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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.

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.

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.

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.

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 (