Due to the characteristics of electrospun nanofibers (NFs), they are considered a suitable substrate for the adsorption and removal of heavy metals. Electrospun nanofibers are prepared based on optimized polycaprolactone (PCL, 12 wt%) and polyacrylic acid (PAA, 1 wt%) polymers loaded with graphene oxide nanoparticles (GO NPs, 1 wt%). The morphological, molecular interactions, crystallinity, thermal, hydrophobicity, and biocompatibility properties of NFs are characterized by spectroscopy (scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, Thermogravimetric analysis), contact angle, and MTT tests. Finally, the adsorption efficacy of NFs to remove lead (Pb²⁺) from water and apple juice samples was determined using inductively coupled plasma optical emission spectroscopy (ICP-OES). The average diameter for PCL, PCL/PAA, and PCL/PAA/GO NFs was 137, 500, and 216 nm, respectively. Additionally, the contact angle for PCL, PCL/PAA, and PCL/PAA/GO NFs was obtained at 74.32º, 91.98º, and 94.59º, respectively. The cytotoxicity test has shown non-toxicity for fabricated NFs against the HUVEC endothelial cell line by more than 80% survival during 72 h. Under optimum conditions including pH (= 6), temperature (25 °C), Pb concentration (25 to 50 mg/L), and time (15 to 30 min), the adsorption efficiency was generally between 80 and 97%. The adsorption isotherm model of PCL/PAA/GO NFs in the adsorption of lead metal follows the Langmuir model, and the reaction kinetics follow the pseudo-second-order. PCL/PA/GO NFs have shown adsorption of over 80% in four consecutive cycles. The adsorption efficacy of NFs to remove Pb in apple juice has reached 76%. It is appropriate and useful to use these nanofibers as a high-efficiency adsorbent in water and food systems based on an analysis of their adsorption properties and how well they work.

The demand for meat products has increased significantly due to changes in people’s lifestyles and rising meat prices. The study aims to investigate the potential use of an accurate and reliable method for detecting adulteration and ensuring the quality of meat products. The sausages are made with various meat from 5 to 100% pure meat. The samples are then analyzed using Fourier-transform Near-Infrared (FT-NIR) spectroscopy. This research used a NIR spectrophotometer, Principal component analysis (PCA), and Linear discriminant analysis (LDA) to identify beef, chicken, donkey, and horse handmade sausage samples. Using PCA, two principal components were extracted that covered 99 percent of the data variance and accurately distinguished sausage samples from different animal meats. Following this, the LDA model can confidently classify the sausage samples and achieved 100% accuracy in classifying all test samples. To conclude, NIR spectroscopy is a reliable tool for sausage quality evaluation and species identification, which can identify different animal species in meat products.