Detection of Sesame Oil Adulteration Using Low-Field Nuclear Magnetic Resonance and Chemometrics

Wang, Ruiying; Liu, Kangjing; Wang, Xiaoling; Tan, Mingqian

doi:10.1515/ijfe-2018-0349

Cited by 16 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Ancora et al [ 21 ] demonstrated that LF-NMR relaxation measurements could be applied to determine the occurrence of adulteration in EVOO samples when mixed with four different edible vegetable oils. Moreover, Wang et al [ 22 ] applied this methodology to detect the adulteration of sesame oil samples with soybean oil, and the adulteration ratios were estimated using principal component analysis (PCA) and partial least squares (PLS). Similarly, Zhu et al [ 23 ] applied discriminant analysis (DA) for estimating the adulteration ratios of peanut oil adulterated with soybean oil, rapeseed oil, or palm oil based on LF-NMR relaxometry measurements.…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Huang

Xin

Sun

et al. 2021

Foods

View full text Add to dashboard Cite

Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.

show abstract

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Huang

Xin

Sun

et al. 2021

Foods

View full text Add to dashboard Cite

show abstract

“…a reduced test set and the rest as a reduced training set. The "reduced classification model" to evaluated the ideal number of PCs (Wang et al, 2019;. Among 65 samples, 25 samples were chosen as reduced test set and 40 samples were chosen as reduced training set.…”

Section: Pca and Fdamentioning

confidence: 99%

“…Meanwhile, combination of more than one chemometric tools has already been employed to detect the adulteration in blends. Wang et al (2019) used PCA partial least squares (PLS) and principal component regression (PCR) methods to detecting sesame oil adulteration based on low-field nuclear magnetic resonance data.…”

Section: Introductionmentioning

confidence: 99%

Combining GC-MS and chemometrics to assess the quality of camellia seed oils

Peng

Huang²,

Peng³

et al. 2021

CyTA - Journal of Food

View full text Add to dashboard Cite

Chemometric tools and GC-MS were employed to detect the adulteration of camellia seed oil. In the training set, 65 samples were formulated by blending pure camellia seed oil with varying concentrations of soybean, peanut and rapeseed oil and their fatty acid contents were used in chemometric analysis. Principal Component analysis revealed the distribution patterns of samples that showed a clear stratiform agglomeration according to varying amount of camellia seed oil. Fisher Discriminant Analysis (FDA) was combined to establish the classification models and the correct classification rate of cross-validation model and original model reached 92.5% and 82.5%, respectively. To test the quality of the model, a new test set was prepared and the results showed a high accuracy of 88%. This was indicated that the developed discrimination model established by PCA plus FDA was efficient in predicting the presence of other low-price edible oils in camellia seed oils. Combinación de GC-MS y quimiometría para evaluar la calidad de los aceites de semillas de camelia RESUMENEl presente estudio empleó herramientas quimiométricas y GC-MS para detectar la adulteración del aceite de semilla de camelia. Con este objetivo, en el conjunto de entrenamiento se formularon 65 muestras, en las que se mezcló aceite de semilla de camelia puro con concentraciones variables de aceite de soya [soja], cacahuate [maní] y colza, utilizándose sus contenidos de ácidos grasos para realizar el análisis quimiométrico. El análisis de componentes principales permitió identificar los patrones de distribución de las muestras. Estas mostraron una clara aglomeración estratiforme de acuerdo con la cantidad variable de aceite de semilla de camelia que poseían. Asimismo, para establecer los modelos de clasificación, se realizó el análisis discriminante de Fisher (FDA), mediante el cual se constató que la tasa de clasificación correcta del modelo de validación cruzada y del modelo original alcanzó 92.5% y 82.5%, respectivamente. A fin de comprobar la calidad del modelo se preparó un nuevo conjunto de pruebas, constatándose una alta precisión en los resultados, de 88%. El conjunto de resultados hizo posible comprobar que el modelo de discriminación desarrollado, establecido por PCA más FDA, es eficiente para determinar la presencia de aceites comestibles de bajo precio en los aceites de semilla de camelia.

show abstract

“…Sesame oil is a seed oil extracted from Sesamum indicum L. Because it provides many health benefits and contains antioxidants, polyunsaturated fatty acids, tocopherols, sesamin and sesamol, which are cardioprotective functional components, it is becoming more popular and demanded worldwide (1,2). Sesame oil consists of up to 48% linoleic acid, 43% oleic acid, 12% palmitic acid, and 7% stearic acid (3).…”

Section: Introductionmentioning

confidence: 99%

Non-destructive Detection of Sesame Oil Adulteration by Portable FT-NIR, FT-MIR, and Raman Spectrometers Combined with Chemometrics

Menevşeoğlu

2021

Journal of the Turkish Chemical Society Section A: Chemistry

View full text Add to dashboard Cite

Edible oils are often adulterated with fixed oils because of their high quality and price. Sesame oil is prone to adulteration due to its high commodity value and popularity. Therefore, a rapid, simple, and non-invasive method to detect adulteration in sesame oil is necessary for quality control purposes. Handheld and portable FT-NIR, FT-MIR, and Raman spectrometers are easy to operate, non-destructive, rapid, and easy to transport for in-situ assessments as well as being cheaper alternatives to traditional instruments. This study aimed to evaluate three different vibrational spectroscopic techniques in detecting sesame oil adulteration with sunflower and canola oil. Sesame oils were adulterated with fixed oils at different concentrations (0 -25%) (w/w). Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to authenticate pure sesame oil and Partial Least Squares Regression (PLSR) to predict the levels of the adulterant. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). In all three instruments, SIMCA provided distinct clusters for pure sesame oils and adulterated samples with interclass distance (ICD) over 3. Furthermore, FT-NIR and FT-MIR showed excellent performance in predicting adulterant levels with rval>0.96. Specifically, the FT-MIR unit provided more precise classification and PLSR prediction models over FT-NIR and Raman units. Still, all the units can be used as an alternative method to traditional methods such as GC, GC-MS, etc. These units showed great potential for in-situ surveillance to detect sesame oil adulterations.

show abstract

Detection of Sesame Oil Adulteration Using Low-Field Nuclear Magnetic Resonance and Chemometrics

Cited by 16 publications

References 29 publications

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Combining GC-MS and chemometrics to assess the quality of camellia seed oils

Non-destructive Detection of Sesame Oil Adulteration by Portable FT-NIR, FT-MIR, and Raman Spectrometers Combined with Chemometrics

Contact Info

Product

Resources

About