The emergence of biofuels and the co-movement between crude oil and agricultural prices

Biofuels are becoming an alternative to non-renewable energy sources but we know little about the economic mechanisms influencing their prices. This paper studies the interrelationships between the spot prices of oil and those of agricultural commodities used as biofuel feedstocks. Using daily data since 1988, we identify a co-movement after 2005 that does not appear for other food-related commodities and is not due to general economic variables. We also find traces of the co-movement in the prices of a large biofuel stock. The results amount to the first systematic piece of empirical evidence linking spot oil and agricultural markets via the emergence of biofuels.

Combining phospholipases and a liquid lipase for one-step biodiesel production using crude oils

Background Enzymatic biodiesel is becoming an increasingly popular topic in bioenergy literature because of its potential to overcome the problems posed by chemical processes. However, the high cost of the enzymatic process still remains the main drawback for its industrial application, mostly because of the high price of refined oils. Unfortunately, low cost substrates, such as crude soybean oil, often release a product that hardly accomplishes the final required biodiesel specifications and need an additional pretreatment for gums removal. In order to reduce costs and to make the enzymatic process more efficient, we developed an innovative system for enzymatic biodiesel production involving a combination of a lipase and two phospholipases. This allows performing the enzymatic degumming and transesterification in a single step, using crude soybean oil as feedstock, and converting part of the phospholipids into biodiesel. Since the two processes have never been studied together, an accurate analysis of the different reaction components and conditions was carried out. Results Crude soybean oil, used as low cost feedstock, is characterized by a high content of phospholipids (900 ppm of phosphorus). However, after the combined activity of different phospholipases and liquid lipase Callera Trans L, a complete transformation into fatty acid methyl esters (FAMEs >95%) and a good reduction of phosphorus (P <5 ppm) was achieved. The combination of enzymes allowed avoidance of the acid treatment required for gums removal, the consequent caustic neutralization, and the high temperature commonly used in degumming systems, making the overall process more eco-friendly and with higher yield. Once the conditions were established, the process was also tested with different vegetable oils with variable phosphorus contents. Conclusions Use of liquid lipase Callera Trans L in biodiesel production can provide numerous and sustainable benefits. Besides reducing the costs derived from enzyme immobilization, the lipase can be used in combination with other enzymes such as phospholipases for gums removal, thus allowing the use of much cheaper, non-refined oils. The possibility to perform degumming and transesterification in a single tank involves a great efficiency increase in the new era of enzymatic biodiesel production at industrial scale.

Prediction of crude protein and classification of the growth stage of wheat plant samples from NIR spectra

Near-infrared spectroscopy (NIRS) was used to analyse the crude protein content of dried and milled samples of wheat and to discriminate samples according to their stage of growth. A calibration set of 72 samples from three growth stages of wheat (tillering, heading and harvest) and a validation set of 28 samples was collected for this purpose. Principal components analysis (PCA) of the calibration set discriminated groups of samples according to the growth stage of the wheat. Based on these differences, a classification procedure (SIMCA) showed a very accurate classification of the validation set samples : all of them were successfully classified in each group using this procedure when both the residual and the leverage were used in the classification criteria. Looking only at the residuals all the samples were also correctly classified except one of tillering stage that was assigned to both tillering and heading stages. Finally, the determination of the crude protein content of these samples was considered in two ways: building up a global model for all the growth stages, and building up local models for each stage, separately. The best prediction results for crude protein were obtained using a global model for samples in the two first growth stages (tillering and heading), and using a local model for the harvest stage samples.