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.

Remediation of oily wastewater from an interceptor tank using a novel photocatalytic drum reactor

A novel photocatalytic reactor has been developed to remediate oily wastewaters. In the first instance degradation rates of model organic compounds, methylene blue (MB) and 4-c hlorophenol (4-CP) were determined. The experimental set-up investigated a 1:10 w/v catalyst to organic solution volume, 30 g catalyst, 300 mls MB (10 μM) or 4-CP (100 μM). The catalyst investigated was a pellet catalyst to improve separation of the remediated volume from the catalyst following treatment. MB concentration decreased by 93% after 15 mins irradiation whilst 4-CP concentration decreased by 94% following 90 mins irradiation. Oily waste water (OWW) from an interceptor tank typically containing diesel oils was obtained from Sureclean, an environmental clean-up company. The OWW was treated using the same conditions as MB and 4-CP, the model organic compounds. Levels of total organic carbon (TOC) and total petroleum hydrocarbon (TPH) were used to monitor the efficacy of the photocatalytic reactor. TOC reduced by 45% following two 90 mins treatment cycles. TPH reduced by 45% following 90 mins irradiation and by a further 25% during a second stage of treatment. This reactor can be used as a polishing technique assembled within a wastewater treatment plant. Allowing for more than one pass through the reactor improves its efficiency. 

Liquid coordination complexes: a new class of Lewis acids as safer alternatives to BF3 in synthesis of polyalphaolefins

Liquid coordination complexes (LCCs) are a new class of liquid Lewis acids, prepared by combining an excess of a metal halide (e.g. GaCl3) with a basic donor molecule (e.g. amides, amines or phosphines). LCCs were used to catalyse oligomerisation of 1-decene to polyalphaolefins (PAOs). Molecular weight distribution and physical properties of the produced oils were compliant with those required for low viscosity synthetic (Group IV) lubricant base oils. Kinematic viscosities at 100 °C of ca. 4 or 6 cSt were obtained, along with viscosity indexes above 120 and pour points below −57 °C. In industry, to achieve similar properties, BF3 gas is used as a catalyst. LCCs are proposed as a safer and economically attractive alternative to BF3 gas for the production of polyalphaolefins.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.