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ABSTRACT: Eggs in the United States are typically washed using chemical sanitizers such as quaternary ammonia (QA) or chlorine. Such treatments generate wash water, which could be potentially hazardous to the environment. A novel, nonthermal sanitization technique for washing shell eggs using cold plasma-activated water (PAW) was investigated in this study. The inactivation efficacy of PAW on Klebsiella michiganensis and the impact of PAW on the cuticle of the eggshell and shell strength were tested in comparison to QA. Washing inoculated eggs with PAW and QA achieved a similar microbial reduction (>5.28 log CFU/egg). Colorimetric analysis showed that ∆E-value for PAW-treated eggs was significantly lower than QA-treated eggs, suggesting higher cuticle coverage in eggs treated with PAW. The texture analysis to test for shell egg strength indicated that washing eggs with PAW did not affect the structural integrity of the eggshell when compared to eggs washed with QA. According to this study, PAW has the potential as an alternative to commercial sanitizers like QA in the egg-washing industry. PAW does not detrimentally impact shell strength or cuticle coverage and provides similar microbial reduction efficacy.

The determination of the levels of roasting of cocoa relies on expensive analytical equipment, sensory panel, and, in the cases of small processors and growers, empiricism. In this study, cocoa beans were roasted for 10–40 min to obtain different levels of roasting, and the images of the beans were captured by a smartphone camera. An extreme learning machine (ELM)-based algorithm was developed to predict the roasting degree of cocoa beans using the images of the cocoa bean cross-sections. A 22-dimension feature vector, including color and texture features, is extracted from each sample, and a total of 350 samples are used to train an ELM network. A majority rule-based voting method was used to make the decision. Experimental results showed that the proposed method achieved a classification accuracy of 93.75%. GC-MS analysis was conducted to determine the chemical compounds in the raw and roasted cocoa beans, and enrichment analysis, principal components analysis, partial least-squares–discriminant analysis, and Pearson correlation analysis were conducted to identify major chemicals respond to roasting time and classify the cocoa beans samples. Caffeine and theobromine were identified as primary chemical compounds that responded to roasting time, and cocoa beans with different levels of roasting were successfully classified.

Muscadine grape pomace and mixed products with chocolate extracts from three muscadine genotypes exhibiting different berry skin colors (black and bronze) were investigated for total phenolic content (TPC), total flavonoid content (TFC), DPPH, FRAP antioxidant activity, and anticancer activity using MDA-MB-468 (MM-468; African American) breast cancer cells. Muscadine berry extracts and mixed products showed cytotoxicity activities of up to 70% against MM-468 breast cancer cells. Cell growth inhibition was higher in ‘macerated Floriana’ with an IC50 value of 20.70 ± 2.43 followed by ‘Alachua’ with an IC50 value of 22.25 ± 2.47. TPC and TFC in macerated MGP powder were (1.4 ± 0.14 and 0.45 ± 0.01 GAE/g FW, respectively), which was significantly higher than those in cocoa powder. Data analysis showed a high association between DPPH, FRAP antioxidant activities, and TPC content and a positive high correlation between anticancer activity and antioxidant capacity and between TPC and anticancer activity. The anticancer and antioxidant effects of muscadine grape pomace and chocolate extracts are attributed to the TPC of extracts, which showed a stronger positive correlation with growth inhibition of African American breast cancer cells. This study would be of great value for food industries as well as other manufacturers who are interested in new food blends.

Hydroponic sweet basil (O. basilicum L.) farming uses a recirculating nutrient solution that may spread waterborne microbial contamination including algae. Plasma, the fourth state of matter, generates antimicrobial reactive oxygen and nitrogen species when exposed to water. The objective of this work was to study the effect of plasma-treated water-based nutrient solution on plant growth and in reduction of algae. Basil plants were grown in isolated ebb and flow hydroponic systems (under monitored environmental conditions) using nutrient solution (NS) and plasma-activated nutrient solution (PANS) with two separate treatments: the same irrigation solutions were used in the growth cycle (Treatment 1: NST1 and PANST1 once at the beginning growth cycle) and new irrigation solutions at every week of the growth cycle (Treatment 2: NST2 and PANST2). The plant growth parameters (height, fresh and dry weight, number of branches and nodes, root length, leaf index), quality parameters (color, texture, aroma, and tissue nutrients concentration), and algae concentrations were measured. Compared to NST1, plants grown on PANST1 were significantly taller (up to 12%), had a higher fresh mass (up to 29%) and dry mass (up to 45%), and had a higher greenness value (up to 28%). Algae growth was significantly reduced in the PANST2 reservoir (up to 24%) compared to the NST2 reservoir. It was confirmed that Treatment 1 significantly improved the yield, morphology, and quality of sweet basil plants, while Treatment 2 was best suited to decreasing algae concentration in the hydroponic environment. This preliminary study indicated that PANS could improve the quality and growth of sweet basil in hydroponic farming while controlling the algae growth in the growing environment.

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.