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Study of vegetable oils and their blends using infrared reflectance spectroscopy and refractometry.

ABSTRACT: The present study aims to perform a comparative analysis of vegetable oils and their two-component blends using infrared spectroscopy and refractometry. The study was conducted in Almaty (Kazakhstan) in 2020. Three samples of 44 vegetable oils and their blends made from two components were examined. Fractometry and infrared spectroscopy were used to investigate the properties of blended vegetable oils. To this end, the fatty acid fraction (in percentage), iodine number, and index of refraction (IOR) were calculated. Afterward, the spectrograms obtained for the blends were analyzed. It was found that the difference between the intensities of weak bands and the band expansion of 722 cm^-1 indicates greater expressiveness. When low-intensity bands (1653 cm^-1) become more distinct due to vibrations of double carbon bonds (C-bonds), the level of unsaturated fatty acids in the blend increases as well.

SUBMITTER: Mukhametov A

PROVIDER: S-EPMC10039264 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Study of vegetable oils and their blends using infrared reflectance spectroscopy and refractometry.

Mukhametov Almas A Mamayeva Laura L Kazhymurat Assemay A Akhlan Togzhan T Yerbulekova Moldir M

Food chemistry: X 20220708

The present study aims to perform a comparative analysis of vegetable oils and their two-component blends using infrared spectroscopy and refractometry. The study was conducted in Almaty (Kazakhstan) in 2020. Three samples of 44 vegetable oils and their blends made from two components were examined. Fractometry and infrared spectroscopy were used to investigate the properties of blended vegetable oils. To this end, the fatty acid fraction (in percentage), iodine number, and index of refraction ( ...[more]

PMID: 36974180

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Project description:Optimal management of free-ranging herbivores requires the accurate assessment of an animal's nutritional status. For this purpose 'near-infrared reflectance spectroscopy' (NIRS) is very useful, especially when nutritional assessment is done through faecal indicators such as faecal nitrogen (FN). In order to perform an NIRS calibration, the default protocol recommends starting by generating an initial equation based on at least 50-75 samples from the given species. Although this protocol optimises prediction accuracy, it limits the use of NIRS with rare or endangered species where sample sizes are often small. To overcome this limitation we tested a single NIRS equation (i.e., multispecies calibration) to predict FN in herbivores. Firstly, we used five herbivore species with highly contrasting digestive physiologies to build monospecies and multispecies calibrations, namely horse, sheep, Pyrenean chamois, red deer and European rabbit. Secondly, the equation accuracy was evaluated by two procedures using: (1) an external validation with samples from the same species, which were not used in the calibration process; and (2) samples from different ungulate species, specifically Alpine ibex, domestic goat, European mouflon, roe deer and cattle. The multispecies equation was highly accurate in terms of the coefficient of determination for calibration R2 = 0.98, standard error of validation SECV = 0.10, standard error of external validation SEP = 0.12, ratio of performance to deviation RPD = 5.3, and range error of prediction RER = 28.4. The accuracy of the multispecies equation to predict other herbivore species was also satisfactory (R2 > 0.86, SEP < 0.27, RPD > 2.6, and RER > 8.1). Lastly, the agreement between multi- and monospecies calibrations was also confirmed by the Bland-Altman method. In conclusion, our single multispecies equation can be used as a reliable, cost-effective, easy and powerful analytical method to assess FN in a wide range of herbivore species.

Project description:BackgroundGlucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method.ResultsHere we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R2 = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g-1 fresh weight and R2 = 0.80 with RPD = 2.4 for predicting µmol GBS·g-1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines.ConclusionsA calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

Dataset Information

Study of vegetable oils and their blends using infrared reflectance spectroscopy and refractometry.

Publications

Study of vegetable oils and their blends using infrared reflectance spectroscopy and refractometry.

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