2019
DOI: 10.1364/osac.2.001148
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Identification and quantification of vegetable oil adulteration with waste frying oil by laser-induced fluorescence spectroscopy

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Cited by 15 publications
(7 citation statements)
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“…The modified oil with VA exhibited moderate results (2.5-fold decrease), while the modified oil with HTYR presented the most evident positive effect. These observations agree with those of Hao et al concerning the effect of heat on the oil pigments and the importance of using antioxidants to preserve the quality of the products [39].…”
Section: Fluorescence Spectroscopysupporting
confidence: 93%
See 1 more Smart Citation
“…The modified oil with VA exhibited moderate results (2.5-fold decrease), while the modified oil with HTYR presented the most evident positive effect. These observations agree with those of Hao et al concerning the effect of heat on the oil pigments and the importance of using antioxidants to preserve the quality of the products [39].…”
Section: Fluorescence Spectroscopysupporting
confidence: 93%
“…The decrease in the fluorescence peak at 670 nm after excitation of oil samples at 400 nm is ascribed to oil pigments, specifically chlorophylls, which undergo deterioration under thermal treatment. Therefore, the fluorescence emission peak at 670 nm is a good indicator of the degradation of oils [39,40]. As shown in Figure 6, the fluorescence peak of the control oil decreased 18-fold after 28 days at 60 °C, whereas the fluorescence peaks of the modified oils with HVA and HTYR were remarkably preserved.…”
Section: Fluorescence Spectroscopymentioning
confidence: 94%
“…Edible oils contain various fluorophores, including oxidation products, polyphenols, vitamin E, and chlorophyll (Botosoa & Karoui, 2022; Xu et al., 2016). Therefore, LIF and LED/IF techniques with specific excitation wavelengths were extensively used for classification (Al Riza et al., 2019; Chen et al., 2022; Kongbonga et al., 2019; Mu et al., 2013a, 2013b; Zhu et al., 2015), adulteration detection(Chen et al., 2018; Hao et al., 2019; Mu et al., 2016; ;2021 Torreblanca‐Zanca et al., 2019a), aflatoxin B 1 (AFB 1 ) contamination detection (Chen et al., 2021; He et al., 2022), and comprehensive analysis including discrimination between EVOO and other vegetable oils as well as identification of storage and thermal effect on emission of oils (Hossain et al., 2020), as shown in Figure 2.…”
Section: Analysis Related To Lif and Led‐if Techniques At Laboratory ...mentioning
confidence: 99%
“…Laser sources in the 400−500 nm range are typically considered. Some studies have collected fluorescence using right‐angle geometry (Hao et al., 2019; Torreblanca‐Zanca et al., 2019), while others have used a 45° rotation of the cuvette to reduce backscattered (Chen et al., 2018; Mu et al., 2016). Qualitative adulteration evaluation has been carried out using SVM and artificial neural networks (ANN), and some studies have conducted quantitative analyses of the adulteration level using PLSR.…”
Section: Analysis Related To Lif and Led‐if Techniques At Laboratory ...mentioning
confidence: 99%
“…Several approaches to investigating and detecting adulterations in food products have been proposed by various food scientists. Researchers have shown the successful application of combining analytical experimental results with other linear and non-linear chemometric tools [10] to build classification and regression models for oil samples i.e., linear discriminant analysis (LDA) [11], multiple linear regression (MLR) [12], multivariate adaptive regression splines (MARS) [13], support vector machine (SVM) [14], artificial neural networks (ANNs) [15], principle component analysis (PCA) [16], orthogonal partial least squares discriminant analysis (OPLS-DA) [17] and partial least squares regression (PLS) [18]. For example, to detect adulteration in extra virgin olive oils, UV-IMS (ultraviolet ion mobility spectrometry) combined with chemometric analysis like PCA and LDA [19], near-infrared spectroscopy with chemometric techniques [20], and DSC combined with SVM [21].…”
Section: Introductionmentioning
confidence: 99%