R. Simpson

Determination of Antioxidant Capacity, Total Phenolic Content and Mineral Composition of Different Fruit Tissue of Five Apple Cultivars Grown in Chile

Apples (Malus domestica Borkh.) have been identified as one of the main dietary sources of antioxidants, mainly phenolic compounds. These compounds vary in their composition and concentration, among cultivars and fruit tissues. In this research, the total phenolic content (Folin-Ciocalteau assay), antioxidant capacity (Ferric Reducing Antioxidant Power, FRAP assay) and mineral composition in three fruit tissues (peel, pulp and whole fruit), of apple cultivars commonly used for dried apple production in Chile, were studied. In addition, the physical-chemical characteristics (dry weight, pH, titratable acidity, soluble solids content and color) were also evaluated. The results indicated that the total phenolic content, the antioxidant capacity, and the mineral composition, of peel were substantially higher than those of whole fruit, and pulp for all the cultivars studied. Among cultivars, �Red Delicious� apple peels have a significantly much higher content of total phenolic (11.6 mg gallic acid equivalents [GAE] g-1 FW) and a higher FRAP (209.9 µmol Fe+2 g-1 FW). Additionally, a high correlation between total phenolic content and antioxidant capacity was found in all the cultivars and fruit tissues analyzed, except in the apple pulp. On the other hand, the physical and chemical composition differed among cultivars and fruit tissues. In conclusion, our results demonstrated that the total phenolic content, antioxidant capacity, mineral composition, and physical and chemical characteristics vary considerably depending on the apple cultivars and fruit tissues analyzed.

Mathematical model development, experimental validation and process optimization: retortable pouches packed with seafood in cone frustum shape

Abstract The aim of this research was to develop and validate a mathematical model coupled with an optimization technique for thermal processing of conduction-heated foods in retortable pouches in order to: (a) search for variable retort temperature profiles to minimize process time, and (b) search for variable retort temperature profiles to minimize quality gradient (thiamine) within the product. The model was validated utilizing Jack mackerel (Trachurus Murphyi) packed in retortable pouches. The conjugated gradient method was utilized as a search technique to find the best variable retort temperature profile to satisfy the specific objectives. The simulation results were in good agreement with the observed temperatures. The prediction errors obtained in the validation study were under 5%. Non-significant differences (P

Simultaneous heat and mass transfer applied to non-respiring foods packed in modified atmosphere

A mathematical model to predict heat and mass transport phenomena in non-respiring food packed in modified atmosphere (MAP) was developed and validated. The model incorporates simultaneous gas convection, sorption, diffusion, heat convection and conduction. The model was applied to MAP systems containing CO2 ,O 2 ,N 2 and H2O. Validation test was done with gelatin. The average errors between experimental and simulated values were low: <0.6 (C) for the temperatures, 3% for relative humidity and <1.43% for the headspace gas composition. Model predictions during heating and cooling phases indicate that temperature modification of the packaged product can be quite slow, reflecting the relevance of proper chilling in the packaging, transport and storage processes of MAP products. The model applied to shelf-life studies, for specific products, can be utilized to identify facility and product handling improvements to generate the greater positive impact on product quality. The development of this or similar mathematical tools would allow for more technical and informed management decisions. 2003 Elsevier Ltd. All rights reserved.

MULTIOBJECTIVE OPTIMIZATION APPROACH: THERMAL FOOD PROCESSING

The objective of this study was to utilize a multiobjective optimization technique for the thermal sterilization of packaged foods. The multiobjective optimization approach used in this study is based on the optimization of well-known aggregating functions by an adaptive random search algorithm. The applicability of the proposed approach was illustrated by solving widely used multiobjective test problems taken from the literature. The numerical results obtained for the multiobjective test problems and for the thermal processing problem show that the proposed approach can be effectively used for solving multiobjective optimization problems arising in the food engineering field.

Comparison of three drying processes to obtain an apple peel food ingredient

Apple peel, a waste product from dried apple manufacture, has a high content of bioactive phenolic compounds. In Chile ca. 9000 ton of apple peel are generated each year. To obtain a novel food ingredient, we compared three drying processes on Granny Smith apple peel: oven (60°C), drum dryer (110°C), and freeze drying. The influence of each drying technology on the chemical characteristics and antioxidant capacity on fresh and dried peel fractions were determined. The results indicate that all the drying processes affected the chemical characteristics and the color of the ingredient. Total phenolics content and antioxidant capacity decreased significantly in all the products during drying. The preservation of phenolics in apple peel was higher using freeze drying (92%). The drum-drying process retained over 70% putatively healthy phenolics. In addition, this is the most economical system suitable to obtain the apple peel ingredient.

FRACTIONAL CALCULUS AS A MATHEMATICAL TOOL TO IMPROVE THE MODELING OF MASS TRANSFER PHENOMENA IN FOOD PROCESSING.

Research, innovations and applications in the food industry are always delayed relative to other areas of engineering, in part because modeling, simulation and optimization of food processes face additional challenges due to the nature of biological materials. In addition, researchers and scientists in other engineering fields tend to have better mathematical training in relation to researchers in biological sciences. Our hypothesis is that the diffusion process within food materials which are non-Fickian, that is, anomalous, can be characterized using a fractional calculus formulation. There is currently strong experimental and theoretical evidence that the diffusion process in food materials generally departs from the Fickian diffusion model which comes from the random walk displacement of the diffusants. In biological materials the heterogeneity due to the cellular structure produces regions in which the diffusants can travel anomalous length distances or be stopped in compartments, which produces a departure from the expected results of the random walk, resulting in anomalous diffusion. The introduction and application of fractional calculus to the field of food science/engineering could lead to many uses, primarily in heat and mass transfer processes. Fractional calculus is a powerful tool for solving and understanding complex natural phenomena; therefore, we believe it is necessary to exploit it to the utmost to obtain realistic and practical solutions for the mass transfer phenomena and to demonstrate its potential to other food science/engineering problems.

Prediction correlation of vapor pressure for methyl jasmonate

Abstract A vapor pressure correlation based on Antoine’s equation was proposed for methyl jasmonate. Equilibrium data was obtained in the range of 3–100 °C and analyses were performed with GC–MS by solid phase microextraction technique. The correlation gives an acceptable extrapolation for the boiling point at atmospheric pressure, allowing evaluation of vapor pressure at temperatures usually found in food and agricultural process applications.

Simple, practical, and efficient on-line correction of process deviations in batch retort through simulation

Abstract This paper describes a simple, practical and efficient (nearly precise, yet safe) strategy for on-line correction of thermal process deviations during retort sterilization of canned foods. The strategy is intended for easy implementation in any cannery around the world. Commercial systems currently in use for on-line correction of process deviations do so by extending process time to that which would be needed had the entire process been carried out at the lower retort temperature reached at the lowest point in the deviation (“commercial” correction). This method of correction often results in extensive unnecessary over-processing with concomitant deterioration in product quality, and costly interruption to the retort loading/unloading rotation schedules in retort cook room operations. These problems are addressed by a novel control strategy that takes into account the duration of the deviation in addition to the magnitude of the temperature drop. It calculates a “proportional” extended process time at the recovered retort temperature that will deliver the final specified target lethality with very little over processing in comparison to current industry practice. Results from an exhaustive “optimization” search routine using the complex method are also reported, that show the proposed strategy will always result in a corrected process that delivers no less than the final target lethality specified for the originally scheduled process.

Kinetic Parameter Determination for Enzyme Hydrolysis of Fish Protein Residue Using D-optimal Design

The objective of this work was to compare the quality of parameter estimation from experiments with protein hydrolysis of fish muscle carried out following a traditional versus D-optimal design of experiments. Traditional design was done from hydrolysis experiments using the rapid titration method, from which a large number of data points was obtained and made available for parameter estimation. D-optimal design is recommended under conditions when it would not be possible to use rapid titration with analytical tools. Such conditions would make it necessary to reduce the number of samples that would need to be taken as well as the error and variance in the estimated parameters needed for the kinetic model. Results have shown that D-optimal and traditional designs of experiments obtained accurate and reliable estimates of kinetic model parameters, with a great difference in the number of data points between both types of design: 18 points in traditional and 3 points in D-optimal design. Nevertheless, a few number of observations in D-optimal design impacted negatively the confidence interval because of a low degree of freedom. Confidence intervals ranging from 7.8% to 87% of the parameters values were obtained with D-optimal design method in contrast with traditional design where confidence intervals ranged from 6.2% to 15% of the parameters values. It is estimated that combining the advantages of both types of design will result in an optimal experimental design in terms of reliability, accuracy, and costs saving of analysis. This would be done by determining the number of observations required to achieve an appropriate confidence interval (about six or more experimental points) and, then, a selection of the best experimental points of a curve of hydrolysis by D-optimal design.

Estimating Reaction Rates in Squid Protein Hydrolysis Using Artificial Neural Networks

In this study, the use of artificial neural networks (ANN) for estimating reaction rates in enzymatic hydrolysis of squid waste protein was investigated. This is a complex process because a number of inherent simultaneous inhibition and enzyme inactivation reactions occur during hydrolysis that make it difficult to develop a reliable kinetic model by more traditional deterministic approaches. A series of 12 enzyme hydrolysis experiments were carried out on samples of squid waste under specified conditions of temperature, pH, and initial enzyme and substrate concentrations. Experimental data in the form of substrate concentration over time were taken as real time course data (TCD), and divided into three groups for respective use in training, validating, and testing the model. A feed-forward architecture was utilized to construct the necessary predictive model. The network was trained until the mean squared error function between target and actual output values reached a desired minimum. Data sets from the remaining two groups were used for subsequent validation and testing of the model. The model performed well when tested against experimental data in the third group (not used in its development) and taken over a wide range of initial conditions. Maximum differences between experimental and predicted values of substrate concentration at any point in time ranged from 0.3 to 0.5 g L-1 (1% to 3% of initial substrate concentration), with correlation coefficients between predicted and experimental results ranging from 0.95 to 0.97.