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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.

Background An e-nose or an e-tongue is a group of gas sensors or chemical sensors that simulate human nose or human tongue. Both e-nose and e-tongue have shown great promise and utility in improving assessments of food quality characteristics compared with traditional detection methods. Scope and approach This review summarizes the application of e-nose and e-tongue in determining the quality-related properties of foods. The working principles, applications, and limitations of the sensors employed by electronic noses and electronic tongues were introduced and compared. Widely employed pattern recognition algorithms, including artificial neural network (ANN), convolutional neural network (CNN), principal component analysis (PCA), partial least square regression (PLS), and support vector machine (SVM), were introduced and compared in this review. Key findings and conclusions Overall, e-nose or e-tongue combining pattern recognition algorithms are very powerful analytical tools, which are relatively low-cost, rapid, and accurate. E-nose and e-tongue are also suitable for both in-line and off-line measurements, which are very useful in monitoring food processing and detecting the end product quality. The user of e-nose and e-tongue need to strictly control sample preparation, sampling, and data processing.

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.

Hydrogels are soft materials with applications in multiple industries, but their fundamental wear behaviors are relatively unknown. Studies on hydrogel wear behaviors generally use empirical measurements. Additionally, it can be difficult to measuring hydrogel wear rates by traditional mass loss measurements because wear debris cannot always be removed without disturbing intact sample. Therefore, the objective of study was to develop a method for more precise calculation of soft material wear rates. The method developed compared creep-recovery and wear-recovery behaviors of casein hydrogels to separate deflection of the material under an applied load from the amount of material removed. Casein hydrogels were prepared at different concentrations (8%, 10%, and 15% w/w) and pH (2.3, 3.6, 4.8), and tested under a range of normal loads (0.3, 0.4, 0.5 N). Wear-recovery behaviors were fit to an exponential model. Maximum penetration depth during wear and creep, creep-recovery, and wear-recovery increased with lower casein concentration and higher normal force and pH. Exponential models for casein gel wear-recovery behaviors indicated good fit (R2 > 0.99). This wear measurement method is useful for developing food products with specific oral and industrial processing behaviors.

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.

Dielectric barrier discharge plasma-activated mist (PAM) is a surface treatment that has been shown to have antimicrobial effects on microorganisms attached to food contact surfaces. In this study, tryptic soy agar, purple lettuce, kale, and baby spinach leaves, were surface-inoculated with Enterobacter aerogenes inoculum (4 × 107 CFU/ml) and held for 30 min at room temperature (25 °C), then subsequently exposed to PAM in an enclosure (0.04 m3) from 5 to 20 min. Reductions ranging from 3.8 ± 0.1 log CFU/plate to 5.6 ± 0.3 log CFU/plate were observed on agar plates after exposure to PAM for 5 to 20 min. The leaves were either dip-inoculated or spot-inoculated. Extending PAM treatment time from 5 to 20 min increased microbial reduction on dip-inoculated leaves from 0.4 ± 0.2, 0.8 ± 0.1, and 0.9 ± 0.1 log CFU/g to 0.9 ± 0.1, 1.3 ± 0.1, and 2.0 ± 0.2 log CFU/g for purple lettuce, kale, and baby spinach leaves, respectively, and similar bacterial inactivations were observed on spot-inoculated leaves.

Thorough and proper washing of fresh produce is critical for ensuring their microbiological safety. Using spherical produce models (40 mm diameter), the effect of the separation distance (40 mm–200 mm) between adjacent models placed in the flow in an industrial flume washer, on bacterial detachment was investigated. The fluid flow in the flume washer was numerically simulated, and the shear stress on the surface of the produce models was calculated. The numerical simulation indicated that varying the center-to-center distance between produce models from 40 mm to 200 mm increased the average shear stress on the produce models from 264 mPas to 469 mPas. The corresponding experimental data of the number of bacteria that survived on the surface of the produce models after 60 s of washing ranged from 8.6 × 103 CFU/cm2 to 2.7 × 102 CFU/cm2. An empirical model, which incorporated the effect of calculated shear stress on the kinetics of bacteria removal, was developed to predict the number of bacteria that survived on the surface of the produce after washing. The findings in this study are useful for fresh-cut produce processors in optimizing product loading rate without compromising food quality and safety during flume washing.

In this study, a capacitance thermal analyzer (CTA) was designed and tested for measuring the melting properties of chocolates, and compared with those measured by DSC and dynamic rheology. Chocolates with different fat content and particle size distribution (PSD) were placed between stainless steel plates, while capacitance and temperature were monitored between 20 and 60 °C. The PSD did not influence the Tonset (~ 25 °C) and Tpeak (33 °C) measured by DSC. However, samples with finer particles had lower Tend than those with coarser particles (36.59–37.28 °C). Varying fat content did not result in differences in the DSC melting curves. Samples with smaller particle sizes had lower temperatures at peak capacitance than those with larger particles, with peak temperatures ranging from 30.8 to 39.3 °C, while higher peak capacitance values (2.61–2.84 10− 11 F) were measured by CTA. Samples with higher fat content had lower peak temperatures (range 34.7–39.71 °C) but higher peak capacitance values (range 3.29–4.3 10− 11F). Values from the CTA were best correlated with results determined by dynamic thermal rheometry.

Rheological properties of food materials are important as they influence food texture, processing properties, and stability. Rotational rheometry has been widely used for measuring rheological properties. However, the measurements obtained using different geometries and rheometers are generally not compared for precision and accuracy, so it is difficult to compare data across different studies. In this study, nine rheometers from seven laboratories were used to measure the viscosity and viscoelastic properties of a commercial salad dressing. The measurements were obtained at three temperatures (8, 25, and 60 °C) using different diameter parallel plates (20, 40, 50, and 60 mm). Generally, the viscosity measurements among rheometers differed significantly ( P 0.05 ). For larger geometry diameter (40, 50, and 60 mm) and at lower temperatures (8 °C), viscosity measurements at lower shear rate (0.01, 0.1, and 1.0 s-1 ) were significantly different. Rheometer brand significantly affected storage modulus only at low (0.01%) and high levels (10% and 100%) of strain. Temperature was an influencing factor on viscoelastic behaviors only at high strain (>10%). Storage moduli values obtained by frequency sweeps were not affected by rheometer or plate diameter. Overall, rheometer, geometry, and temperature can influence rheological measurements and care should be taken when comparing data across laboratories or published works. Higher shear rates (≥10 s-1 ) and moderate strains (0.1% to 10%) generally provide more repeatable data among different laboratories. PRACTICAL APPLICATION: This study provides information on what factors may potentially influence rheological measurements conducted across different laboratories. It is useful for rheometer users who want to compare their experimental data to published data or compare two sets of published data. It is better to compare data collected at shear rates 10 s-1 and strains between 0.1% and 1.0%.

Cocoa bean fermentation is an important postharvest process that develops aroma and processing properties. Although the cocoa fermentation is of high complexly, farmers are employing empirical methods to determine the fermentation degree of cocoa. Researchers, on the other hand, are using expensive equipment such as high‐performance liquid chromatography and gas chromatography‐mass spectrometry to study cocoa fermentation. In this study, machine learning based electronic nose system, a fast measuring and affordable method, was developed to determine the fermentation degree of cocoa beans. Six machine‐learning methods (bootstrap forest, boosted tree, decision tree, artificial neural network (ANN), naive Bayes, and k‐nearest neighbors) were conducted to classify the fermentation time of cocoa beans. Bootstrap forest algorithm achieved a misclassification rate as low as 9.4%. ANN and boosted tree achieved 12.8 and 13.6% misclassification rate respectively. However, other methods failed to do classification for cocoa beans. PRACTICAL APPLICATIONS: The electronic nose system can be used by cocoa farmers to monitor cocoa fermentation and ensure the quality of cocoa beans. The method is relatively inexpensive and easy to operate.

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.

Outbreaks of foodborne illnesses from fresh produce in recent years have prompted industrial community to consider new practices aimed at reducing the risks of pathogenic microbial contamination on the produce. The presence of organic matter in wash water not only decreases the efficacy of sanitizers to inactivate microorganisms, but also has the potential to transfer microbial contamination to fresh produce. This study aims to comprehensively evaluate the transfer of pathogens from inoculated organic matter to uninoculated fresh produce leaves during washing, as well as determination of the adequate active free chlorine concentration needed to prevent the potential risk of cross-contamination during produce washing process. In addition, the study also characterized the role of particles in increasing the mechanical shear at the leaf surface using numerical simulation. The results showed that cross-contamination of fresh produce occurred significantly in a short time (

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.

Composition and microstructure can significantly influence the rheological and texture behaviors of semisolid food. It is important for food manufacturers to modify their formulations and processing methods to produce products with desirable texture, appearance, and processing properties. This chapter gives an overview on key semisolid food rheological behaviors, including non-Newtonian, viscoelastic, and creep-recovery behaviors, as well as rheological models used for semisolid foods. Examples of and mechanism for modifying rheological and texture behaviors by composition, microstructure, and physical treatments are provided. Typical semisolid foods, including yogurt, salad dressing, mayonnaise, butter spread, and sauce, are used as examples.

The microstructure of semisolid foods can have great impact on their texture, functional properties, and rheological properties. Semisolid food microstructures can be modified during their manufacturing process by either altering processing parameters or adding functional ingredients. Functional ingredients for creating and altering food microstructures include proteins, polysaccharides, and lipids are introduced; key food structural features include protein and polysaccharide networks, emulsions, and foams. Processing strategies that can influence these food microstructures include homogenization, heat treatment, and acidification. Illustration of how formulation and processing can impact semisolid food microstructures can be found in yogurt, whipped cream, and ice cream: adding functional ingredients, such as dietary fibers, gums, calcium, and whey protein isolates, as well as altering their processing parameters, can dramatically change their microstructural features.

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.

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.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%.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.