Characterization of Edible Oils Using NIR Spectroscopy and Chemometric Methods

Ranjan, Rishi; Kumar, Navjot; Kiranmayee, A H; Panchariya, P. C.

doi:10.1007/978-3-030-16660-1_29

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…On the other hand, similar to the present study, Inarejos-Garcia et al found that virgin olive oils of different quality classes differed in the 1700 nm region, which they related to stretching vibrations of methyl, methylene and ethylene groups [ 34 ]. Comparison of NIR spectroscopy data of sunflower, maize and olive oils also revealed large differences between samples at about 1650 nm [ 35 ], as well as sesame, safflower, mustard, peanut, olive, canola and soybean oils [ 36 ]. Due to its size, the NIR instrument that was used for the present study could be considered portable.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data

Balbino

Vincek

Trtanj³

et al. 2022

Foods

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Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904–922 nm and 1675–1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

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Section: Resultsmentioning

confidence: 99%

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data

Balbino

Vincek

Trtanj³

et al. 2022

Foods

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“…To validate the created model, the remaining 30% of the data were used as a testing data set. The minimum value of root mean square error of cross validation (RMSECV) with venetian blinds technique was used for selection of the optimum number of latent variables (LV) 33 . In our study, model performance has been statistically validated by the sensitivity (ratio of true positives to actual positives), specificity (data ratio of true negatives to the sum of all negatives), precision (ratio of true positives to all positives), accuracy (ratio of truly identified data to all data), class error and non-assigned rate for the calibration, cross-validation and testing.…”

Section: Methodsmentioning

confidence: 99%

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis–NIR–SWIR hyperspectral imaging

Hamad,

Chakraborty

2024

Sci Rep

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The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis–NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis–NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis–NIR spectral data.

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“…The effective wavelength selection was of important for establishing simplified and stabilized prediction models. In this paper, the successive projections algorithm (SPA) [27], variable importance of projection (VIP) [28] and principal component analysis (PCA) [29] were used for spectra feature extraction and wavelength selection.…”

Section: Selection Of Effective Wavelengths Of Vis-nir Reflectance Spectramentioning

confidence: 99%

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

Pan

Wang

et al. 2021

Biosensors

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The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.

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Characterization of Edible Oils Using NIR Spectroscopy and Chemometric Methods

Cited by 7 publications

References 19 publications

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis–NIR–SWIR hyperspectral imaging

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

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