Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics

Wang, Tong; Long, Wanjun; Hu, Yong; Li, Cheng; Chen, An‐Qi; Yu, Ru‐Qin

doi:10.1016/j.foodchem.2019.04.109

Cited by 90 publications

(20 citation statements)

References 32 publications

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“…The details of functional groups responsible for FTIR absorption bands are described in Table 1. In our study, the spectra of these oil samples showed many similarities among absorbance bands, which is consistent with previous reports [10,27,30,[36][37][38]. All plant oils are made up of triacylglycerol (92%), low concentrations of di-and mono-acylglycerols (5%), and low levels of other components [23].…”

Section: Ftir Spectra Of Edible Oilssupporting

confidence: 92%

“…Due to its valuable composition and related properties, CAO is gaining popularity as a food, and in pharmaceuticals and cosmetics. These oils are relatively expensive because of their nutritional values, and thus it is important to protect them from adulteration with low price/quality edible oils such as RSO, SBO, SFO, CO, and peanut oil (PO) [8][9][10][11]. Moreover, rapeseed and derivatives such as RSO are recognized as a new potential source of allergies [12].…”

Section: Introductionmentioning

confidence: 99%

“…All of these reports used FTIR spectroscopy to monitor the adulteration of edible oils. Although there have been several studies on near-infrared (NIR), mid-infrared (MIR), Raman, and fluorescence spectroscopy investigations into camellia oil adulteration [10,11,29], studies on the authentication of camellia oil using FTIR spectroscopy remain limited.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Classification and Quantification of Camellia (Camellia oleifera Abel.) Oil Blended with Rapeseed Oil Using FTIR-ATR Spectroscopy

et al. 2020

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Currently, the authentication of camellia oil (CAO) has become very important due to the possible adulteration of CAO with cheaper vegetable oils such as rapeseed oil (RSO). Therefore, we report a Fourier transform infrared (FTIR) spectroscopic method for detecting the authenticity of CAO and quantifying the blended levels of RSO. In this study, two characteristic spectral bands (1119 cm−1 and 1096 cm−1) were selected and used for monitoring the purity of CAO. In combination with principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression (PLSR) analysis, qualitative and quantitative methods for the detection of camellia oil adulteration were proposed. The results showed that the calculated I1119/I1096 intensity ratio facilitated an initial check for pure CAO and six other edible oils. PCA was used on the optimized spectral region of 1800–650 cm−1. We observed the classification of CAO and RSO as well as discrimination of CAO with RSO adulterants. LDA was utilized to classify CAO from RSO. We could differentiate and classify RSO adulterants up to 1% v/v. In the quantitative PLSR models, the plots of actual values versus predicted values exhibited high linearity. Root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) values of the PLSR models were 1.4518–3.3164% v/v and 1.7196–3.8136% v/v, respectively. This method was successfully applied in the classification and quantification of CAO adulteration with RSO.

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Section: Ftir Spectra Of Edible Oilssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rapid Classification and Quantification of Camellia (Camellia oleifera Abel.) Oil Blended with Rapeseed Oil Using FTIR-ATR Spectroscopy

et al. 2020

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“…Recent advances in physical technologies have also enabled rapid analytical systems that do not require sample preprocessing and therefore induce less chemical waste. For example, Fourier transform near infrared (FT-NIR) had been used with PLS1 in the detection of extra virgin olive oil adulteration 28 , Low-field nuclear magnetic resonance (LF-NMR) was used with discriminant analysis (DA) for the binary classification of adulterated peanut oil 29 , proton nuclear magnetic resonance was used with PLS1 for the detection of Camellia oil 30 and excitation–emission matrix fluorescence spectroscopy had been used with N-way PLS for the detection of Camellia oil 31 . However, these studies also used traditional chemometric techniques and did not study the generalizability of their models across a large diverse set of samples.…”

Section: Discussionmentioning

confidence: 99%

Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures

Lim

Pan²,

Yu³

et al. 2020

Nat Commun

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Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid profile of a specific oil type can be mimicked by a mixture of 2 or more oil types. This has led to fraudulent oil adulteration and intentional mislabeling of edible oils threatening food safety and endangering public health. Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures. Trained on a large number of simulated oil mixtures, independent test dataset validation demonstrates that the model has a 50th percentile absolute error between 1.4–1.8% and a 90th percentile error of 4–5.4% for any 3-way mixtures of the ten oil types. The deep learning model can also be further refined with on-line training. Because oil-producing plants have diverse geographical origins and hence slightly varying fatty acid profiles, an online-training method provides also a way to capture useful knowledge presently unavailable. Our method allows the ability to control product quality, determining the fair price of purchased oils and in-turn allowing health-conscious consumers the future of accurate labeling.

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“…Partial Least Square Discriminant Analysis is a linear classification method that is based on the PLS regression algorithm [8,16]. This method is a combination of properties of partial least squares regression and the discrimination power of classification technique.…”

Section: Pls Discriminant Analysis (Pls-da)mentioning

confidence: 99%

Comparison Analysis: Large Data Classification Using PLS-DA and Decision Trees

Rashid¹,

Nasaruddin²,

Kassim³

et al. 2020

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Classification studies are widely applied in many areas of research. In our study, we are using classification analysis to explore approaches for tackling the classification problem for a large number of measures using partial least square discriminant analysis (PLS-DA) and decision trees (DT). The performance for both methods was compared using a sample data of breast tissues from the University of Wisconsin Hospital. A partial least square discriminant analysis (PLS-DA) and decision trees (DT) predict the diagnosis of breast tissues (M = malignant, B = benign). A total of 699 patients diagnose (458 benign and 241 malignant) are used in this study. The performance of PLS-DA and DT has been evaluated based on the misclassification error and accuracy rate. The results show PLS-DA can be considered as a good and reliable technique to be used when dealing with a large dataset for the classification task and have good prediction accuracy.

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Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics

Cited by 90 publications

References 32 publications

Rapid Classification and Quantification of Camellia (Camellia oleifera Abel.) Oil Blended with Rapeseed Oil Using FTIR-ATR Spectroscopy

Rapid Classification and Quantification of Camellia (Camellia oleifera Abel.) Oil Blended with Rapeseed Oil Using FTIR-ATR Spectroscopy

Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures

Comparison Analysis: Large Data Classification Using PLS-DA and Decision Trees

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