Saffron (Crocus sativus L.) is known as the most expensive spice. C. sativus dried red stigmas, called threads, are used for culinary, cosmetic, and medicinal purposes. The rest of the flower is often discarded, but is now being used in teas, as coloring agents, and fodder. Previous studies have attributed antioxidant, anti-inflammatory, hepatoprotective, neuroprotective, anti-depressant, and anticancer properties to C. sativus floral bio-residues. The aim of this study is to assess C. sativus flower water extract (CFWE) for its effects on hemoglobin, brush boarder membrane (BBM) functionality, morphology, intestinal gene expression, and cecal microbiome in vivo (Gallus gallus), a clinically validated model. For this, Gallus gallus eggs were divided into six treatment groups (non-injected, 18 Ω H₂O, 1% CFWE, 2% CFWE, 5% CFWE, and 10% CFWE) with n~10 for each group. On day 17 of incubation, 1 mL of the extracts/control were administered in the amnion of the eggs. The amniotic fluid along with the administered extracts are orally consumed by the developing embryo over the course of the next few days. On day 21, the hatchlings were euthanized, the blood, duodenum, and cecum were harvested for assessment. The results showed a significant dose-dependent decrease in hemoglobin concentration, villus surface area, goblet cell number, and diameter. Furthermore, we observed a significant increase in Paneth cell number and Mucin 2 (MUC2) gene expression proportional to the increase in CFWE concentration. Additionally, the cecum microbiome analysis revealed C. sativus flower water extract altered the bacterial populations. There was a significant dose-dependent reduction in Lactobacillus and Clostridium sp., suggesting an antibacterial effect of the extract on the gut in the given model. These results suggest that the dietary consumption of C. sativus flower may have negative effects on BBM functionality, morphology, mineral absorption, microbial populations, and iron status.

The dairy as well as other food industries face major challenges as retirement of experienced staff members can lead to substantial knowledge and skill gaps. New artificial intelligence and modeling tools may offer an opportunity for the dairy industry to capture existing expert knowledge into digital systems. A specific industry challenge includes the identification of fluid milk spoilage patterns, which is a pre-requisite for selecting and implementing targeted spoilage control strategies; traditionally, these classification tasks have been performed by human experts. To address this challenge, we developed a machine-learning-based digital expert system that uses microbiological data to classify the major types of fluid milk spoilage (e.g., spoilage due to post-pasteurization contamination with Gram-negative bacteria versus spoilage due to sporeforming bacteria). Microbiological data collected from shelf-life testing of 770 fluid milk samples were reviewed by 3 experts, who assigned milk samples into 3 distinct spoilage types, including (i) spoilage due to post-pasteurization contamination by Gram-negative bacteria, (ii) spoilage due to sporeformer contamination, and (iii) no microbial spoilage. This data set was then split into training, validation, and testing sets to develop a baseline model that can classify fluid milk spoilage. Additionally, 9 models based on subsets of data representing scenarios such as reducing the number of days or type of media needed for shelf-life testing were trained, validated, and subsequently tested based on the spoilage type classification accuracy. The baseline model, which included microbiological data from d 7, 14, and 21 of shelf-life, was validated with 91.7% classification accuracy and tested with 96.4% classification accuracy. Among the 9 models that represent different scenarios, the 2 models that included both standard plate counts and total Gram-negative counts (i) on d 14 and 21 of shelf-life and (ii) only on d 21 of shelf-life were both able to classify the spoilage types with 94.2% testing accuracy, suggesting a potential for optimizing shelf-life testing. Overall, our models have the potential to help milk processors (i) optimize their microbiological testing schemes to reduce costs and resources, (ii) identify the predominant spoilage pattern in a facility, and (iii) investigate the root-cause of spoilage to develop targeted interventions.