A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater.

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box-Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 µL·L-1). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.

A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey.

According to European Union regulations, honey is a pure product and adding to or removing from it any kind of substance is illegal. Nevertheless, its adulteration by adding inexpensive and artificial adulterants is a common practice. This paper deals with the use of visible and near-infrared spectroscopy (Vis-NIRS) combined with chemometric tools as a screening technique for the identification and quantification of different types of adulterants (inverted sugar, rice syrup, brown cane sugar and fructose syrup) added to high-quality honey (Granada Protected Designation of Origin, Spain) at different levels (5%-50%). A complete discrimination between non-adulterated and adulterated samples was achieved. A general regression model to quantify the adulteration levels was developed as well as specific models for each adulterant. The coefficients of determination were higher than 0.96 for all the models. These results demonstrate the capacity of Vis-NIRS combined with chemometric tools for honey quality control.