J. Subbiah

Development of a multi-temperature calibration method for measuring dielectric properties of food

In the most commonly used, open-ended coaxial probe method, dielectric properties of food products are measured after calibrating the instrument at 25°C using air (open circuit), short (short circuit) and deionized water. Measurement accuracy may be compromised when dielectric properties are measured at temperatures other than 25°C. The main objective of this study was to systematically perform calibration at multiple temperatures, quantify measurement errors and develop a method for multitemperature calibration to measure dielectric properties of materials over a wide temperature range. The dielectric properties of deionized water were measured from 1 to 90°C after calibrating the dielectric measurement system using air, short and deionized water at six different temperatures (1, 25, 40, 55, 70, and 85°C). The temperature-dependent dielectric properties of water calibrated at six temperatures were then compared with the reported values at two typical microwave frequencies of 915 and 2450 MHz. The results showed that the 25°C calibration is valid for dielectric constant measurement, but not valid for dielectric loss factor measurement at temperatures far from the calibration temperature. Multitemperature calibration is helpful for reducing errors and improving the accuracy of the temperature-dependent dielectric properties measurement, especially for low-loss materials. Calibrations at two temperatures (25 and 85°C) within the range studied were found to be suitable for the temperature-dependent dielectric properties measurement. The dielectric properties of lasagna components (ricotta cheese, pasta, and meat sauce) were measured using this multitemperature calibration method.

Modeling and Validation of Microwave Ablations With Internal Vaporization

Numerical simulation is increasingly being utilized for computer-aided design of treatment devices, analysis of ablation growth, and clinical treatment planning. Simulation models to date have incorporated electromagnetic wave propagation and heat conduction, but not other relevant physics such as water vaporization and mass transfer. Such physical changes are particularly noteworthy during the intense heat generation associated with microwave heating. In this paper, a numerical model was created that integrates microwave heating with water vapor generation and transport by using porous media assumptions in the tissue domain. The heating physics of the water vapor model was validated through temperature measurements taken at locations 5, 10, and 20 mm away from the heating zone of the microwave antenna in homogenized ex vivo bovine liver setup. Cross-sectional area of water vapor transport was validated through intraprocedural computed tomography (CT) during microwave ablations in homogenized ex vivo bovine liver. Iso-density contours from CT images were compared to vapor concentration contours from the numerical model at intermittent time points using the Jaccard index. In general, there was an improving correlation in ablation size dimensions as the ablation procedure proceeded, with a Jaccard index of 0.27, 0.49, 0.61, 0.67, and 0.69 at 1, 2, 3, 4, and 5 min, respectively. This study demonstrates the feasibility and validity of incorporating water vapor concentration into thermal ablation simulations and validating such models experimentally.

Validation of radio-frequency dielectric heating system for destruction of Cronobacter sakazakii and Salmonella species in nonfat dry milk

Cronobacter sakazakii and Salmonella species have been associated with human illnesses from consumption of contaminated nonfat dry milk (NDM), a key ingredient in powdered infant formula and many other foods. Cronobacter sakazakii and Salmonella spp. can survive the spray-drying process if milk is contaminated after pasteurization, and the dried product can be contaminated from environmental sources. Compared with conventional heating, radio-frequency dielectric heating (RFDH) is a faster and more uniform process for heating low-moisture foods. The objective of this study was to design an RFDH process to achieve target destruction (log reductions) of C. sakazakii and Salmonella spp. The thermal destruction (decimal reduction time; D-value) of C. sakazakii and Salmonella spp. in NDM (high-heat, HH; and low-heat, LH) was determined at 75, 80, 85, or 90 °C using a thermal-death-time (TDT) disk method, and the z-values (the temperature increase required to obtain a decimal reduction of the D-value) were calculated. Time and temperature requirements to achieve specific destruction of the pathogens were calculated from the thermal destruction parameters, and the efficacy of the RFDH process was validated by heating NDM using RFDH to achieve the target temperatures and holding the product in a convection oven for the required period. Linear regression was used to determine the D-values and z-values. The D-values of C. sakazakii in HH- and LH-NDM were 24.86 and 23.0 min at 75 °C, 13.75 and 7.52 min at 80 °C, 8.0 and 6.03 min at 85 °C, and 5.57 and 5.37 min at 90 °C, respectively. The D-values of Salmonella spp. in HH- and LH-NDM were 23.02 and 24.94 min at 75 °C, 10.45 and 12.54 min at 80 °C, 8.63 and 8.68 min at 85 °C, and 5.82 and 4.55 min at 90 °C, respectively. The predicted and observed destruction of C. sakazakii and Salmonella spp. were in agreement, indicating that the behavior of the organisms was similar regardless of the heating system (conventional vs. RFDH). Radio-frequency dielectric heating can be used as a faster and more uniform heating method for NDM to achieve target temperatures for a postprocess lethality treatment of NDM before packaging.

Short communication: Radio frequency dielectric heating of nonfat dry milk affects solubility and whey protein nitrogen index

The US infant formula market is estimated at over

Heat and Mass Transfer Modeling for Microbial Food Safety Applications in the Meat Industry: A Review

3.5 billion, of which 75% are dairy-based formulas. Dried dairy powders pose a significant food safety risk, with Cronobacter sakazakii and Salmonella spp. being pathogens of particular concern. Radio frequency dielectric heating (RFDH) can provide rapid, uniform heat treatment of dry powders; thus, it potentially may be used as a postprocess lethality treatment for nonfat dry milk (NDM) or powdered infant formula. Because RFDH is a heat treatment, the functionality of the NDM may be altered and should be evaluated. High heat- and low heat-NDM were RFDH processed at temperatures ranging from 75 to 90°C for 5 to 125 min. Products were then assessed for whey protein nitrogen index (WPNI), solubility, and color. In low heat-NDM, RFDH decreased WPNI and solubility if the process was done at ≥ 80°C; however, in high heat-NDM, RFDH had a greater effect on solubility than WPNI and some color properties were altered. Further investigation of RFDH is merited to validate its application as a pathogen control process for NDM across processing parameters that result in acceptable functional properties for infant formula and other food products containing NDM.

Modeling Heat Transfer during Cooling of Cooked Ready-to-Eat Meats using Three-Dimensional Finite Element Analysis

Temperature is an important factor affecting microbial growth in meat products, and hence the most controlled and monitored parameter for food safety in the meat industry. In the last few decades, modeling of heat and mass transfer in products has gained special attention in the meat industry as it can be integrated with predictive microbial models, and eventually with risk assessment models. Thus, heat and mass transfer models can be used as practical tools to assess microbial safety of meat products quantitatively, especially in the event of unexpected processing issues such as thermal processing deviations. This manuscript reviews research efforts related to heat and mass transfer modeling in meat products that have been published in recent years. It synthesizes the main ideas behind modeling of thermal processing in the meat industry encompassing common considerations and techniques. This review specially emphasizes in research efforts that have been oriented to industrial applications, and can be potentially integrated with food safety tools. Literature indicates that despite great advances in the field, there are several challenges that persist and the scientific community must address them to develop models applicable to the meat industry.

Non Destructive Determination of Peanut Moisture Content Using Near Infrared Reflectance Spectroscopy

A heat transfer model was developed for simulating time-varying temperature distributions in ready-to-eat meats during thermal processing. Three-dimensional transient finite element analysis was presented as a tool to solve the model without the need for proprietary software. The model considered conduction as the governing heat transfer phenomenon; with evaporative, convective and radiative boundary conditions. Moreover, it took into account several factors present in meat processing such as three-dimensional products with irregular geometries; time-dependant processing conditions including air temperature, air velocity and relative humidity; non-uniform initial temperature distributions; and temperature dependent thermal properties. The finite element analysis was performed on meshes containing linear tetrahedral and triangular elements. The step-by-step methodology described can be easily extrapolated to computational algorithms implementable in free license software (e.g. Java Technology). In addition, these algorithms can be integrated with predictive microbiology models; which can be particularly useful for evaluating the severity of thermal processing deviations caused by unexpected processing disruptions. This integration can be the foundation of open source software packages which will serve as quantitative tools to support food safety management in the meat industry.

NIR reflectance method to determine moisture content in food products

A custom made Near Infrared reflectance spectroscope, measuring reflectance value of the energy incident on a peanut sample over the wavelengths 1000 nm to 2500 nm, is used to estimate the moisture content (MC) of in-shell peanuts and peanut kernels of Valencia type, non-destructively. About 150g of peanuts were filled into a Petri dish, in at least two layers, placed on a platform under the light source of the instrument, and NIR light reflected from the sample was collected by the NIR instrument. Valencia peanut samples with MC range between 5% and 23% were used as the calibration set of samples. A calibration model was developed with the measured spectral values and their MC reference values, determined earlier by standard air-oven method. Partial least square (PLS) regression method was used to develop models for MC prediction. Peanuts were then shelled, and similar measurements were made on the kernels. MC values of peanut samples in the moisture range of 6% to 21%, not used in the calibration, were predicted by the developed model and compared with their standard air-oven values for validation. The best calibration model was selected based on the calculated standard Error of Prediction (SEC), coefficient of determination (R2) and bias values. For Valencia peanut kernels the best model was the model developed using the spectral data with reflection plus derivative pretreatment. For in-shell peanuts the model developed by reflection plus normalization pretreatment was found to be the best.

Estimation of moisture and oil content of in-shell nuts with a capacitance sensor using discrete wavelet analysis

Moisture content (MC) is an important quality factor that is measured and monitored, at various stages of processing and storage, in the food industry. There are some commercial instruments available that use near infrared (NIR) radiation measurements to determine the moisture content of a variety of grain products, such as wheat and corn, with out the need of any sample grinding or preparation. However, to measure the MC of peanuts with these instruments the peanut kernels have to be chopped into smaller pieces and filled into the measuring cell. This is cumbersome, time consuming and destructive. An NIR reflectance method is presented here by which the average MC of about 100 g of whole kernels could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. Initially, NIR reflectance measurements were made at 1 nm intervals in the wave length range of 1000 nm to 1800 nm and the data was modeled using partial least squares regression (PLSR). The predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R2 value of 0.96 and a standard error of prediction (SEP) of 0.83. Using the PLSR beta coefficients, five key wavelengths were identified and using multiple linear regression (MLR) method MC predictions were made. The R2 and SEP values of the MLR model were 0.84 and 1.62, respectively. Both methods performed satisfactorily and being rapid, nondestructive, and non-contact, may be suitable for continuous monitoring of MC of grain and peanuts as they move on conveyor belts during their processing.

NIR Reflectance Spectroscopic Method for Nondestructive Moisture Content Determination of In-Shell Peanuts

Moisture and oil contents are important quality factors often measured and monitored in the processing and storage of food products such as corn and peanuts. For estimating these parameters for peanuts nondestructively a parallel-plate capacitance sensor was used in conjunction with an impedance analyzer. Impedance, phase angle and dissipation factor were measured for the parallel-plate system, holding the in-shell peanut samples between its plates, at frequencies ranging between 1MHz and 30 MHz in intervals of 0.5 MHz. The acquired signals were analyzed with discrete wavelet analysis. The signals were decomposed to 6 levels using Daubechies mother wavelet. The decomposition coefficients of the sixth level were passed onto a stepwise variable selection routine to select significant variables. A linear regression was developed using only the significant variables to predict the moisture and oil content of peanut pods (inshell peanuts) from the impedance measurements. The wavelet analysis yielded similar R2 values with fewer variables as compared to multiple linear and partial least squares regressions. The estimated values were found to be in good agreement with the standard values for the samples tested. Ability to estimate the moisture and oil contents in peanuts without shelling them will be of considerable help to the peanut industry.