Surface hardness determination of laser cladding using laser-induced breakdown spectroscopy and machine learning (PLSR, CNN, ResNet, and DRSN).

In this study, we employed laser-induced breakdown spectroscopy (LIBS) along with machine learning algorithms, which encompass partial least squares regression (PLSR), the deep convolutional neural network (CNN), the deep residual neural network (ResNet), and the deep residual shrinkage neural network (DRSN), to estimate the surface hardness of laser cladding layers. (The layers were produced using Fe316L, FeCrNiCu, Ni25, FeCrNiB, and Fe313 powders, with 45 steel and Q235 serving as substrates.) The research findings indicate that both linear and nonlinear models can effectively fit the relationship between LIBS spectra and surface hardness. Particularly, the model derived from the ResNet exhibits superior performance with an R 2 value as high as 0.9967. We hypothesize that the inclusion of numerous noises in the LIBS spectra contributes to the enhanced predictive capability for surface hardness, thereby leading to the superior performance of the ResNet compared to the DRSN.

Comparative Peptidomics Analysis of Fermented Milk by Lactobacillus delbrueckii ssp. bulgaricus and Lactobacillus delbrueckii ssp. lactis.

Few studies have investigated the peptidomics of fermented milk by Lactobacillus delbrueckii. The aim of the present study was to interpret the peptidomic pattern of the fermented milk by five strains of L. delbrueckii ssp. bulgaricus and ssp. lactis prior to and after the simulated gastrointestinal digestion in vitro. The results indicated variations in the peptidomics among the samples, particularly between the samples of different subspecies. The peptides originating from ß-casein were abundant in the samples of ssp. bulgaricus, whereas the peptides derived from αs1-casein and αs2-casein were more likely to dominate in those of ssp. lactis. For ß-casein, the strains of ssp. bulgaricus displayed extensive hydrolysis in the regions of (73-97), (100-120), and (130-209), whereas ssp. lactis mainly focused on (160-209). The digestion appears to reduce the variations of the peptidomics profile in general. Among the five strains, L. delbrueckii ssp. bulgaricus DQHXNS8L6 was the most efficient in the generation of bioactive peptides prior to and after digestion. This research provided an approach for evaluating the peptide profile of the strains during fermentation and digestion.