CHAPTER THREE
3.0 Materials and Methods
3.1 Description of the Study Area
Dry and clean seeds of peanut, sesame, and sunflower were obtained from farmers in four districts—Nebbi District, Arua District, Yumbe District, and Zombo District—located in the West Nile sub-region of Northern Uganda. Sampling was conducted between September and December 2017. Approximately 5 kg of each seed type was packaged in black polythene bags and transported to the Chemistry Laboratory at Kyambogo University for analysis.
3.2 Sample Preparation
Only clean and undamaged seeds were selected for analysis. The seeds were sun-dried for 24 hours to remove moisture. Sunflower seeds were de-hulled, while peanut seeds were de-shelled prior to grinding. The prepared seeds were then ground into a fine, uniform powder using an electric grinder (Brooks Crompton Series 2000, UK). The resulting powders were stored in plastic containers and kept in a cool, dry cupboard at approximately 25°C for two days before oil extraction.
3.3 Oil Extraction Using Soxhlet Apparatus
A 50 g portion of each sample was placed in a thimble and subjected to extraction using n-hexane (boiling point 68°C) in a Soxhlet extractor for 8 hours, following the method adapted from Pena et al. (1992). The solvent was removed using a rotary evaporator, and any remaining solvent was eliminated by oven drying at 60°C for 1 hour and flushing with nitrogen gas (99.9%).
The extracted oil was further heated in a water bath at 70°C for 30 minutes to ensure complete solvent removal. The oil volume was measured and expressed as percentage oil content using the formula:
Oil Content (%)=Weight of oilWeight of sample×100\text{Oil Content (\%)} = \frac{\text{Weight of oil}}{\text{Weight of sample}} \times 100
The oil samples were then stored at −20°C prior to analysis of physicochemical properties, fatty acid composition, and heavy metal content.
3.4 Determination of Physicochemical Properties
3.4.1 Density
Density was determined using the pycnometer method as described by AOCS (2009). The weight of the empty pycnometer, oil-filled pycnometer, and water-filled pycnometer at 27°C were recorded. Density was calculated using:
ρ=Weight of oil-filled pycnometer−Weight of empty pycnometerWeight of water-filled pycnometer−Weight of empty pycnometer\rho = \frac{\text{Weight of oil-filled pycnometer} – \text{Weight of empty pycnometer}}{\text{Weight of water-filled pycnometer} – \text{Weight of empty pycnometer}}
3.4.2 Viscosity
Kinematic viscosity was measured using a viscometer at 27°C. The flow time of the oil sample was recorded and used to calculate viscosity:
ν=ct\nu = ct
Dynamic viscosity was then obtained using:
η=ρν\eta = \rho \nu
where ρ\rho is density.
3.4.3 Peroxide Value (PV)
Peroxide value was determined by titration using sodium thiosulphate, with starch as an indicator. The PV was calculated as:
PV=(S−B)×NWPV = \frac{(S – B) \times N}{W}
where S = sample titre, B = blank titre, N = normality, and W = sample weight.
3.4.4 Saponification Value (SV)
The oil sample was refluxed with alcoholic KOH and titrated with HCl. The saponification value was calculated using:
SV=(B−T)×N×56.1WSV = \frac{(B – T) \times N \times 56.1}{W}
3.4.5 Iodine Value (IV)
Iodine value was determined using Wijs solution and titration with sodium thiosulphate after incubation in the dark. The IV was calculated as:
IV=12.69×C(V1−V2)MIV = \frac{12.69 \times C (V_1 – V_2)}{M}
3.4.6 Acid Value (AV)
The acid value was determined by titrating the oil sample with potassium hydroxide using phenolphthalein indicator:
AV=V×N×56.1WAV = \frac{V \times N \times 56.1}{W}
3.5 Fatty Acid Analysis
Fatty acid composition was determined using Gas Chromatography–Mass Spectrometry (GC–MS) after converting the oils into fatty acid methyl esters (FAMEs).
3.5.1 Preparation of FAME Standards
Standard fatty acids including palmitic, stearic, oleic, linoleic, and linolenic acids were obtained from Sigma-Aldrich (Germany). These were used to prepare calibration standards for identification and quantification.
3.5.2 Preparation of Fatty Acid Methyl Esters (FAMEs)
Oil samples were esterified using methanolic KOH and boron trifluoride. The resulting methyl esters were extracted with n-hexane and prepared for GC–MS analysis.
3.5.3 GC–MS Analysis
FAMEs were analyzed using an Agilent GC–MS system equipped with a capillary column. Helium was used as the carrier gas, and identification of fatty acids was based on retention times and mass spectra comparison with standards.
3.6 Determination of Heavy Metal Content
3.6.1 Sample Preparation
Oil samples were digested using nitric acid and hydrogen peroxide in a microwave digestion system. The digested samples were diluted and analyzed using Flame Atomic Absorption Spectroscopy (FAAS).
3.6.2 Calibration Curves
Standard solutions of lead (Pb), iron (Fe), zinc (Zn), and cadmium (Cd) were prepared, and calibration curves were generated using FAAS.
3.6.3 Method Validation
Validation parameters included detection limits, quantification limits, precision, and accuracy.
- IDL = 3 × standard deviation of blank
- LOD = 3 × standard deviation of blank
- LOQ = 3 × standard deviation of blank
3.6.4 Precision and Accuracy
Precision was expressed as relative standard deviation (RSD), while accuracy was assessed through recovery studies:
%RSD=Standard deviationMean×100\%RSD = \frac{\text{Standard deviation}}{\text{Mean}} \times 100 %Recovery=Spiked − UnspikedActual spike×100\%Recovery = \frac{\text{Spiked − Unspiked}}{\text{Actual spike}} \times 100
3.6.5 Sample Analysis
Metal concentrations were determined using FAAS under specified operating conditions. Final concentrations were calculated as:
Concentration (mg/kg)=C×VW\text{Concentration (mg/kg)} = \frac{C \times V}{W}
3.6.11 Statistical Analysis
All analyses were conducted in triplicate, and results were expressed as mean ± standard deviation. Statistical analysis was performed using Minitab (Version 13.3). One-way ANOVA was used to determine significant differences among samples at a confidence level of P < 0.05.
3.6.12 Limitations of the Study
The study had several limitations. Sampling was based on random selection, which may introduce bias and sampling errors, potentially influencing the results. Additionally, the study focused on a limited number of heavy metals and only three types of oilseed crops, which may restrict the generalization of findings.
