Spectral detection technology of vegetable oil: Spectral analysis of porphyrins and terpenoids

Wang, Hongpeng; Xin, Yingjian; Xiong, Wei

doi:10.1016/j.saa.2021.119965

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…In addition, the authors observed transmission rates between 20 and 100% in all the oils they studied. This study, however, observed an absorption peak at 350 nm for all temperatures, which suggests that the oils may contain the natural pigment molecules chlorophyll and carotenoid, belonging to porphyrins and terpenoids [ 69 , 70 ]. Ref.…”

Section: Discussionmentioning

confidence: 99%

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Determination of Maximum Oil Yield, Quality Indicators and Absorbance Spectra of Hulled Sunflower Seeds Oil Extraction under Axial Loading

Kabutey

Herák

Mizera

2022

Foods

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The present study aims to estimate the maximum oil yield of hulled sunflower seed samples in a uniaxial process under a load of 40 kN and speed of 4 mm/min. The oil samples were assessed for their quality parameters and spectra curves within the wavelength range of 325–600 nm. The results show that heating temperatures in the range of 40 °C to 80 °C increased the oil output; however, a maximum oil yield of 48.869 ± 6.023% with a minimum energy of 533.709 ± 65.644 J at the fifth repeated pressing was obtained from the unheated sample compared to the heated samples. The peroxide values ranged from 6.898 ± 0.144 to 7.290 ± 0.507 meq O2/kg, acid values from 1.043 ± 0.166 to 1.998 ± 0.276 mg KOH/g oil and free fatty acid values from 0.521 ± 0.083 to 0.999 ± 0.138 mg KOH/g oil, which were within the recommended quality threshold. There were significant spectral differences among the oil samples. A single absorbance peak was observed at 350 nm for all oil samples, indicating low levels of pigment molecules in the oil. The study revealed the need for repeated pressings to recover the considerable residual oil remaining in the seedcake after the first pressing.

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Section: Discussionmentioning

confidence: 99%

“…Ref. [ 70 ] further indicated that β carotene and chlorophyll were the major factors that caused the difference in absorption spectra. The absorption and transmission rates were between 0.28 and 1.305 (-) and 5 and 52.53%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Determination of Maximum Oil Yield, Quality Indicators and Absorbance Spectra of Hulled Sunflower Seeds Oil Extraction under Axial Loading

Kabutey

Herák

Mizera

2022

Foods

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“…In this paper, radial basis function was used as the kernel function of SVM. The classification hyperplane established by SVM can guarantee the classification accuracy ( Wang et al, 2021a ). For the optimization problem of the parameters of the SVM (parameter c and g ), this paper used the particles swarm optimization (PSO) algorithm ( Huang et al, 2021 ) and the grey wolf optimizer (GWO) algorithm ( Deng et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

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“…It has the characteristics of high efficiency, fast iterative convergence, and stability [19]. BEADS has been successfully applied to baseline correction of Raman spectra and spectral data denoising [20][21][22][23]. The cut-off frequency Fc, filtering order parameter D, and asymmetry parameter R of the BEADS algorithm used in this paper were 0.05, 1.00, and 6.00, respectively.…”

Section: Spectral Data Pre-processingmentioning

confidence: 99%

Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion

Wang

Yan

Xin

et al. 2023

Sensors

Self Cite

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This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure.

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Spectral detection technology of vegetable oil: Spectral analysis of porphyrins and terpenoids

Cited by 12 publications

References 27 publications

Determination of Maximum Oil Yield, Quality Indicators and Absorbance Spectra of Hulled Sunflower Seeds Oil Extraction under Axial Loading

Determination of Maximum Oil Yield, Quality Indicators and Absorbance Spectra of Hulled Sunflower Seeds Oil Extraction under Axial Loading

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion

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