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Dunaliella tertiolecta is a marine microalgae that has been studied extensively as a potential carbon-neutral biofuel source (Tang et al., 2011). Microalgae oil contains high quantities of energy-rich fatty acids and lipids, but is not yet commercially viable as an alternative fuel. Carefully optimised growth conditions, and more recently, algal-bacterial co-cultures have been explored as a way of improving the yield of D. tertiolecta microalgae oils. The relationship between the host microalgae and bacterial co-cultures is currently poorly understood. Here, a complete workflow is proposed to analyse the global metabolomic profile of co-cultured D. tertiolectra and Phaeobacter italicus R11, which will enable researchers to explore the chemical nature of this relationship in more detail. To the best of the authors' knowledge this study is one of the first of its kind, in which a pipeline for an entirely untargeted analysis of the algal metabolome is proposed using a practical sample preparation, introduction, and data analysis routine. Agency for Science, Technology and Research (A*STAR) The authors wish to acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and the support given to The Metabolomics Innovation Centre (TMIC) through grants from Genome Alberta, Genome Canada, and The Canada Foundation for Innovation. This work was also supported by A*STAR SFS IAF-PP grant (A20H7a0152) awarded to Rebecca Case.

Infection of plants by pathogens can result in the upregulation of induced defenses; plants may be more or less susceptible to attack by insect herbivores following infection. We investigated the interaction between canola, Brassica napus L., plants infected with clubroot, Plasmodiophora brassicae Woronin, and a generalist herbivore the bertha armyworm (BAW) Mamestra configurata Walker using two canola cultivars that varied in susceptibility to clubroot disease. Volatile organic compounds released from experimental plants differed with infection and female adult BAW could discriminate between canola plants inoculated with P. brassicae and disease-free plants. Adult female moths preferentially laid eggs on disease-free plants of the susceptible cultivar to P. brassicae. Inoculation of resistant canola with P. brassicae, however, did not influence oviposition by female BAW. The fitness of BAW larvae was reduced when they were reared on susceptible canola inoculated with P. brassicae. Salicylic acid and its conjugates in susceptible canola plants were induced following P. brassicae inoculation as compared to disease-free susceptible plants. We conclude that suppression of BAW oviposition and offspring fitness may result in part from a change in the volatile profile of the plant as a result of inoculation and the induction of defenses in inoculated susceptible canola.

Quantification of comprehensive two-dimensional gas chromatographic (GC×GC) data in a commercial software package is examined. ChromaTOF(®) (Leco Instruments) includes a smoothing step prior to data integration. Improper choice of integration parameters can introduce errors in excess of ±10% and as high as 60% in the total peak area. Herein we demonstrate the critical nature of the smoothing parameters in this software, particularly the expected peak width in the second dimension ((2)w) which should be verified as part of a QA/QC protocol.

Oil blends containing olive oil are commonly commercialized due to economical and nutritional reasons. According to European trade standards only when an oil blend contains more than 50% (w/w) of olive oil it can be marketed with images or graphics highlighting its presence. In this work a fast method for verifying the correct labelling of such oil blends was developed. First, the capability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy to distinguish olive oil was tested both in pure oil samples and in blends of different oils by using a method based on partial least squares discriminant analysis (PLS-DA). 100% of the olive oils samples were correctly identified within a wide collection of pure samples of different types of vegetable oils including olive oil of different categories, varieties and origin. When studying oil blends, it was possible to differentiate between blends with olive oil content higher than 50% (w/w) and below 50% (w/w). Finally, an attempt of quantification of olive oil in blends based on PLS regression was performed. For blend samples with olive oil contents up to 50% (w/w) a prediction error of 8.28 was obtained.