Weaning stress in piglets can lead to poor health outcomes and reduced production. We investigated the effects of probiotics, one potential antibiotic alternative, on the growth performance, serum biochemical parameters, intestinal morphology, mucosal immunity, hypothalamic neurotransmitters, and colonic microflora in weaned piglets. Thirty-six weaned piglets were fed a basal diet, a diet supplemented with colistin sulphate antibiotic, or a diet supplemented with probiotics including Clostridium butyricum, Bacillus subtilis, and B. licheniformis. Probiotics significantly increased the feed : gain ratio, improved the average day gain from day 1 to day 28, and decreased the diarrhoea index. Probiotics also lowered the serum concentrations of AST, ALT, and ALP on day 14 and lowered the serum concentration of ALT on day 28 compared with the control. Probiotic supplementation caused fewer ileal apoptotic cells. The serum and ileal concentrations of TNF-α and IL-1β on day 28 were significantly lowered, and the serum concentrations of IL-6 were significantly lowered on days 14 and 28. Probiotic-fed piglets exhibited higher contents of hypothalamic serotonin and dopamine as well as serum γ-aminobutyric acid along with higher colonic concentrations of butyrate and valerate on day 28. High-throughput sequencing showed 972 core operational taxonomic units among all groups, of which 48 were unique to the probiotic-treated group. The relative abundance of genus Bacillus and species Bacillus velezensis was enriched in probiotic piglets; the phylogenetic investigation of communities by the reconstruction of unobserved states indicated that amino acid metabolism, DNA repair, replication and recombination proteins, and secretion systems were enriched with probiotics. In conclusion, the Clostridium butyricum-based probiotics improved growth performance, enhanced intestinal morphology, changed hypothalamic neurotransmitters and modulated colonic microflora in weaned piglets.

Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.

Food packaging plays a vital role in the food supply chain by acting as an additional layer to protect against food contamination, but the main function of traditional conventional packaging is only to isolate food from the outside environment, and cannot provide related information about food spoilage. Intelligent packaging can feel, inspect, and record external or internal changes in food products to provide further information about food quality. Importantly, intelligent packaging indicators will account for a significant proportion of the food industry’s production, with promising application potential. In this review, we mainly summarize and review the upcoming progress in the classification, preparation, and application of food packaging indicators. Equally, the feasibility of 3D printing in the preparation of intelligent food packaging indicators is also discussed in detail, as well as the limitations and future directions of smart food packaging. Taken together, the information supported in this paper provides new insights into monitoring food spoilage and food quality.