Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

Su, Ning; Pan, Fangfang; Wang, Liusan; Weng, Shizhuang

doi:10.3390/bios11080261

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…It is observed that the spectra obtained in the NIR region have characteristic bands corresponding to vibrational modes that are common for vegetable oils. The format of the spectra obtained from soybean oil is very similar to works described in the literature that used NIR spectroscopy to determine adulterations and classify oils (Costa et al, 2016;Pereira et al, 2019;Su et al, 2021). However, in the literature there is no description of the NIR spectrum of the baru seed oil, only the spectrum of the cross sections of the wood of its tree .…”

Section: Nir Spectroscopy Analysismentioning

confidence: 82%

Detection and characterization of adulterants in baru and soursop oils

Trindade

Colares

Souza

2023

Food Sci. Technol (Campinas)

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The presence of vegetable oils in the pharmaceutical and cosmetic industries boosts their production and commercialization, maximizing the economic potential, however to increase profits, vegetable oils with high added value are the target of adulteration practices. This negatively affects the quality of the product and compromises the bioactive properties of these oils. The objective of this study was to build partial least squares regression models to identify baru oil and soursop oil samples adulterated with soybean oil. For data collection the oils samples were characterized using portable near-infrared spectrometer and Fourier-transform infrared spectrometer. These techniques were chosen because both provide information about the functional groups of chemical compounds, however portable equipment, being cheaper, presents signals with lower spectral resolution. Thus, the objective of the work is also to verify if the results using both equipment are similar. The developed regression models were effective; however, the near-infrared technique presented some limitations in the identification of soursop oil. This is because for the construction of his model it was necessary to use a smaller number of variables and levels of adulteration. Nevertheless, high R² values and relatively low errors were obtained for all models, making it possible to identify the adulteration. Pratica Application: Adulteration analysis of baru and soursop oils by infrared spectroscopy and portable equipment

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Section: Nir Spectroscopy Analysismentioning

confidence: 82%

Detection and characterization of adulterants in baru and soursop oils

Trindade

Colares

Souza

2023

Food Sci. Technol (Campinas)

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“…The height of the lens from the top surface of oil sample was adjusted to 3 cm. The reflectance spectra of the empty glass beaker and white board were measured before spectral acquisition experiments of the oil samples to calibrate the spectrometer [ 26 ]. Finally, a total of 110 Vis-NIR reflectance spectra were obtained for multivariate analysis.…”

Section: Methodsmentioning

confidence: 99%

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

Weng

Wang

et al. 2021

Biosensors

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The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

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“…According to the Supplementary Materials (Figures S3 and S4), all the VOs along with other types of oils had negative profiles. ANN analysis is widely used to classify and predict VOs by FA composition [74,75]. The obtained FA data can be used for optimizing the pretreatment parameters of the analysis to achieve a higher value-added biodiesel [76].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Comparative Analysis and Determination of the Fatty Acid Composition of Kazakhstan’s Commercial Vegetable Oils by GC-FID

Toishimanov,

Nurgaliyeva,

Serikbayeva

et al. 2023

Applied Sciences

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Here, we present the results of analyzing the fatty acid composition of the main edible vegetable oils from Kazakhstani oilseed producers (safflower, sunflower, maize (corn), linseed, cottonseed, soybean and rapeseed) in comparison with the known fatty acid (FA) composition of specific vegetable oils complying with the Codex Standard for Named Vegetable Oil (Codex Stan 210-1999). The fatty acid composition of 35 different vegetable oils was analyzed by gas chromatography with a Shimadzu GC-2010 Plus instrument with flame ionization detection using a high-polarity CP-Sil 2560, which allowed us to establish their authenticity for high accuracy and excellent separation. A comparative study of the fatty acid composition, groups and omega-6/omega-3 ratios in seven different vegetable oils was carried out. Subsequently, the data were processed by hierarchical clustering analysis, principal component analysis, artificial neural network and Pearson’s correlation. Artificial neural network analysis demonstrated correct predictions. Principal component analysis showed the effects of oleic, linoleic and α-linolenic acids to vegetable oils classification.

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Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

Cited by 7 publications

References 38 publications

Detection and characterization of adulterants in baru and soursop oils

Detection and characterization of adulterants in baru and soursop oils

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

Comparative Analysis and Determination of the Fatty Acid Composition of Kazakhstan’s Commercial Vegetable Oils by GC-FID

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