BACKGROUND The emergence of monoclonal antibodies (mAbs) as new biopharmaceutical products requires the development of new purification methods that are not only effective but are able to reduce production costs. To address the problematic recovery of mAbs, gum arabic (GA) coated magnetic particles (MPs) were used for the purification of human antibodies from animal cells supernatants. RESULTS MPs were synthesized via co-precipitation and exhibited a spherical-like physical aspect, with an average hydrodynamic diameter of 473 nm and a zeta potential of –26 mV. The adsorption and elution of IgG on these adsorbents was thoroughly studied. Adsorption of human IgG was enhanced at pH 6, for which a qmax of 244 mg IgG g−1 MPs and Kd of 25 mg L−1 were obtained. Increasing salt concentrations at a basic pH (1 mol L−1 NaCl at pH 11) were found to improve the elution of bound IgG. The MPs were challenged with an artificial protein mixture containing human IgG, albumin, insulin and apo-transferrin. An overall yield of 84% was achieved, retrieving 92% of bound IgG. CONCLUSIONS MPs were successfully used for the capture of monoclonal antibodies from two distinct mammalian cell cultures, a Chinese hamster ovary (CHO) and a hybridoma cell culture supernatants. The elution yields were high, ranging between 84% and 94%, with overall yields ranging from 72% to 88%. Final purities of 85% were reached for hybridoma cell supernatants. © 2014 Society of Chemical Industry

Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This
work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by
sensing gels in an in-house developed e-nose. The proposed signals’ analysis methodology is based on the
generated signals’ morphology for each VOC since different sensing gels produce signals with different shapes
when exposed to the same VOC. For this study, two different gel formulations were considered, and an average
f1-score of 84% and 71% was obtained, respectively. Moreover, a standard method in time series classification
was used to compare the performances. Even though this comparison reveals that the morphological approach
is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive
sentences with text mining techniques to perform VOC classification.