Application of random forests to select premium quality vegetable oils by their fatty acid composition

Automatic ¹H-NMR Screening of Fatty Acid Composition in Edible Oils

In this work, we introduce an NMR-based screening method for the fatty acid composition analysis of edible oils. We describe the evaluation and optimization needed for the automated analysis of vegetable oils by low-field NMR to obtain the fatty acid composition (FAC). To achieve this, two scripts, which automatically analyze and interpret the spectral data, were developed. The objective of this work was to drive forward the automated analysis of the FAC by NMR. Due to the fact that this protocol can be carried out at low field and that the complete process from sample preparation to printing the report only takes about 3 min, this approach is promising to become a fundamental technique for high-throughput screening. To demonstrate the applicability of this method, the fatty acid composition of extra virgin olive oils from various Spanish olive varieties (arbequina, cornicabra, hojiblanca, manzanilla, and picual) was determined by ¹H-NMR spectroscopy according to this protocol.

A modified random forest approach to improve multi-class classification performance of tobacco leaf grades coupled with NIR spectroscopy

A novel approach, namely MC-UVE-RF, to improve multi-class classification performance of tobacco leaf grades by NIR spectroscopy.

Chemical Composition and Antioxidant Capacity of Brazilian Passiflora Seed Oils

The seed oils of different varieties of 4 Passiflora species cultivated in Brazil were analyzed and compared regarding their physicochemical parameters, fatty acid composition and the presence of minor components, such as phytosterols, tocopherols, total carotenoids, and phenolic compounds. The antioxidant capacities of the oil extracts were determined using the 2,2'azinobis [3-ethylbenzothiazoline-6-sulfonic acid] and oxygen radical absorbance capacity methods. The results revealed that all studied Passiflora seed oils possessed similar physicochemical characteristics, except for color, and predominantly contained polyunsaturated fatty acids with a high percentage of linolenic acid (68.75% to 71.54%). Other than the total phytosterol content, the extracted oil from Passiflora setacea BRS Pérola do Cerrado seeds had higher quantities (% times higher than the average of all samples), of carotenoids (44%), phenolic compounds (282%) and vitamin E (215%, 56%, 398%, and 100% for the α-tocopherol, β-tocopherol, γ-tocopherol, and δ-tocopherol isomers, respectively). The methanolic extracts from Passiflora setacea BRS Pérola do Cerrado seed oil also showed higher antioxidant activity, which was positively correlated with the total phenolic, δ-tocopherol, and vitamin E contents. For the first time, these results indicate that Passiflora species have strong potential regarding the use of their seeds for oil extraction. Due to their interesting composition, the seed oils may be used as a raw material in manufacturing industries in addition to other widely used vegetable oils.

Fatty Acids Composition of Vegetable Oils and Its Contribution to Dietary Energy Intake and Dependence of Cardiovascular Mortality on Dietary Intake of Fatty Acids

Characterizations of fatty acids composition in % of total methylester of fatty acids (FAMEs) of fourteen vegetable oils--safflower, grape, silybum marianum, hemp, sunflower, wheat germ, pumpkin seed, sesame, rice bran, almond, rapeseed, peanut, olive, and coconut oil--were obtained by using gas chromatography (GC). Saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA), palmitic acid (C16:0; 4.6%-20.0%), oleic acid (C18:1; 6.2%-71.1%) and linoleic acid (C18:2; 1.6%-79%), respectively, were found predominant. The nutritional aspect of analyzed oils was evaluated by determination of the energy contribution of SFAs (19.4%-695.7% E(RDI)), PUFAs (10.6%-786.8% E(RDI)), n-3 FAs (4.4%-117.1% E(RDI)) and n-6 FAs (1.8%-959.2% E(RDI)), expressed in % E(RDI) of 1 g oil to energy recommended dietary intakes (E(RDI)) for total fat (E(RDI)--37.7 kJ/g). The significant relationship between the reported data of total fat, SFAs, MUFAs and PUFAs intakes (% E(RDI)) for adults and mortality caused by coronary heart diseases (CHD) and cardiovascular diseases (CVD) in twelve countries has not been confirmed by Spearman's correlations.

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

In this study, the authenticity identification model was built by the one-class partial least squares (OCPLS) classifier for peanut oils, which could effectively detect adulterated oils at the adulteration level of more than 4%.

An improved method for extraction of high-quality total RNA from oil seeds

Seeds of oilseed plants that contain large amounts of oil, polysaccharides, proteins and polyphenols are not amenable to conventional RNA isolation protocols. The presence of these substances affects the quality and quantity of isolated nucleic acids. Here, a rapid and efficient RNA isolation protocol that, in contrast to other methods tested, allows high purify, integrity and yield of total RNA from seeds of sesame, corn, sunflower, flax and rapeseed was developed. The average yields of total RNA from 70 mg oil seeds ranged from 84 to 310 µg with A260/A280 between 1.9 and 2.08. The RNA isolated with this protocol was verified to be suitable for PCR, quantitative real-time PCR, semi-quantitative RT-PCR, cDNA synthesis and expression analysis.

Classification and adulteration detection of vegetable oils based on fatty acid profiles

The detection of adulteration of high priced oils is a particular concern in food quality and safety. Therefore, it is necessary to develop authenticity detection method for protecting the health of customers. In this study, fatty acid profiles of five edible oils were established by gas chromatography coupled with mass spectrometry (GC/MS) in selected ion monitoring mode. Using mass spectral characteristics of selected ions and equivalent chain length (ECL), 28 fatty acids were identified and employed to classify five kinds of edible oils by using unsupervised (principal component analysis and hierarchical clustering analysis), supervised (random forests) multivariate statistical methods. The results indicated that fatty acid profiles of these edible oils could classify five kinds of edible vegetable oils into five groups and are therefore employed to authenticity assessment. Moreover, adulterated oils were simulated by Monte Carlo method to establish simultaneous adulteration detection model for five kinds of edible oils by random forests. As a result, this model could identify five kinds of edible oils and sensitively detect adulteration of edible oil with other vegetable oils about the level of 10%.