Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

Nagpal, Tanya; Yadav, Vikas; Khare, Sunil Kumar; Siddhanta, Soumik; Sahu, Jatindra K.

doi:10.1016/j.foodchem.2023.136746

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Fourier Transform Infrared spectroscopy was used to monitor the thermal stability of pure sesame oil, and a linear correlation was obtained between the FTIR signals at different conditions and the proportion of pure sesame oil, with the root mean square of prediction (RMSEP) between 0.8802 and 2.3827 and R 2 between 0.9841 and 0.8834, respectively [18]. Moreover, surface-enhanced Raman spectroscopy, combined with an Artificial Neural Network, was used for the determination of peroxide value and fatty acid composition, with an accuracy of 99% [19]. In addition, fluorescence spectroscopy [20][21][22][23], reflectance spectroscopy [24][25][26], and terahertz spectroscopy [27][28][29][30] have also been used in edible oil measurement.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

Sun,

Hu,

Liu

et al. 2024

Foods

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Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

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Section: Introductionmentioning

confidence: 99%

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

Sun,

Hu,

Liu

et al. 2024

Foods

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“…1 It is believed that oil's oxidative rancidity significantly reduces the quality and safety of edible oils and related foods and shortens the shelf life. 1,2 Therefore, it is necessary to evaluate the degree of oxidative rancidity in edible oils. Recently, acid value (AV), as the official and authoritative quality index of oxidative rancidity, represents the content of free fatty acids in edible oils, which is strongly related to the freshness evaluation, stability prediction, and quality control of edible oils.…”

Section: Introductionmentioning

confidence: 99%

“…However, during long-term storage and improper processing, edible oils are easily oxidized rancid under exposure to oxygen, water, light, heat, etc . It is believed that oil’s oxidative rancidity significantly reduces the quality and safety of edible oils and related foods and shortens the shelf life. , Therefore, it is necessary to evaluate the degree of oxidative rancidity in edible oils. Recently, acid value (AV), as the official and authoritative quality index of oxidative rancidity, represents the content of free fatty acids in edible oils, which is strongly related to the freshness evaluation, stability prediction, and quality control of edible oils. , Long-term intakes of edible oils with exceeded AV may lead to gastrointestinal discomfort, diarrhea, and even growth and development disorders.…”

Section: Introductionmentioning

confidence: 99%

CsPbBr₃ NCs Confined and In Situ Grown in ZIF-8: A Stable, Sensitive, Reliable Fluorescent Sensor for Evaluating the Acid Value of Edible Oils

Lu,

Xiong,

Lin

et al. 2024

ACS Appl. Mater. Interfaces

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Rapidly and sensitively evaluating the acid value (AV) of edible oils is significant to ensuring food quality and safety. Cesium lead bromide perovskite nanocrystals (CsPbBr 3 NCs) are an effective candidate for AV detection; however, their instability restricts wide applications. Herein, CsPbBr 3 @ZIF-8 was prepared by confining and growing CsPbBr 3 NCs in situ into zeolitic imidazolate framework-8 (ZIF-8) to improve the stability, and a fluorescence sensor was established to evaluate the AV of edible oils. The results present that CsPbBr 3 NCs (below 5 nm) with excellent optical properties were confined and grown in situ in micropores and mesopores of ZIF-8. Meanwhile, CsPbBr 3 @ZIF-8 had better long-term storage, ultraviolet-irradiation, and water-exposure stabilities, compared with CsPbBr 3 NCs. Given the fact that free fatty acids (the major contributor of AV) decrease the fluorescence of CsPbBr 3 NCs, the fluorescence intensities of CsPbBr 3 @ZIF-8 were negative-linearly related to oil AV (R 2 = 0.9902) in 0.04−6.00 mg of KOH/g with a 0.06 mg of KOH/g limit of detection. Besides, the practical AV recovery was 92−101% with an average relative standard deviation of 2%. Furthermore, the detection time was 20 min. The response mechanism revealed that free fatty acids could remove surface ligands and increase surface defects to prompt the aggregation of CsPbBr 3 NCs and the formation of lattice fringe dislocations, inducing a decrease in the fluorescence. Thus, a stable, sensitive, reliable sensor was established to evaluate the AV of edible oils.

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Identification of Adulterated Cooking Oil by Raman Spectroscopy

Shehnaz,

Ashraf,

Majeed

et al. 2024

Food Anal. Methods

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Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

Cited by 5 publications

References 33 publications

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

CsPbBr₃ NCs Confined and In Situ Grown in ZIF-8: A Stable, Sensitive, Reliable Fluorescent Sensor for Evaluating the Acid Value of Edible Oils

Identification of Adulterated Cooking Oil by Raman Spectroscopy

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Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

Cited by 5 publications

References 33 publications

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms

CsPbBr3 NCs Confined and In Situ Grown in ZIF-8: A Stable, Sensitive, Reliable Fluorescent Sensor for Evaluating the Acid Value of Edible Oils

Identification of Adulterated Cooking Oil by Raman Spectroscopy

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Product

Resources

About

CsPbBr₃ NCs Confined and In Situ Grown in ZIF-8: A Stable, Sensitive, Reliable Fluorescent Sensor for Evaluating the Acid Value of Edible Oils