Identification of Sudanese Sesame Oil Quality Using a 680 nm Semiconductor Laser Spectrometer and Other Techniques

This study investigates the optical properties of sesame oil from traditional and industrial sources using a custom-designed semiconductor laser spectrometer, UV-Vis spectroscopy, and FTIR spectroscopy. Six samples were collected from traditional presses and factories in Khartoum State and White Nile State. The spectrometer, constructed with a 680 nm semiconductor laser and various resistor values, measured the absorbance of sesame oil samples. UV-Vis spectroscopy identified absorbance peaks at 670 nm and 417 nm, corresponding to chlorophyll a and b. FTIR analysis showed nearly identical spectra among the samples, indicating similar chemical compositions. Laser spectrometer analysis revealed specific absorbance values for each sample. The results highlight the feasibility of using a 680 nm semiconductor laser for analyzing sesame oil, providing a cost-effective alternative to other wavelengths. This study demonstrates the potential of integrating traditional methods with modern spectroscopic techniques for the quality assessment of sesame oil.

Discrimination of pressed sesame oil:A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods

This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints.The goal of this study was to provide a reliable tool for assessing the quality of sesame oil.The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200-330 nm.Various classification and prediction methods,including linear discrimination analysis(LDA),support vector machines(SVM),soft independent modeling of class analogy(SIMCA),partial least squares regression(PLSR),support vector machine regression(SVR),and back-propagation neural network(BPNN),were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil.The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil,achieving an accuracy of 96.15%,a sensitivity of 97.87%,and a specificity of 80%.For quantitative analysis,BPNN yielded the best prediction results,with an R^(2) value of 0.99,RMSEP of 2.34%,and RPD value of 10.60(LOD of 8.60%and LOQ of 28.67%).Overall,the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.