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Organic polymer coatings have been extensively used for painting in industrial applications to prevent the deleterious effects on the metallic components. In this study, electrostatic polyester coatings with two amounts of graphite (5 and 10 wt% Gr) were deposited on structural steel substrate. The effect of Gr addition on the microstructure, mechanical, adhesion and tribological behavior were characterized using SEM, EDS, XRD, nano-indentation and scratch test. The coatings’ resistance to corrosion was investigated using electrochemical measurements in 3.5 wt% NaCl solution. According to the results, the Gr particles were homogeneously distributed throughout the polyester coatings. The hardness and elastic modulus of the polyester coatings increased from ~0.23 and ~6.7 to ~0.68 and 10.9 GPa upon increasing the Gr contents from 0 to 10 wt% Gr, respectively. Moreover, the friction coefficient decreased from ~0.5 to ~0.2. The results also showed an attenuation of the severe wear mechanism and a reduction of the plastic deformation. Consequently, the wear resistance improved by three times due to the lubricating role of Gr. All the coatings showed a high adhesion level to the steel substrate with a cohesive critical load higher than Lc1 = ~7 N and an adhesion strength greater than σc = ~215 MPa. The incorporation of Gr allows high protection of the steel against corrosion due to their barrier role.

Pine seeds are known for their richness in lipid compounds and other healthy substances. However, the reference procedures that are commonly applied for their analysis are quite laborious, time-consuming, and expensive. Therefore, it is important to develop rapid, accurate, multi-parametric, cost-effective and, essentially, environmentally friendly analytical techniques that are easily implemented at an industrial scale. The viability of using near-infrared (NIR) spectroscopy to analyse the seed lipid content and profile of three different pine species (Pinus halepensis, Pinus brutia and Pinus pinaster) was investigated. Moreover, species discrimination using NIR was also attempted. Different chemometric models, namely partial least squares (PLS) regression, for lipid analysis, and partial least square—discriminant analysis (PLS-DA), for pine species discrimination, were applied. In relation to the discrimination of pine seed species, a total of 90.5% of correct classification rates were obtained. Regarding the quantification models, most of the compounds assessed yielded determination coefficients (R2P) higher than 0.80. The best PLS models were obtained for total fat, vitamin E, saturated and monounsaturated fatty acids, C20:2, C20:1n9, C20, C18:2n6c, C18:1n9c, C18 and C16:1. Globally, the obtained results demonstrated that NIR spectroscopy is a suitable analytical technique for lipid analysis and species discrimination of pine seeds.