A Real-time Metabolomic Profiling Approach to Identify Virgin Olive Oil Adulteration by Rapid Evaporative Ionization Mass Spectrometry

Wen, Wen; Xin, Zhao; Wang, Huijun; Cao, Xueli

doi:10.1007/s12161-023-02466-9

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Some parameters of the PCA-LDA model were set as follows: PCA components: 25, linear discriminants: 4, outlier setting: by standard deviation, and mass range: 50-1000 m/z. After meticulous model construction, its accuracy was critically evaluated using a robust and trusted leave-20%-out cross-validation approach, resulting in an impressive 93.8% correct classification rate, as demonstrated in Table 2, reinforcing the accuracy and reliability of the developed chemometric model [42]. Compared to other common methods of discriminating between various types of oil samples, the REIMS method possesses distinct advantages as it does not require any prior sample preparation, providing an efficient solution for the investigation of oil samples.…”

Section: Model Building and Validationmentioning

confidence: 99%

Real-Time Authentication of Camellia Oil by Rapid Evaporative Ionization Mass Spectrometry

Xiang,

Liu,

Jing

et al. 2024

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Camellia oil is a high-value product with rich nutrients. Recently, the adulteration of camellia oil has become an increasingly concerning issue related to human health. In this study, electric soldering iron coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the identification and analysis of camellia oil without any sample preparation. REIMS technology coupled with chemometrics was applied to develop an analysis model for the authentication of camellia oil adulterated with soybean oil, peanut oil, rapeseed oil, sunflower oil, and corn oil (5–40%, v/v). The results showed that different types of vegetable oils could be classified using principal component analysis-linear discriminant analysis (PCA-LDA) with a correct classification of 93.8% in leave-20%-out cross-validation and 100% correctly identified in real-time recognition. The established prediction models were found to be particularly sensitive when the camellia oil samples were adulterated with 5–40% of other oils, indicating that REIMS could be a powerful tool for the authentication and adulteration analysis of camellia oil, particularly for cases where the adulteration levels are relatively high. In conclusion, the results provide valuable insights into the potential of REIMS for the rapid, accurate, and real-time authentication and adulteration analysis of camellia oil.

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Section: Model Building and Validationmentioning

confidence: 99%