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Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils
被引:21
|作者:
Jimenez-Sotelo, Paola
[1
]
Hernandez-Martinez, Maylet
[1
]
Osorio-Revilla, Guillermo
[2
]
Gabriela Meza-Marquez, Ofelia
[2
]
Garcia-Ochoa, Felipe
[1
]
Gallardo-Velazquez, Tzayhri
[1
]
机构:
[1] Inst Politecn Nacl, Dept Biofis, Escuela Nacl Ciencias Biol, Mexico City, DF, Mexico
[2] Inst Politecn Nacl, Dept Ingn Bioquim, Escuela Nacl Ciencias Biol, Mexico City, DF, Mexico
来源:
关键词:
Avocado oil;
ternary mixture;
adulteration;
mid-FTIR;
SIMCA;
chemometrics;
VIRGIN COCONUT OIL;
OLIVE OIL;
VEGETABLE-OILS;
EDIBLE OILS;
ADULTERATION;
QUANTIFICATION;
STABILITY;
TOOLS;
D O I:
10.1080/19440049.2016.1203073
中图分类号:
O69 [应用化学];
学科分类号:
081704 ;
摘要:
Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R-2 > 0.9961, standard errors of calibration (SEC) in the range of 0.3963-0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.
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页码:1105 / 1115
页数:11
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