University-industry technology transfer models: an empirical analysis

The aim of this paper is to contribute to the understanding of the underlying factors in the process of transferring technology from university to industry. Findings point to strategic importance of critical factors as the definition of common objectives, cooperation, motivation, and the elimination of technical and legal barriers. These challenges must have implications in the incorporation of cooperative aspects of research projects in the design of public innovation policies.

Elimination of variability sources in NIR spectra for the determination of fat content in olive fruits

Models for prediction of oil content as percentage of dried weight in olive fruits were comput- ed through PLS regression on NIR spectra. Spectral preprocessing was carried out by apply- ing multiplicative signal correction (MSC), Sa vitzky–Golay algorithm, standard normal variate correction (SNV), and detrending (D) to NIR spectra. MSC was the preprocessing technique showing the best performance. Further reduction of variability was performed by applying the Wold method of orthogonal signal correction (OSC). The calibration model achieved a R 2 of 0.93, a SEPc of 1.42, and a RPD of 3.8. The R 2 obtained with the validation set remained 0.93, and the SEPc was 1.41.