Search

We present a comprehensive ab initio, high-throughput study of the frictional and cleavage strengths of interfaces of elemental crystals with different orientations. It is based on the detailed analysis of the adhesion energy as a function of lateral, γ(x, y), and perpendicular displacements, γ(z), with respect to the considered interface plane. We use the large amount of computed data to derive fundamental insight into the relation of the ideal strength of an interface plane with its adhesion. Moreover, the ratio between the frictional and cleavage strengths is provided as good indicator for the material failure mode – dislocation propagation versus crack nucleation. All raw and curated data are made available to be used as input parameters for continuum mechanic models, benchmarks, or further analysis.

Herein, the potential of Fourier transform infrared spectroscopy (FTIR) spectroscopy coupled with principal component analysis and artificial neural networks to discriminate butters obtained from milk creams collected in different seasons (spring, summer or winter) and produced from two feeding regimens (traditional or unifeed) in the Parmigiano Reggiano cheese area was investigated. The predictive ability of neural networks to predict the season of production was 100%. The favorable results obtained by artificial neural network (ANN) analysis were in agreement with those reported by principal component analysis (PCA) analysis. The ability of ANNs to predict the feeding regimens was 90.0%, 75.0% and 75.0%, respectively, for samples collected in spring, summer and winter. These results also confirm that the method is highly suitable for its intended purpose. Highlights ► 405 FT-IR spectra of butter were analyzed by PCA and ANN. ► ANN discriminate the season of production and dietary regimen of butters. ► The ability of ANN to predict the season of production of milk cream was 100%.

The seasonal variation of the fatty acids composition of butters were investigated over three seasons during a 12-month study in the protected designation of origin Parmigiano-Reggiano cheese area. Fatty acids were analyzed by GC-FID, and then computed by artificial neural networks (ANN). Compared with spring and winter, butter manufactured from summer milk creams showed an optimal saturated/un-saturated fatty acids ratio (−8.89 and −5.79%), lower levels of saturated fatty acids (−2.63 and −1.68%) and higher levels of mono-unsaturated (+5.50 and +3.45%), poly-unsaturated fatty acids (+0.65 and +0.17%), and rumenic acid (+0.55 and +3.41%), while vaccenic acid had lower levels in spring and higher in winter (−2.94 and +2.91%). Moreover, the ANN models were able to predict the season of production of milk creams, and classify butters obtained from spring and summer milk creams on the basis of the type of feeding regimens. Practical applications: The investigation on variables that affect the milk fatty acids composition can improve the quality of milk across all systems, and the combination of chromatographic and computational techniques will ensure a secure traceability enabling producers to characterize dairy products.