کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5768674 1628513 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک دانش تغذیه
پیش نمایش صفحه اول مقاله
Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy
چکیده انگلیسی


- Use of the methyl-transesterified fraction to authenticate olive oil.
- Quantification models are built using vibrational spectroscopic techniques.
- Quality metrics provide a high percentage of success (equal or near to 100%).
- One-input, two-input and pseudo two-input class classification strategies were tested.

Samples of olive oils (n = 67) from different qualities and samples of other vegetable edible oils (including soybean, sunflower, rapeseed, corn oil etc; n = 79) were used in this study as pure oils. Previous to spectroscopy analysis, a transesterification step was applied to the pure vegetable oil samples and all the different oil blends were then prepared to create in-house blended samples. Spectral acquisition was performed with typical parameters to collect the FTIR and Raman fingerprints. For the olive/non-olive classification model, three classification strategies have been applied: (i) one input-class (1iC) classification; (ii) two input-class (2iC) classification; and (iii) one input-class plus one 'dummy' class classification (or pseudo two input-class (p2iC) classification). The multivariate classification methods used were k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), one-class partial least squares (OCPLS), support vector machine classification (SVM-C), and soft independent modelling of class analogies (SIMCA). The multivariate quantification method used was partial least square-regression (PLS-R). FTIR fingerprints showed excellent classification ability to distinguish pure olive from non-olive oil. When PLS-DA or SVM-C techniques are applied, 100% of olive oil samples and 92% of other vegetable edible oils are correctly classified. In general FTIR fingerprints were more discriminative than Raman's in both classification and regression scenarios.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: LWT - Food Science and Technology - Volume 86, December 2017, Pages 174-184
نویسندگان
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