There are two viable options to produce shrimp shells as by-product waste, either within the shrimp production phases or when the shrimp are peeled before cooking by the end user. This waste is considered a double-edged sword, as it is possible to be either a source of environmental pollution, through dumping and burning, or a promising source from which to produce chitosan as a biodegradable, biocompatible biopolymer which has a variety of agricultural, industrial, and biomedical applications. Chitosan is a deacetylated form of chitin that can be chemically recovered from shrimp shells through the three sequential stages of demineralization, deproteinization, and deacetylation. The main aim of this review paper is to summarize the recent literature on the chemical extraction of chitosan from shrimp shells and to represent the physicochemical properties of chitosan extracted from shrimp shells in different articles, such as chitosan yield, moisture content, solubility, ash content, and degree of deacetylation. Another aim is to analyze the influence of the main predictors of the chemical extraction stages (demineralization, deproteinization, and deacetylation) on the chitosan yield percentage by using a multilayer perceptron artificial neural network. This study showed that the deacetylation alkali concentration is the most crucial parameter, followed by the concentrations of acid and alkali of demineralization and deproteinization, respectively. The current review was conducted to be used in prospective studies for optimizing the chemical extraction of chitosan from shrimp wastes.

Green synthesis methods of silver nanoparticles have gained great attention because they offer sustainable, eco-friendly, and less-toxic alternatives to traditional methods. This study sheds light on the green synthesis of chitosan silver nanoparticle composites, providing a comparative evaluation of microwave-assisted (M1) and a one-pot (M2) reduction methods. The morphological, crystallinity, and structural uniformity characteristics were evaluated by UV-Visible, Raman spectroscopy, X-ray diffraction (XRD) and scanning electron microscopy (SEM) with employing image processing pipeline based on deep learning model for segmentation and particles size estimation. The UV-visible spectrum exhibited independent SPR peaks ranging from 400 to 450 nm for all samples; however, microwave assisted-synthesis possessed narrower and more intense peaks indicative of better crystallinity and mono-dispersity. SEM depicted smaller, more uniformly dispersed particles for microwave-assisted (M1), while deep learning segmentation showed lower particle size variability (σ ≈ 24–43 nm), compared to polydisperse (σ ≈ 16–59 nm) in M2 samples. XRD showed crystalline face-centered cubic (FCC) silver with dominant peaks in M1 samples, whereas M2 had broader, less intense peaks with amorphous features. Raman vibrations revealed more structural order and homogenous capping in M1 than M2. Therefore, microwave-assisted (M1) showed better control on nucleation, particle size, crystallinity, and homogeneity due to a faster and uniform energy distribution. The future research would focus on the antimicrobial evaluation of such nanoparticles in agronomy.

A pot study was conducted to assess the potential of green waste compost on soil properties, growth, physiology, and metal uptake of lettuce plants grown in acidic and neutral loam-textured soils irrigated with copper- and zinc-polluted wastewater (WW). The experiment consists of sixteen treatments involving two different soils with and without compost addition (compost and NoCompost) and irrigated with unpolluted WW, Cu-polluted WW, Zn-polluted WW, and Cu- plus Zn-polluted WW, arranged following factorial layout with three replications. The results illustrated that Cu- and Zn-polluted WW significantly reduced the growth, chlorophylls, and carotenoid pigments of lettuce plants in both soils under NoCompost conditions. However, the decline in these attributes was more pronounced in acidic soil (45–59%) than in neutral soil (30–38%). In the case of neutral soil, Zn-polluted WW did not negatively affect these attributes compared to control. All the metal-polluted treatments increased total polyphenols, polyphenolic acids, flavonoids, and antiradical activity in lettuce shoots. Alternatively, the compost application consistently increased (8–50%) the growth and physiological attributes of lettuce in both soils. Compost treatment decreased root and shoot metal (Cu, Zn) concentrations and uptake by 25–60% and 16–25%, respectively, in both soils. Likewise, compost decreased the metal health risk index (37%—2.7 folds) in both soils. Copper-polluted WW decreased the dehydrogenase activity of soils more than Zn-polluted WW, but compost significantly increased it in both soils, enhancing the organic matter contents of both soils. Conclusively, the addition of compost at the rate of 2% substantially alleviated the metal toxicity thereafter human health risks in both soils.

Optimizing chitosan recovery from shrimp shells is one of the most effective measures in shrimp waste management. Incorporating machine learning-based models will significantly impact the optimization process. This research aimed to evaluate the optimization of chitosan extraction from Litopenaeus vannamei shrimp shells using deproteinization and exploratory machine learning-based similarity model approaches. Chitosan extraction from shrimp shells was optimized using a deproteinization method, where various NaOH concentrations (1, 2, 3, 4, 5, and 10%) were applied at room temperature (RT) and 50 ± 2 °C, while maintaining controlled conditions for demineralization and deacetylation. The chitosan products were characterized by ash content, moisture, yield percentage, deproteinization efficiency, FTIR, deacetylation degree (DD), XRD, crystallinity index (CI%), and scanning electron microscopy (SEM). A machine learning random forest regressor model was developed to evaluate the similarities between the laboratory-synthesized and commercial chitosan (CC) samples. The results confirmed the formation of chitosan with semi-complete deacetylation (DD% from 98.84 ± 0.1% to 99.27 ± 0.004%). Deproteinization efficacy was in the range of 93.39 ± 0.083% to 97.0 ± 0.31%. XRD and SEM analyses demonstrated that commercial chitosan (CC) possessed a predominately amorphous structure, whereas the isolated chitosan samples exhibited low crystallinity, with increased amorphism at higher NaOH concentrations and temperatures. The machine learning-based similarity model indicated that Ch3 and Ch4 samples exhibited the highest resemblance degrees to commercial chitosan, while the S1 sample showed the lowest similarity. However, most of the recovered chitosan samples showed low similarity to commercial chitosan; they retained their higher degree of deacetylation (DD%), structural integrity, and quality parameters, indicating the success of the deproteinization route in enhancing chitosan production.