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The use of saline water under drought conditions is critical for sustainable agricultural development in arid regions. Biochar is used as a soil amendment to enhance soil properties such as water-holding capacity and the source of nutrition elements of plants. Thus, the research was carried out to assess the impact of biochar treatment on the morphological and physiological characteristics and production of Solanum lycopersicum in greenhouses exposed to drought and saline stresses. The study was structured as a three-factorial in split-split-plot design. There were 16 treatments across three variables: (i) water quality, with freshwater and saline water, with electrical conductivities of 0.9 and 2.4 dS m- 1, respectively; (ii) irrigation level, with 40%, 60%, 80%, and 100% of total evapotranspiration (ETC); (iii) and biochar application, with the addition of biochar at a 3% dosage by (w/w) (BC3%), and a control (BC0%). The findings demonstrated that salt and water deficiency hurt physiological, morphological, and yield characteristics. Conversely, the biochar addition enhanced all characteristics. Growth-related parameters, such as plant height, stem diameter, leaf area, and dry and wet weight, and leaf gas exchange attributes, such rate of transpiration and photosynthesis, conductivity, as well as leaf relative water content were decreased by drought and salt stresses, especially when the irrigation was 60% ETc or 40% ETc. The biochar addition resulted in a substantial enhancement in vegetative growth-related parameters, physiological characteristics, efficiency of water use, yield, as well as reduced proline levels. Tomato yield enhanced by 4%, 16%, 8%, and 3% when irrigation with freshwater at different levels of water deficit (100% ETc, 80% ETc, 60% ETc, and 40% ETc) than control (BC0%). Overall, the use of biochar (3%) combined with freshwater shows the potential to enhance morpho-physiological characteristics, support the development of tomato plants, and improve yield with higher WUE in semi-arid and arid areas.

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Romaine lettuce in the U.S. is primarily grown in California or Arizona and either processed near the growing regions (source processing) or transported long distance for processing in facilities serving distant markets (forward processing). Recurring outbreaks of Escherichia coli O157:H7 implicating romaine lettuce in recent years, which sometimes exhibited patterns of case clustering in Northeast and Midwest, have raised industry concerns over the potential impact of forward processing on romaine lettuce food safety and quality. In this study, freshly harvested romaine lettuce from a commercial field destined for both forward and source processing channels was tracked from farm to processing facility in two separate trials. Whole-head romaine lettuce and packaged fresh-cut products were collected from both forward and source facilities for microbiological and product quality analyses. High-throughput amplicon sequencing targeting16S rRNA gene was performed to describe shifts in lettuce microbiota. Total aerobic bacteria and coliform counts on whole-head lettuce and on fresh-cut lettuce at different storage times were significantly (p < 0.05) higher for those from the forward processing facility than those from the source processing facility. Microbiota on whole-head lettuce and on fresh-cut lettuce showed differential shifting after lettuce being subjected to source or forward processing, and after product storage. Consistent with the length of pre-processing delays between harvest and processing, the lettuce quality scores of source-processed romaine lettuce, especially at late stages of 2-week storage, was significantly higher than of forward-processed product (p < 0.05).

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Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation's output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model's output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

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Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k-mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food. IMPORTANCE: Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k-mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

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In recent years, the use of natural products such as essential oils (EOs) and other plant extracts for the preservation of fresh produce has attracted much interest from the food industry. Many endemic medicinal and aromatic plants, such as Cypriot oregano (Origanum dubium), present a plethora of properties that can be utilized by the fruit and vegetable sectors of the food industry. The purpose of the present study was to assess the effects of O. dubium EO and hydrosol (at different concentrations and durations of dipping application) for the preservation of tomato and cucumber fruit quality, and their effectiveness as sanitizing agents against two foodborne pathogens (Listeria monocytogenes and Salmonella enterica). The results of this study indicated that increased concentrations of EO, combined with a longer duration of application, resulted in less marketable fruit compared to hydrosol application. Interestingly, EO application at lower concentrations and shorter durations of application (i.e., 0.01% for 5 min) increased fruit antioxidant, ascorbic acid and carotenoid levels (for tomato fruit), suggesting an increase in the nutritional value of the treated fruit, compared to the control. EO and hydrosol were able to decrease the bacterial populations (both bacteria) on fruits. Both products were especially effective against L. monocytogenes, even seven days after their application and storage at 11 °C (up to an approx. 3 log reduction with the EO application). Overall, the results of this study suggest that the use of O. dubium EO and hydrosol could be considered as alternative sanitation means for tomatoes and cucumbers.

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Protein hydrolysate biostimulants are environmentally friendly options for the reduction of nitrogen input, but their plant growth-promoting mechanisms are still not completely unveiled. Here, to put the "signaling peptide theory" to the test, a greenhouse experiment was undertaken using low (1 mM) and optimal (8 mM) NO3-treated butterhead lettuce and three molecular fractions (PH1 (>10 kDa), PH2 (1−10 kDa) and PH3 (<10 kDa) fractions), in addition to the whole product Vegamin®: PH, in a randomized block design. PH1 and PH3 significantly increased fresh yield (+8%) under optimal (lighter leaves), but not under low (darker leaves) NO3 conditions. Total ascorbic acid, lutein and ß-carotene increased with PH3, and disinapoylgentobiose and kaempferol-3-hydroxyferuloyl-sophorosie-7-glucoside content increased with PH (whole/fractions) treatments, particularly under low NO3 conditions. The complete hydrolysate and analyzed peptide fractions have differential biostimulatory effects, enhancing the growth and nutritional quality of lettuce.

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The influence of aqueous ozone (1.4 mg/L) treatment for 1, 5, and 10 min on the microbial growth and quality attributes of fresh-cut cabbage during storage at 4°C for 12 days was evaluated. The pesticide residue removal effect of aqueous ozone treatment for 5 min was also determined. The results show that the growth rates of aerobic bacteria, coliforms, and yeasts were significantly inhibited (p < .05) by aqueous ozone treatment during storage; treatment for 10 min showed the greatest inactivation of bacteria, coliforms, and molds. Aqueous ozone stimulated initial respiratory metabolism compared with that of the control. Aqueous ozone treatments reduced ethylene production and improved the overall quality of fresh-cut cabbage. In addition, the effect of aqueous ozone treatment for 5 min on the removal of trichlorfon, chlorpyrifos, methomyl, dichlorvos, and omethoate from fresh-cut cabbage was greater (p < .05) than that of the control. These results indicate that aqueous ozone treatment for 5 min could be an economic and effective method to remove pesticide residues and enhance the storability of fresh-cut cabbage.

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Maintaining microbial safety and quality of fresh fruits and vegetables are a global concern. Harmful microbes can contaminate fresh produce at any stage from farm to fork. Microbial contamination can affect the quality and shelf-life of fresh produce, and the consumption of contaminated food can cause foodborne illnesses. Additionally, there has been an increased emphasis on the freshness and appearance of fresh produce by modern consumers. Hence, disinfection methods that not only reduce microbial load but also preserve the quality of fresh produce are required. Chlorine dioxide (ClO2) has emerged as a better alternative to chlorine-based disinfectants. In this review, we discuss the efficacy of gaseous and aqueous ClO2 in inhibiting microbial growth immediately after treatment (short-term effect) versus regulating microbial growth during storage of fresh produce (long-term effect). We further elaborate upon the effects of ClO2 application on retaining or enhancing the quality of fresh produce and discuss the current understanding of the mode of action of ClO2 against microbes affecting fresh produce.

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This study evaluated the effects of electrolyzed oxidizing (EO) water treatment on the removal of pesticide residues (diazinon, cyprodinil and phosmet) from spinach, snap beans and grapes, and the effect on produce quality. High available chlorine content (ACC) and long treatment time of EO water resulted in high pesticide removals. Up to 59.2, 66.5 and 37.1% of diazinon; 43.8, 50.0 and 31.5% of cyprodinil; 85.7, 73.0 and 49.4% of phosmet; were removed from spinach, snap beans and grapes, respectively, after 15min EO water treatment at 120mg/l ACC. EO water was also more effective than electrolyzed reduced water, bleach, VegWash and DI water on pesticide removal. In addition, no significant colour or texture deterioration were found on produce samples treated with EO water. It was concluded, EO water can be very effective in pesticide residue removal from fresh produce without affecting the produce quality.

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