Iron intake among Lebanese women: sociodemographic factors, iron-rich dietary patterns, and preparation of hummus, a Mediterranean dish.

BACKGROUND: Plant-based foods such as hummus are alternative to animal protein, and when properly prepared, they help to alleviate nutritional iron deficiency that leads to anemia, a global health problem. OBJECTIVE: The objective was to assess iron intake among Lebanese women and related participant's characteristics, discern iron-rich dietary patterns, evaluate their association with nutrients intake and participant's sociodemographic characteristics, and identify the women preparing hummus traditionally and properly for an enhanced iron bioavailability. DESIGN: A cross-sectional study of 400 Lebanese women (18-74 years old) was conducted in Lebanon. Data from a questionnaire, including sociodemographic and health characteristics, dietary intake, and hummus preparation and consumption, were collected. Dietary data were obtained by a food frequency questionnaire and a 24-h recall. Dietary patterns were identified by principal component analysis. Linear regression and binomial logistic regression models were used to explore the association between the intake of dietary iron, its patterns, and the participants' characteristics. RESULTS: About 60% of the women had iron intake deficiency, especially with lower income (odds ratio [OR] = 1.88, 95% confidence interval [CI]: 1.107, 3.194). Four iron-rich dietary patterns were identified: legumes; organ/lunch meat and chicken; canned fish; and beef and hummus. The factor scores of the latter were positively correlated with protein, vitamin C, iron, folate, vitamin B12, and vitamin A with r = 0.195 and P < 0.01 for all. No significant difference was shown among the women's sociodemographic characteristics for the consumption of the hummus-related pattern. Only 9.2 and 22.7% of the women considered proper preparation of chickpea and hummus, respectively, which significantly (P < 0.05) correlated with older women (66.7%). DISCUSSION & CONCLUSION: The majority of the Lebanese women still have iron intake deficiency and the minority reported proper preparation of hummus. Intervention programs spreading awareness among Lebanese women are needed for encouraging adequate iron intake and considering proper steps to improve iron bioavailability from plant-based food.

Lemon Juice, Sesame Paste, and Autoclaving Influence Iron Bioavailability of Hummus: Assessment by an In Vitro Digestion/Caco-2 Cell Model.

Hummus, an iron-containing plant-based dish mainly made from chickpea purée, tahini, lemon juice and garlic, could be a valuable source of iron when bioavailable. Since the processing and formulation of food influence iron bioavailability, the present study investigated for the first time, their effects on hummus. Firstly, iron bioaccessibility was assessed on eight samples (prepared according to the screening Hadamard matrix) by in vitro digestion preceding iron dialysis. Then, iron bioavailability of four selected samples was estimated by the in vitro digestion/Caco-2 cell model. Total and dialyzable iron were determined by the atomic absorption spectrometry and ferritin formation was determined using an ELISA kit. Only autoclaving, among other processes, had a significant effect on iron bioaccessibility (+9.5, p < 0.05). Lemon juice had the highest positive effect (+15.9, p < 0.05). Consequently, the effect of its acidic components were investigated based on a full factorial 23 experimental design; no significant difference was detected. Garlic's effect was not significant, but tahini's effect was negative (-8.9, p < 0.05). Despite the latter, hummus had a higher iron bioavailability than only cooked chickpeas (30.4 and 7.23 ng ferritin/mg protein, respectively). In conclusion, hummus may be a promising source of iron; further in vivo studies are needed for confirmation.

A New pH-Dependent Macrocyclic Rhodamine B-Based Fluorescent Probe for Copper Detection in White Wine.

For efficiently measuring copper (II) ions in the acidic media of white wine, a new chemosensor based on rhodamine B coupled to a tetraazamacrocyclic ring (13aneN4CH2NH2) was designed and synthesized by a one-pot reaction using ethanol as a green solvent. The obtained chemosensor was characterized via NMR, UV and fluorescent spectra. It was marked with no color emission under neutral pH conditions, with a pink color emission under acidic conditions, and a magenta color emission under acidic conditions where copper (II) ions were present. The sensitivity towards copper (II) ions was tested and verified over Ca2+, Ag+, Zn2+, Mg2+, Co2+, Ni2+, Fe2+, Pb2+, Cd2+, Fe3+, and Mn2+, with a detection limit of 4.38 × 10-8 M in the fluorescence spectrum.

An untargeted evaluation of food contact materials by flow injection analysis-mass spectrometry (FIA-MS) combined with independent components analysis (ICA).

Food contact materials (FCMs), especially plastics, are known to be a potential source of contaminants in food. In fact, various groups of additives are used to protect the integrity of the material during processing and life time. However, these intentionally added substances (IAS) could also lead to degradation products called non-intentionally added substances (NIAS), due to reactions occurring in the polymeric material. Complex mixtures of components may therefore be generated within the material, creating a source of potential migrating substances towards food in contact. In this context, an innovative analytical approach is proposed in order to assess IAS and NIAS in plastic FCMs for a fast screening of their composition. For this purpose, solvent extracts of polyethylene (PE) pellets, containers and films were analyzed by flow injection analysis-mass spectrometry (FIA-MS). This direct approach offers the ability to perform a large number of analyses in a short time. Mass spectral fingerprints were then treated by a multivariate data analysis technique called independent components analysis (ICA) in order to overcome the complexity of such data and to highlight hidden information related to IAS and NIAS molecules. ICA applied on mass spectral fingerprints of PE extracts highlighted group discriminations related to different m/z values which were putatively assigned to IAS and also to NIAS. In order to confirm these putative annotations, a hybrid LTQ-Orbitrap was used for high resolution mass spectrometry analysis. Moreover, MS/MS experiments were performed on some discriminant ions to improve their putative identification. The proposed methodology combining FIA-MS fingerprints and ICA proved its efficiency in identifying IAS and NIAS in plastic FCMs and its capability to discriminate different PE samples, in a relatively fast approach compared to classical analytical techniques. This approach may help the FCMs classification for compounders in the selection of the starting substances in plastic formulation and for plastic converters in the control of manufacturing processes as well as for the monitoring of final products.

Attenuated total reflectance-mid infrared spectroscopy (ATR-MIR) coupled with independent components analysis (ICA): A fast method to determine plasticizers in polylactide (PLA).

Compliance of plastic food contact materials (FCMs) with regulatory specifications in force, requires a better knowledge of their interaction phenomena with food or food simulants in contact. However these migration tests could be very complex, expensive and time-consuming. Therefore, alternative procedures were introduced based on the determination of potential migrants in the initial material, allowing the use of mathematical modeling, worst case scenarios and other alternative approaches, for simple and fast compliance testing. In this work, polylactide (PLA), plasticized with four different plasticizers, was considered as a model plastic formulation. An innovative analytical approach was developed, based on the extraction of qualitative and quantitative information from attenuated total reflectance (ATR) mid-infrared (MIR) spectral fingerprints, using independent components analysis (ICA). Two novel chemometric methods, Random_ICA and ICA_corr_y, were used to determine the optimal number of independent components (ICs). Both qualitative and quantitative information, related to the identity and the quantity of plasticizers in PLA, were retrieved through a direct and fast analytical method, without any prior sample preparations. Through a single qualitative model with 11 ICs, a clear and clean classification of PLA samples was obtained, according to the identity of plasticizers incorporated in their formulations. Moreover, a quantitative model was established for each formulation, correlating proportions estimated by ICA and known concentrations of plasticizers in PLA. High coefficients of determination (higher than 0.96) and recoveries (higher than 95%) proved the good predictability of the proposed models.

Validation of Direct Analysis Real Time source/Time-of-Flight Mass Spectrometry for organophosphate quantitation on wafer surface.

Microelectronic wafers are exposed to airborne molecular contamination (AMC) during the fabrication process of microelectronic components. The organophosphate compounds belonging to the dopant group are one of the most harmful groups. Once adsorbed on the wafer surface these compounds hardly desorb and could diffuse in the bulk of the wafer and invert the wafer from p-type to n-type. The presence of these compounds on wafer surface could have electrical effect on the microelectronic components. For these reasons, it is of importance to control the amount of these compounds on the surface of the wafer. As a result, a fast quantitative and qualitative analytical method, nondestructive for the wafers, is needed to be able to adjust the process and avoid the loss of an important quantity of processed wafers due to the contamination by organophosphate compounds. Here we developed and validated an analytical method for the determination of organic compounds adsorbed on the surface of microelectronic wafers using the Direct Analysis in Real Time-Time of Flight-Mass Spectrometry (DART-ToF-MS) system. Specifically, the developed methodology concerns the organophosphate group.

Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil.

Olive oil is one of the most valued sources of fats in the Mediterranean diet. Its storage was generally done using glass or metallic packaging materials. Nowadays, plastic packaging has gained worldwide spread for the storage of olive oil. However, plastics are not inert and interaction phenomena may occur between packaging materials and olive oil. In this study, extra virgin olive oil samples were submitted to accelerated interaction conditions, in contact with polypropylene (PP) and polylactide (PLA) plastic packaging materials. 3D-front-face fluorescence spectroscopy, being a simple, fast and non destructive analytical technique, was used to study this interaction. Independent components analysis (ICA) was used to analyze raw 3D-front-face fluorescence spectra of olive oil. ICA was able to highlight a probable effect of a migration of substances with antioxidant activity. The signals extracted by ICA corresponded to natural olive oil fluorophores (tocopherols and polyphenols) as well as newly formed ones which were tentatively identified as fluorescent oxidation products. Based on the extracted fluorescent signals, olive oil in contact with plastics had slower aging rates in comparison with reference oils. Peroxide and free acidity values validated the results obtained by ICA, related to olive oil oxidation rates. Sorbed olive oil in plastic was also quantified given that this sorption could induce a swelling of the polymer thus promoting migration.

Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA).

Plastic packaging wastes increased considerably in recent decades, raising a major and serious public concern on political, economical and environmental levels. Dealing with this kind of problems is generally done by landfilling and energy recovery. However, these two methods are becoming more and more expensive, hazardous to the public health and the environment. Therefore, recycling is gaining worldwide consideration as a solution to decrease the growing volume of plastic packaging wastes and simultaneously reduce the consumption of oil required to produce virgin resin. Nevertheless, a major shortage is encountered in recycling which is related to the sorting of plastic wastes. In this paper, a feasibility study was performed in order to test the potential of an innovative approach combining mid infrared (MIR) spectroscopy with independent components analysis (ICA), as a simple and fast approach which could achieve high separation rates. This approach (MIR-ICA) gave 100% discrimination rates in the separation of all studied plastics: polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), polystyrene (PS) and polylactide (PLA). In addition, some more specific discriminations were obtained separating plastic materials belonging to the same polymer family e.g. high density polyethylene (HDPE) from low density polyethylene (LDPE). High discrimination rates were obtained despite the heterogeneity among samples especially differences in colors, thicknesses and surface textures. The reproducibility of the proposed approach was also tested using two spectrometers with considerable differences in their sensitivities. Discrimination rates were not affected proving that the developed approach could be extrapolated to different spectrometers. MIR combined with ICA is a promising tool for plastic waste separation that can help improve performance in this field; however further technological improvements and developments are required before it can be applied at an industrial level given that all tests presented here were performed under laboratory conditions.

Chemometric tools to highlight possible migration of compounds from packaging to sunflower oils.

Polyethylene terephthalate (PET) could be considered for the packaging of vegetable oils taking into account the impact of its oxygen permeability on the oxidation of the oil and the migration of volatile organic compounds (VOC) from the polymer matrix. After accelerated aging tests at 40 °C for 10, 20, and 30 days, the headspace of three sunflower oils packed in PET with high density polyethylene caps was carried out using solid phase microextraction. VOCs such as benzene hydrocarbons, ethylbenzene, xylene isomers and diethyl phthalate were identified in vegetable oils by gas chromatography coupled to mass spectrometry. Chemometric tools such as principal components analysis (PCA), independent components analysis (ICA), and a multiblocks analysis, common components and specific weight analysis (CCSWA) applied to analytical data were revealed to be very efficient to discriminate between samples according to oil oxidation products (hexanal, heptanal, 2-pentenal) and to the migration of packaging contaminants (xylene).

Chemometric tools to highlight non-intentionally added substances (NIAS) in polyethylene terephthalate (PET).

In an effort to identify non-intentionally added substances (NIAS), which is still a challenging task for analytical chemists, PET pellets, preforms and bottles were analyzed by an optimized headspace solid phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS). Fingerprints obtained by the proposed method were analyzed by three chemometric tools: Principal Components Analysis (PCA), Independent Components Analysis (ICA) and a multi-block method (Common Components and Specific Weights Analysis CCSWA) in order to extract pertinent variations in NIAS concentrations. Total ion current (TIC) chromatograms were used for PCA and ICA while extracted ion chromatograms (EIC) were used for CCSWA, each ion corresponding to a block. PCA managed to discriminate pellets and preforms from bottles due to several NIAS. Volatiles like 2-methyl-1,3-dioxolane, ethylene glycol, ethylbenzene and xylene were responsible for the discrimination of pellets and preforms. Less volatile compounds like linear aldehydes and phthalates were responsible for the discrimination of bottles. ICA showed more specific discriminations especially for bottles and pellets while CCSWA managed to discriminate preforms. The proposed methodology, combining HS-SPME/GC-MS with chemometric tools proved its efficiency in highlighting NIAS in PET samples in a relatively simple and fast approach compared to classical techniques.