Relative to fresh dough, gas production significantly reduced after freezing for all treatments. The doughs with CT at 0 °C for 1 or 3 h or 10 °C for 1 h had significantly higher gas production after freezing and less rapid decline in gas production during frozen storage than the doughs with the 0 °C/0.5 h CT. The 10 °C/3 h CT gave no gas production benefit after freezing and had the most rapid decline in gas production during frozen storage.     

Rapid differentiation between E. coli and Salmonella Typhimurium using metal oxide sensors integrated with pattern recognition

A rapid method to differentiate between E coli and Salmonella Typhimurium was developed. E. coli and S. Typhimurium were separately grown in super broth and incubated at 37 °C. Super broth without inoculation of E. coli or S. Typhimurium was used as control. Numbers of E. coli and S. Typhimurium were followed using a colony counting method. Identification of the volatile metabolites produced by E. coli and S. Typhimurium was determined using solid-phase microextraction coupled with gas chromatography/mass spectrometry. An electronic nose with 12 non-specific metal oxide sensors was used to monitor the volatile profiles produced by E. coli and S. Typhimurium. Principal component analysis (PCA) and back-propagation neural network (BPNN) were used as pattern recognition tools. PCA was used for data exploration and dimensional reduction. PCA could visualize class separation between sample subgroups. The BPNN was shown to be capable of predicting the number of E. coli and S. Typhimurium. Good prediction was possible as measured by a regression coefficient (R² = 0.96) between true and predicted data. Using metal oxide sensors and pattern recognition techniques, it was possible to discriminate between samples containing E. coli from those containing S. Typhimurium.