Spinach is a significant source of vitamins, minerals, and antioxidants. These nutrients make it delicious and beneficial for human health. However, the genetic mechanism underlying the accumulation of nutrients in spinach remains unclear. In this study, we analyzed the content of chlorophyll a, chlorophyll b, oxalate, nitrate, crude fiber, soluble sugars, manganese, copper, and iron in 62 different spinach accessions. Additionally, 3,356,182 high-quality, single-nucleotide polymorphisms were found using resequencing and used in a genome-wide association study. A total of 2077 loci were discovered that significantly correlated with the concentrations of the nutritional elements. Data mining identified key genes in these intervals for four traits: chlorophyll, oxalate, soluble sugar, and Fe. Our study provides insights into the genetic architecture of nutrient variation and facilitates spinach breeding for good nutrition.

The main principle in the enrichment of food with minor bioactive compounds is the prediction and evaluation of possible chemical interactions of the components included in the matrix of the food. These interactions have a impact on the bioavailability of minor bioactive compounds. In our work, we studied the processes of sorption and desorption (release), the main processes affecting the bioavailability of the minor bioactive compound ecdysterone (20 E) in the composition of functional food ingredients obtained from spinach leaves (FFI-1) and quinoa grains (FFI-2) on hydrocolloid matrix - inulin. The objective of the research was to study the completeness of sorption-desorption processes of 20 E in adaptogenic compositions with inulin and functional food ingredients based on spinach and quinoa under the influence of hydrolytic enzymes of the gastrointestinal tract (GIT) in vitro. Material and methods. To obtain experimental compositions, containing FFI-1 and FFI-2 and the polysaccharide (inulin), a mechanical mixing method was used. To study the sorption properties, model solutions of the compositions were prepared. Using an in vitro enzymatic model, the ability of 20 E to be released from the matrix of the compositions was studied. The content of 20 E was determined by HPLC-MS/MS. Results. 6 compositions with different ratios of polysaccharide/FFI were obtained. At the first stage of the study, the maximum sorption of 20E in the model solution was observed for 4 compositions with the ratio of inulin : FFI = 2.50 or 3.75 g : 189.19 mg FFI-1 or 68.40 mg FFI-2. At the second stage of the study, when assessing the desorption of 20 E on the enzymatic GIT model, it was found that 20 E almost completely released only from 2 compositions, in other cases about 25% of 20 E remained in a bound state. Conclusion. The formulation of two compositions with the ratio of inulin (2.50 g) : FFI-1 (189.19 mg)/FFI-2 (68.40 mg) were obtained, which have the most optimal sorption / release parameters of 20 E under the influence of human gastrointestinal enzymes. These compositions can be considered promising for inclusion in the formulation of fortified foods.

BACKGROUND: Women with polycystic ovary syndrome (PCOS) have higher intestinal mucosal permeability, leading to the lipopolysaccharide (LPS) leakage and endotoxemia. This, in turn, leads to oxidative stress (OS) and neuro-inflammation caused by the gut-brain axis, affecting the neurotrophic factors levels such as brain-derived neurotrophic factor (BDNF) and S100 calcium-binding protein B (S100 B) levels. In this study, it was hypothesized that the thylakoid membranes of spinach supplementation along with a hypocaloric diet may have improved the LPS levels, neurotrophic factors, and OS in PCOS patients.
METHODS: In this double-blind, randomized, placebo-controlled, and clinical trial, 48 women with obesity and diagnosed with PCOS based on Rotterdam criteria were randomly assigned to thylakoid (N = 21) and placebo groups (N = 23). A personalized hypocaloric diet with 500 calories less than the total energy expenditure was prescribed to all patients. The participants were daily supplemented with either a 5 g/day thylakoid-rich spinach extract or a placebo (5 g cornstarch) for 12 weeks along with a prescribed low-calorie diet. Anthropometric measurements and biochemical parameters were assessed at baseline and after the 12-week intervention.
RESULTS: A statistically significant decrease in the LPS levels (P < 0.001) and an increase in the BDNF levels (P < 0.001) were recorded for the participants receiving the oral thylakoid supplements and a low-calorie diet. Furthermore, significant decreases were observed in fasting blood glucose, insulin, homeostatic model of assessment for insulin resistance, free testosterone index, and follicle-stimulating hormone / luteinizing hormone ratio in both groups (P < 0.05). No significant differences were detected between the two groups regarding the changes in malondialdehyde, catalase, total antioxidant capacity, and S100B levels (P > 0.05).
CONCLUSIONS: In sum, the thylakoid membranes of spinach supplemented with a hypocaloric diet reduced the LPS levels, increased the BDNF levels, and improved the glycemic profile and sex-hormone levels; however, they had no effects on the OS markers levels after 12 weeks of intervention.

The FDA Bacteriological Analytical Manual (BAM) Salmonella culture method takes at least 3 days for a presumptive positive result. The FDA developed a quantitative PCR (qPCR) method to detect Salmonella from 24-h preenriched cultures, using ABI 7500 PCR system. The qPCR method has been evaluated as a rapid screening method for a broad range of foods by single laboratory validation (SLV) studies. The present multi-laboratory validation (MLV) study was aimed to measure the reproducibility of this qPCR method and compare its performance with the culture method. Sixteen laboratories participated in two rounds of MLV study to analyze twenty-four blind-coded baby spinach test portions each. The first round yielded ∼84% and ∼82% positive rates across laboratories for the qPCR and culture methods, respectively, which were both outside the fractional range (25%-75%) required for fractionally inoculated test portions by the FDA\'s Microbiological Method Validation Guidelines. The second round yielded ∼68% and ∼67% positive rates. The relative level of detection (RLOD) for the second-round study was 0.969, suggesting that qPCR and culture methods had similar sensitivity (p > 0.05). The study demonstrated that the qPCR yields reproducible results and is sufficiently sensitive and specific for the detection of Salmonella in food.

Clostridioides difficile is a human pathogen that is ubiquitous in soil. Despite increasing infection rates and evidence of foodborne transmission, there is limited data on prevalence in soil or which factors influence persistence. The aim of this study was to investigate the prevalence of these bacteria in soil from three different spinach fields and to examine the chemical composition (carbon, organic carbon, nitrogen, organic matter, minerals and pH) and microbiota to gain insight into the factors that may promote/inhibit C. difficile. The overall C. difficile prevalence (10%) was lower than expected (based on international studies) and a significantly (P < 0.05) higher prevalence was obtained in Field 3 (20%) as compared to Fields 1 and 2 (5% each). Analysis of the soil suggested that the pH as well as organic matter, calcium and phosphorus content directly and indirectly (via the microbiota) influenced the prevalence of C. difficile in adjacent fields, where other factors (eg. climate) are similar. Although further studies are required to validate our findings, the data provides the first step in developing potential soil based control strategies.

The accumulation of six pharmaceuticals of different therapeutic uses has been thoroughly investigated and compared between onion, spinach, and radish plants grown in six soil types. While neutral molecules (e.g., carbamazepine (CAR) and some of its metabolites) were efficiently accumulated and easily translocated to the plant leaves (onion > radish > spinach), the same for ionic (both anionic and cationic) molecules seems to be minor to moderate. The maximum accumulation of CAR crosses 38,000 (onion), 42,000 (radish), and 7000 (spinach) ng g-1 (dry weight) respectively, in which the most majority of them happened within the plant leaves. Among the metabolites, the accumulation of carbamazepine 10,11-epoxide (EPC - a primary CAR metabolite) was approximately 19,000 (onion), 7000 (radish), and 6000 (spinach) ng g-1 (dry weight) respectively. This trend was considerably similar even when all these pharmaceuticals applied together. The accumulation of most other molecules (e.g., citalopram, clindamycin, clindamycin sulfoxide, fexofenadine, irbesartan, and sulfamethoxazole) was restricted to plant roots, except for certain cases (e.g., clindamycin and clindamycin sulfoxide in onion leaves). Our results clearly demonstrated the potential role of this accumulation process on the entrance of pharmaceuticals/metabolites into the food chain, which eventually becomes a threat to associated living biota.

Heavy metals in soil pose a serious threat through their toxic effect on the human food chain. Phytoremediation is a clean and green potentially cost-effective technology in remediating the heavy metal-contaminated soil. However, the efficiency of phytoextraction is very often limited by low phytoavailability of heavy metals in soil, slow growth, and small biomass production of hyper-accumulator plants. To solve these issues, accumulator plant(s) with high biomass production and amendment(s) which can solubilize metals in soil is required for better phytoextraction. A pot experiment was conducted to assess the efficiency of phytoextraction of sunflower, marigold, and spinach as affected by the incorporation of Sesbania (solubilizer) and addition of gypsum (solubilizer) in nickel (Ni)-, lead (Pb)-, and chromium (Cr)-contaminated soil. A fractionation study was conducted to study the bioavailability of the heavy metals in contaminated soil after growing the accumulator plants and as affected by using soil amendments (Sesbania and gypsum). Results showed that marigold was the most efficient among the three accumulator plants in phytoextraction of the heavy metals in the contaminated soil. Both sunflower and marigold were able to reduce the bioavailability of the heavy metals in the post-harvest soil, which was reflected in their (heavy metals) lower concentration in subsequently grown paddy crop (straw). The fractionation study revealed that carbonate and organically bound fractions of the heavy metals control the bioavailability of the heavy metals in the experimental soil. Both Sesbania and gypsum were not effective in solubilizing the heavy metals in the experimental soil. Therefore, the possibility of using Sesbania and gypsum for solubilizing heavy metals in contaminated soil is ruled out.

Excessive Cd accumulation in rice grain has caused chronic Cd diseases in humans. In most crops, 100 times more Zn than Cd strongly inhibits Cd uptake and translocation. However, this response is not found for rice (Oryza sativa L.), which was found to have an unusual Cd uptake pattern compared with other crops, such as spinach (Spinacia oleracea L.). Moreover, studies on shared transporters between Zn and Cd using normal solution experiments with traditional high concentrations of metal ions may result in irrelevant interactions. Therefore, we developed ethyleneglycoltetraacetate-buffered nutrient solutions in this work. Rice and spinach seedlings were grown under calibrated low Cd2+ activity and low to phytotoxic Zn2+ activity levels while buffering other micronutrient cations at sufficient levels. Results showed that as rice grew with pZn2+  = 8.1-5.4, root Cd and shoot Ni decreased significantly and gradually. However, shoot Cd and Mn in rice decreased slightly with the increase of solution Zn2+ from deficiency to sufficiency and then increased at toxic Zn2+ solution (pZn2+  = 5.4). The shoot/root ratios of Cd in rice under toxic pZn2+ (5.6 and 5.4 pZn2+ activity) were significantly increased (p < .05). It could be concluded that rice absorption of Cd is not inhibited by co-contaminating (toxic) Zn. For spinach, with Zn varying from pZn2+  = 8.1-5.7, both shoot and root Cd substantially decreased, as did shoot Ni. This work revealed that, to understand food chain Cd risks, one needs to consider the inhibitory role of Zn in limiting Cd absorption in all crops studied except rice.

Ready-to-eat (RTE) and fresh-cut vegetables meet the current needs for healthy and easy-to-prepare food. However, raw vegetables are widely known to harbor large and diverse bacterial communities promoting spoilage and reducing their shelf-life. A better understanding of their bacterial community and the impact of various environmental factors on its composition is essential to ensure the production of high-quality fresh-cut produce. Therefore, a metagenetic amplicon approach, based on gyrB sequencing, was applied for deciphering the bacterial communities associated with the spoilage of RTE rocket and baby spinach and monitoring the changes occurring in their composition during storage at different temperatures. Our results indicated that Pseudomonas genus was the main spoilage group for both leafy vegetables. Specifically, Pseudomonas viridiflava was dominant in most samples of rocket, while a new Pseudomonas species as well as, Pseudomonas fluorescens and/or Pseudomonas fragi were highly abundant in baby spinach. A significant variability on bacterial species composition among different batches of each vegetable type was observed. In the case of baby spinach, the impact of temperature and/or storage time on bacterial microbiota was not explicitly revealed at batch-level. Concerning rocket, the storage time was the most influential factor resulting in the reduction of Pseudomonas species\' abundances and the parallel increase of lactic acid bacteria abundances. The results suggest that a large-scale sampling and further investigation of the various environmental factors shaping the microbiota are needed for gaining deeper knowledge of the diverse bacterial communities on RTE leafy vegetables and thus, enhance the quality of these products.

Although diffuse reflectance spectroscopy (DRS) measurements can be collected rapidly and simultaneously, the resulting datasets are imbalanced and redundant due to the highly correlated spectral features collected on relatively few samples. Consequently, modelling these datasets using machine learning (ML) techniques is challenging and necessitates longer training times and more computational resources. Furthermore, models developed with such data are frequently prone to overfitting, resulting in promising but often non-reproducible results. We demonstrate the advantage of using an eigenvector decomposition principal component analysis (PCA) in reducing the dimensionality and data mining of DRS measurements in the short near-infrared region (750-900 nm). A total of 547 DRS measurements consisting of 151 wavelengths were acquired from spinach samples sprayed with two different pesticides and control samples. The measurements were later preprocessed with a Savitzky-Golay filter and multiplicative scatter analysis. After performing PCA on the preprocessed data, two principal components (PCs) that explained 77% of the cumulative variance and maximized the interclass variation were extracted and used as inputs to three ML models namely; artificial neural networks, support vector machine and random forest, to classify the samples. Re-sampling was used to tune the models and avoid overfitting. The performance of the models was compared using raw DRS data, pre-processed (PP) DRS data, and PCs data. The results show that pesticide classification using PCs data requires the least amount of training time (average 2.4 s) for all the models, and achieves 100% classification accuracy. In addition, it was observed that spectral data pre-processing improves accuracy and training time when compared to using raw spectral data. These findings are particularly encouraging since they demonstrate the possibility of developing rapid and accurate classification models for screening pesticide residues in fresh produce based on DRS measurements with minimal computational resources.