One-class classification based authentication of peanut oils by fatty acid profiles

Zhang, Liangxiao; Li, Peiwu; Sun, Xiaorui; Mao, Jin; Ma, Fang; Ding, Xin; Zhang, Qi

doi:10.1039/c5ra07329d

Cited by 30 publications

(16 citation statements)

References 29 publications

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“…Traditional supervised chemometric methods lack the feature of generalizability. Previous studies have shown that fatty acid profiles could be used to identify oil types via unsupervised clustering and supervised classification 9 , 10 , 12 but these qualitative models were not directly generalizable to quantitative predictions. A recent work showed that a quantitative model for detecting two-way sesame oil mixtures showed promising results but exhibited higher errors when generalized to four-way mixtures 11 .…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…A number of chemometric methods to detect adulterated oil using fatty acid profiles has been described 9 – 12 . Usually these methods deal with a simple mixture of 2–3 oil types and are often qualitative in nature (e.g., PCA).…”

Section: Introductionmentioning

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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“…By including the information of additional classes (i.e., VOO/OO blends and EVOO with other vegetable oils), the sensitivity and specificity of the SIMCA models were 100% for all the oil classes (Table 3). Since authentication studies are often approached as a one-class classification analysis, the adulterants are usually unknown [57]. A one-class SIMCA model was developed for EVOO based on the infrared spectra of genuine samples, and any adulterated samples were classified as outliers when tested against the PCA model boundaries.…”

Section: Pattern Recognition Modeling Using Ft-ir and Raman Spectroscopymentioning

confidence: 99%

Non-Targeted Authentication Approach for Extra Virgin Olive Oil

Aykas

Karaman

Keser

et al. 2020

Foods

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The aim of this study is to develop a non-targeted approach for the authentication of extra virgin olive oil (EVOO) using vibrational spectroscopy signatures combined with pattern recognition analysis. Olive oil samples (n = 151) were grouped as EVOO, virgin olive oil (VOO)/olive oil (OO), and EVOO adulterated with vegetable oils. Spectral data was collected using a compact benchtop Raman (1064 nm) and a portable ATR-IR (5-reflections) units. Oils were characterized by their fatty acid profile, free fatty acids (FFA), peroxide value (PV), pyropheophytins (PPP), and total polar compounds (TPC) through the official methods. The soft independent model of class analogy analysis using ATR-IR spectra showed excellent sensitivity (100%) and specificity (89%) for detection of EVOO. Both techniques identified EVOO adulteration with vegetable oils, but Raman showed limited resolution detecting VOO/OO tampering. Partial least squares regression models showed excellent correlation (Rval ≥ 0.92) with reference tests and standard errors of prediction that would allow for quality control applications.

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“…Food fraud, motivated by financial benefits, is a common phenomenon around the world, especially for oils, dairy products, fruit juices, honey, wine and seafood [ 1 , 2 ]. Among the food mentioned before, oil adulteration accounts for a large proportion and attracts serious attention from researchers [ 2 , 3 , 4 ]. Edible oil plays an indispensable role in our daily life as the sources of essential fatty acids, carotenoids, and lipid-soluble vitamins like vitamin E and vitamin K [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Multispecies Adulteration Detection of Camellia Oil by Chemical Markers

et al. 2018

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Adulteration of edible oils has attracted attention from more researchers and consumers in recent years. Complex multispecies adulteration is a commonly used strategy to mask the traditional adulteration detection methods. Most of the researchers were only concerned about single targeted adulterants, however, it was difficult to identify complex multispecies adulteration or untargeted adulterants. To detect adulteration of edible oil, identification of characteristic markers of adulterants was proposed to be an effective method, which could provide a solution for multispecies adulteration detection. In this study, a simple method of multispecies adulteration detection for camellia oil (adulterated with soybean oil, peanut oil, rapeseed oil) was developed by quantifying chemical markers including four isoflavones, trans-resveratrol and sinapic acid, which used liquid chromatography tandem mass spectrometry (LC-MS/MS) combined with solid phase extraction (SPE). In commercial camellia oil, only two of them were detected of daidzin with the average content of 0.06 ng/g while other markers were absent. The developed method was highly sensitive as the limits of detection (LODs) ranged from 0.02 ng/mL to 0.16 ng/mL and the mean recoveries ranged from 79.7% to 113.5%, indicating that this method was reliable to detect potential characteristic markers in edible oils. Six target compounds for pure camellia oils, soybean oils, peanut oils and rapeseed oils had been analyzed to get the results. The validation results indicated that this simple and rapid method was successfully employed to determine multispecies adulteration of camellia oil adulterated with soybean, peanut and rapeseed oils.

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One-class classification based authentication of peanut oils by fatty acid profiles

Cited by 30 publications

References 29 publications

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

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

Non-Targeted Authentication Approach for Extra Virgin Olive Oil

Multispecies Adulteration Detection of Camellia Oil by Chemical Markers

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