The susceptibility of lipids to oxidation is a major cause of quality deterioration in food emulsions. The reaction mechanism and factors that influence oxidation are appreciably different for emulsified lipids than for bulk lipids. This article reviews the current understanding of the lipid oxidation mechanism in oil-in-water emulsions. It also discusses the major factors that influence the rate of lipid oxidation in emulsions, such as antioxidants, chelating agents, ingredient purity, ingredient partitioning, interfacial characteristics, droplet characteristics, and ingredient interactions. This knowledge is then used to define effective strategies for controlling lipid oxidation in food emulsions.

A multivariate method based on solvent terminated dispersive liquid–liquid microextraction was developed for the determination of Cu²⁺ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22–140, 0.08 and 0.22 µg L⁻¹, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L⁻¹ were in the range of 85–98%.

A new kind of solid phase extraction (SPE), which we named in situ surfactant-based solid phase extraction (ISS-SPE), represents a simple, selective and rapid method for preconcentration and determination of manganese from food and water samples. This method has distinct advantages: extraction times are short and recoveries are high; further, we can see formation of fine particles of large specific surface and their good dispersion in the solution. In this work, a small amount of cationic surfactant, n-dodecyltrimethylammonium bromide (DTAB) was injected into the water sample containing Mn ions, which were complexed by 1-(2-pyridylazo)-2-naphthol (PAN). After shaking, a little volume of NaPF6 as an ion-pairing agent was added into the solution by a microsyringe. After preconcentration, the settled phase was dissolved in a specific volume of ethanol and then aspirated into the flame atomic absorption spectrometer by using a homemade microsample introduction system. The effective parameters such as pH, concentration of surfactant, concentration of chelating agent, concentration of ion-pairing agent and effect of salt concentration were optimized by a fractional factorial design to identify the most important parameters and their interactions, and central composite methodology was used to achieve the optimum point of effective parameters to the response. Under the optimum conditions, preconcentration of 10 ml of the sample solution permitted detection of 0.88 μg l−1 with enhancement factor of 45.6, and the relative standard deviation (RSD) for five determinations of Mn ions was 3.5%. The developed method was applied to the determination of trace manganese in various real samples with satisfactory results.

A solid phase extraction procedure has been developed using multiwalled carbon nanotubes (MWCNTs) as a solid sorbent and quinalizarin [1,2,5,8-tetrahydroxyanthracene-9,10-dione] as a chelating agent for separation and preconcentration of trace amounts of some heavy metal ions, Cd(II), Cu(II), Ni(II), Pb(II) and Zn(II) before their determination by flame atomic absorption spectroscopy (FAAS). The influences of the analytical parameters, including pH, amounts of quinalizarin and adsorbent, sample volume, elution conditions such as volume and concentration of eluent, flow rates of solution and matrix ions, were investigated for the optimum recoveries of the analyte ions. No interference effects were observed from the foreign metal ions. The preconcentration factor was 100. The detection limit (LOD) for the investigated metals at the optimal conditions were observed in the range of 0.30–0.65 μg L ⁻¹. The relative standard deviation (RSDs), and the recoveries of standard addition for this method were lower than 5.0% and 96–102%, respectively. The new procedure was successfully applied to the determination of analytes in food, water and environmental samples with satisfactory results.

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd²⁺ and Cu²⁺ ions in aqueous solutions. Multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN–MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd²⁺ and 98.3% for Cu²⁺). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N = 3), the quantitation limit(S/N = 10), and the linear range for Cu²⁺ were 0.05, 0.15, and 0.15–1000 μg L⁻¹, respectively, and for Cd²⁺ were 0.07, 0.21, and 0.21–750 μg L⁻¹, respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3 mg L⁻¹ of Cu²⁺ and Cd²⁺ ions under the optimal conditions obtained by ANN–MOEA/D ranged from 94.8 to 105%.

In the present study a selective method is presented for the simultaneous determination of copper and cadmium in food samples by adsorptive stripping voltammetry. In preliminary studies, it has been proven that the copper and cadmium react with 3-aminophthalhydrazide (luminol), giving rise to the formation of these complexes. These complexes have adsorptive characteristics on hanging mercury drop electrode (HMDE) and can be reduced in a reduction step. In this study the optimum reaction parameters and conditions studies are investigated. The calibration graphs were linear in the concentration range of 0.5–105.0 and 0.8–70.0ng/ml for copper and cadmium, respectively. The limit of detection of the method was 0.04ng/ml for Cu²⁺ and 0.02ng/ml for Cd²⁺. The interference of some common ions was studied and it was concluded that application of this method for the determination of copper (II) and cadmium in food and water samples led to satisfactory results.

A separation and preconcentration procedure was developed for the determination of trace amounts of Cd(II), Cu(II), Ni(II), and Pb(II) in water and food samples using Amberlite XAD-2 fuctionalized with a new chelating ligand, 3-(2-nitrophenyl)-1H-1,2,4-triazole-5(4H)-thione (Amberlite XAD-2-NPTT). The chelating resin was characterized by Fourier transform infrared spectroscopy (FT-IR) and used as a solid sorbent for enrichment of analytes from samples. The sorbed elements were subsequently eluted with 10 mL of 1.0 M HNO3, and the eluates were analyzed by inductively coupled plasma–atomic emission spectrometry. The influences of the analytical parameters including pH, amount of adsorbent, eluent type and volume, flow rate of the sample solution, volume of the sample solution, and effect of matrix on the preconcentration of metal ions have been studied. The optimum pH for the sorption of four metal ions was about 6.0. The limits of detection were found to be 0.22, 0.18, 0.20, and 0.16 μg L-1 for Cd(II), Cu(II), Ni(II), and Pb(II), respectively, with a preconcentration factor 60. The proposed method was applied successfully for the determination of metal ions in water and food samples.