Antioxidant Activity of Sesame (Seasamum indicum L.) Cake Extract for the Stabilization of Olein Based Butter

This study investigated the effect of ethanolic sesame cake extract on oxidative stabilization of olein based butter. Fractionation of cream was performed by the dry fractionation technique at 10 °C, ethanolic sesame cake extract (SCE) was incorporated into olein butter at three different concentrations; 50, 100, 150 ppm (T1, T2, T3) and compared with a control. The total phenolic content of SCE was 1.72 (mg gallic acid equivalent g−1 dry weight). The HPLC characterization of ethanolic sesame cake revealed the presence of antioxidant substances viz. sesamol, sesamin and sesamolin in higher extents. The DPPH free radical scavenging activity of SCE was 83 % as compared to 64 and 75 % in BHA and BHT. Fractionation of milk fat at 10 °C significantly (p < 0.05) influenced the fatty acid profile of olein and stearin fractions from the parent milk fat. Concentration of oleic acid and linoleic acid in olein fraction was 29.62 and 33.46 % greater than the parent milk fat. The loss of C18:1 in 90 days stored control and T3 was 24.37 and 3.58 %, respectively, 58 % C18:2 was broken down into oxidation products over 8.55 % loss in T3. The peroxide value of control, T1, T2, BHT and T3 in the Schaal oven test was 8.59, 8.12, 5.34, 4.52 and 2.49 (mequiv O2/kg). The peroxide value and anisidine value of 3 months stored control and T3 were 1.21, 0.42 (mequiv O2/kg) and 27.25, 13.25, respectively. The concentration of conjugated dienes in T3 was substantially less than the control. The induction period of T3 was considerably higher than BHT with no difference in sensory characteristics (p > 0.05). Ethanolic SCE can be used for the long-term preservation of olein butter, with acceptable sensory characteristics.

Evaluation of methodologies to determine vegetable oil species present in oil mixtures: Proposition of an approach to meet the EU legislation demands for correct vegetable oils labelling

Refined vegetable oils are widely used in the food industry as ingredients or components in many processed food products in the form of oil blends. To date, the generic term 'vegetable oil' has been used in the labelling of food containing oil blends. With the introduction of new EU Regulation for Food Information (1169/2011) due to take effect in 2014, the oil species used must be clearly identified on the package and there is a need for development of fit for purpose methodology for industry and regulators alike to verify the oil species present in a product. The available methodologies that may be employed to authenticate the botanical origin of a vegetable oil admixture were reviewed and evaluated. The majority of the sources however, described techniques applied to crude vegetable oils such as olive oil due to the lack of refined vegetable oil focused studies. Nevertheless, DNA based typing methods and stable isotopes procedures were found not suitable for this particular purpose due to several issues. Only a small number of specific chromatographic and spectroscopic fingerprinting methods in either targeted or untargeted mode were found to be applicable in potentially providing a solution to this complex authenticity problem. Applied as a single method in isolation, these techniques would be able to give limited information on the oils identity as signals obtained for various oil types may well be overlapping. Therefore, more complex and combined approaches are likely to be needed to identify the oil species present in oil blends employing a stepwise approach in combination with advanced chemometrics. Options to provide such a methodology are outlined in the current study.

Development of a novel continuous statistical modelling technique for detecting the adulteration of extra virgin olive oil with hazelnut oil by using spectroscopic data

The adulteration of extra virgin olive oil with other vegetable oils is a certain problem with economic and health consequences. Current official methods have been proved insufficient to detect such adulterations. One of the most concerning and undetectable adulterations with other vegetable oils is the addition of hazelnut oil. The main objective of this work was to develop a novel dimensionality reduction technique able to model oil mixtures as a part of an integrated pattern recognition solution. This final solution attempts to identify hazelnut oil adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. The proposed Continuous Locality Preserving Projections (CLPP) technique allows the modelling of the continuous nature of the produced in house admixtures as data series instead of discrete points. This methodology has potential to be extended to other mixtures and adulterations of food products. The maintenance of the continuous structure of the data manifold lets the better visualization of this examined classification problem and facilitates a more accurate utilisation of the manifold for detecting the adulterants.

A new analytical approach for the detection of palm oil in vegetable oil blends

The new Food Information Regulation (1169/2011), dictates that in a refined vegetable oil blend, the type of oil must be clearly identified in the package in contract with current practice where is labelled under the generic and often misleading term “vegetable oil”. With increase consumer awareness in food authenticity, as shown in the recent food scandal with horsemeat in beef products, the identification of the origin of species in food products becomes increasingly relevant. Palm oil is used extensively in food manufacturing and as global demand increases, producing countries suffer from the aftermath of intensive agriculture. Even if only a small portion of global production, sustainable palm oil comes in great demand from consumers and industry. It is therefore of interest to detect the presence of palm oil in food products as consumers have the right to know if it is present in the product or not, mainly from an ethical point of view. Apart from palm oil and its derivatives, rapeseed oil and sunflower oil are also included. With DNA-based methods, the gold standard for the detection of food authenticity and species recognition deemed not suitable in this analytical problem, the focus is inevitably drawn to the chromatographic and spectroscopic methods. Both chromatographic (such as GC-FID and LC-MS) and spectroscopic methods (FT-IR, Raman, NIR) are relevant. Previous attempts have not shown promising results due to oils’ natural variation in composition and complex chemical signals but the suggested two-step analytical procedure is a promising approach with very good initial results.

Identifying oil species in oil mixtures

Detection of adulteration of non-processed vegetable oil with lesser value seed oils (classic example is hazelnut in virgin olive oil) has been in the centre of scientific attention for many years and several chemical methods were proposed. The recent EC Regulation 1169/2011, however, introduces necessity for different analytical method in a more complicated matrix. From the end of 2014, food businesses required to declare the composition of the refined oil mixture in the food product label. This creates a gap since there is no analytical method currently available to perform such analysis. In the first phase the work focused on 100% oil blends of various oil species of palm oil (and derivatives), sunflower and rapeseed oil before expanding to foodstuffs. Chromatographic methods remain highly relevant although suffer from various limitations which derive from natural compositional variation. Modern multivariate techniques based on machine learning algorithms, however, when applied in FTIR, Raman spectroscopic data have a strong potential in tackling the problem.

Virgin Olive Oil: Losses of Antioxidant Polar Phenolic Compounds due to Storage, Packaging, and Culinary Uses

Virgin olive oil is a high quality natural product obtained only by physical means. In addition to triacylglycerols it contains nutritionally important polar and non-polar antioxidant phenols and other bioactive ingredients. The polar fraction is a complex mixture of phenolic acids, simple phenols, derivatives of the glycosides oleuropein and ligstroside, lignans, and flavonoids. These compounds contribute significantly to the stability, flavor, and biological value of virgin olive. In the various stages of production, during storage and in the culinary uses, polar phenols and other valuable bioactive ingredients may be damaged. Oxidation, photo-oxidation, enzymic hydrolysis and heating at frying temperatures have a serious adverse effect. Due to the biological importance of the oil and its unique character, analytical methods have been developed to evaluate antioxidant activity or analyse complex phenol mixtures. These are based on radical scavenging assays and chromatographic techniques. Hyphenated methods are also used including liquid chromatography-mass spectrometry and liquid chromatography-nuclear magnetic resonance spectroscopy.

A multi-capability sensor for hydrocarbons, synthetic-based fluids and heavy metals:Applications for environmental monitoring during removal of drill cuttings piles

As part of any drilling cuttings pile removal process the requirement for monitoring the release of contaminants into the marine environment will be critical. Traditional methods for such monitoring involve taking samples for laboratory analysis. This process is time consuming and only provides data on spot samples taken from a limited number of locations and time frames. Such processes, therefore, offer very restricted information. The need for improved marine sensors for monitoring contaminants is established. We report here the development and application of a multi-capability optical sensor for the real-time in situ monitoring of three key marine environmental and offshore/oil parameters: hydrocarbons, synthetic-based fluids and heavy metal concentrations. The use of these sensors will be a useful tool for real-time in situ environmental monitoring during the process of decommissioning offshore structures. Multi-capability array sensors could also provide information on the dispersion of contamination from drill cuttings piles either while they are in situ or during their removal.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.