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Background The knowledge of the thermal resistance of target pathogens in food matrices is a prerequisite for the design of effective control treatments. It is also desirable, or even necessary, to validate the treatments using appropriate surrogates for the target pathogens. To obtain the thermal death kinetic information for both the target pathogens and their surrogates or validate the effect of new thermal treatments using surrogates, bacteria of interests (pathogen or surrogate) must be introduced to the food matrices at an adequate concentration to obtain survivor curves. A major challenge for the inoculation of the bacteria in low-moisture foods (LMFs) is that the inoculation could result in changes to the physical characteristics of the food matrices. For example, alteration of the microstructures and particular size could lead to different moisture absorption and desorption behaviors of treated foods in thermal treatments. Scope and approach The safety of LMFs is an emerging concern in the food industry. Extensive research only took place over the past ten years, and dry-inoculation has risen as a promising tool for developing efficient treatments to control pathogens in LMFs. This paper provides a general review of the methodologies for LMFs inoculation. It summarizes the recently published work in the developments of dry-inoculation methods and compares the advantages and limitations of different LMFs inoculation methods. Key findings and conclusions Dry-inoculation is a more suitable approach for LMFs inoculation, which offers an attractive alternative to wet-inoculation. Dry-inoculation methods require a short preparation time, and the inoculum has a long shelf-life, minimal influence on the physio-chemical properties of the food matrices, and is easier to transport.

The objective of this study was to develop a numerical model to characterize wear behavior of solid fats with different crystalline networks. Wear behaviors of three different fats (cocoa butter [CB], anhydrous milk fat [AMF], and palm oil [PO]) crystallized at three temperatures (dependent on fat type) and two cooling rates (0.1 and 15 °C min−1) were evaluated under several normal forces (0.3–2.0 N) at room temperature (20 °C). A numerical model for penetration depth that accounted for both deformation and removal of material due to wear was developed. All models had mean absolute error

Refining and conching are two important processes for chocolate manufacturing, as they help improve the flavor and texture of chocolates. However, methods such as GC-MS for assessing flavor changes are expensive. In this study, an e-nose was used to continuously monitor the volatile compounds of cocoa samples undergoing refining, for samples differing in sample mass and degree of roasting. The responses of the e-nose were characterized by three parameters (Rarea, Rpeak and Rwidth) that were able to detect the overall influence of roasting. These values along with sample mass were also used to train Kernel Distribution Models (KDM) which were implemented to better account for temperature and air flow fluctuations. For trained KDMs validated with data taken under the same conditions (sample mass, degree of roasting) the error was ∼1%. When validated under different conditions the error ranged from 2.9 to 9.3%. The degree of roasting had greater affect on the error than the sample mass. A trained KDM can be used to predict the overall volatile compounds at different refining stages, and detect whether the processing variables of sample mass and roasting degree varied from its training samples.

Soft solid foods commonly undergo processing steps such as extrusion, slicing, and shredding. Food tribological behaviors, especially wear behaviors, may be a good indication of their ability to be successfully sliced, shredded, or extruded. However, food wear behaviors are currently unknown. Thus, the objective of this study was to characterize the wear behavior of gels with different structures using a numerical model. Wear behaviors of whey protein isolate and κ-carrageenan gels were evaluated under several normal forces. An empirical model for penetration depth was developed that accounted for deformation due to applied force and removal of material. All models had mean absolute error

Roasting is a critical step in chocolate processing, where moisture content is decreased and unique flavors and texture are developed. The determination of the degree of roasting in cocoa beans is important to ensure the quality of chocolate. Determining the degree of roasting relies on human specialists or sophisticated chemical analyses that are inaccessible to small manufacturers and farmers. In this study, an electronic nose system was constructed consisting of an array of gas sensors and used to detect volatiles emanating from cocoa beans roasted for 0, 20, 30 and 40 min. The several signals were used to train a three-layer artificial neural network (ANN). Headspace samples were also analyzed by gas chromatography/mass spectrometry (GC/MS), with 23 select volatiles used to train a separate ANN.; Results: Both ANNs were used to predict the degree of roasting of cocoa beans. The electronic nose had a prediction accuracy of 94.4% using signals from sensors TGS 813, 826, 822, 830, 830, 2620, 2602 and 2610. In comparison, the GC/MS predicted the degree of roasting with an accuracy of 95.8%.; Conclusion: The electronic nose system is able to predict the extent of roasting, as well as a more sophisticated approach using GC/MS. © 2018 Society of Chemical Industry.; © 2018 Society of Chemical Industry.

The glass transition temperature (Tg) is an important property that influences the processing and textural characteristics of candy. Measurement of Tg is done by comparably expensive and complex instruments. In this study, we tested a new system which uses capacitance thermal analysis (CTA) of candy trapped between stainless plates, as the system is caused to heat at an uncontrolled rate. The data of capacitance as a function of temperature were processed by a genetic algorithm (GA), and fitted to a three-section model to determine Tg. Tg of the candies were independently measured by DSC as a reference. The results showed that when the Tg of the candy was below ∼15 °C, the measurement from the GA-CTA was higher (2–3 °C) than that from DSC. However, if Tg of the candy was higher than 15 °C, the two methods gave similar values. GA based CTA provides a feasible new way to measure phase transitions in candies with relatively inexpensive equipment, and with less need for user interpretation of data.

Hydrogels are used in multiple industries; although hydrogel deformation and wear behaviors can be a good indicator for their functionality, there are no available models for characterizing hydrogel deformation–wear behaviors. This study characterized wear behaviors of casein (3–5% w/w, 200 mM NaCl), κ-carrageenan (0.8–1.2% w/w, 100 mM NaCl), and whey protein gels (13% w/w, 50–150 mM NaCl) at room temperature (20 °C) under several normal forces (0.1–1.5 N). A kernel-based model was used to characterize hydrogel wear behavior by separating the deformation–wear process deformation-dominant, constant wear rate, and failure regions. Higher normal force and lower polymer concentration showed higher constant wear rates and lower durability. The kernel model was able to characterize hydrogel wear-deformation behaviors and is useful for industrial applications.

Tomato puree was processed by continuous high-pressure (CHP) homogenization at 69–276 MPa, for 1–3 passes. Laser scattering and light microscopy showed CHP reduced the pulp particles to ∼10–100 μm, producing smaller and more uniform particles, with processing at 276 MPa and 2 passes producing greatest particle reduction. No differences in moisture or color were found due to different treatments. In general, G ′ > G ″ for all samples, suggesting a soft gel network. Both the storage modulus ( G ′) and loss modulus ( G ″) decreased with CHP pressure. G ′ decreased modestly with frequency between 0.1 and 2 Hz, and more dramatically between 2 and 30 Hz, with behavior characteristic of entangled polymers. In general, yield stress decreased with homogenization pressure, but increased with number of passes. CHP-treated samples had lower consistency and were less shear-thinning than the control. Repeated passes increased the consistency of CHP samples. The results suggest CHP processing produced smaller and more uniform particles, causing a reduced level of microstructure that contributes to elastic properties at small deformation.