The present study proposes a system for co-composting food waste and poultry manure amended with rice husk biochar at different doses (0, 3, 5, 10%, w/w), saw dust, and salts. The effect of rice husk biochar on the characteristics of final compost was evaluated through stabilization indices such as electrical conductivity, bulk density, total porosity, gaseous emissions and nitrogen conservation. Results indicated that when compared to control, the biochar amendment extended the thermophilic stage of the composting, accelerated the biodegradation and mineralization of substrate mixture and helped in the maturation of the end product. Carbon dioxide, methane and ammonia emissions were reduced and the nitrogen conservation was achieved at a greater level in the 10% (w/w) biochar amended treatments. This study implies that the biochar and salts addition for co-composting food waste and poultry manure is beneficial to enhance the property of the compost.
Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
Overgrowth of microalgae will result in harmful algae blooms that can affect the aquatic ecosystem and human health. Therefore, the quantitation of chlorophyll pigments can be used as an indicator of algae bloom. However, it is difficult to monitor the geographical and temporal distribution of chlorophyll in the aquatic environment. Accordingly, an innovative and inexpensive method based on the red-green-blue (RGB) image analysis was utilized in this study to estimate the microalgae chlorophyll content. The digital images were acquired using a smartphone camera. The colour index was then evaluated using software and associated with chlorophyll concentration significantly. A regression model, using RGB colour components as independent variables to estimate chlorophyll concentration, was developed and validated. The Green in the RGB index was the most promising way to estimate chlorophyll concentration in microalgae. The result showed that acetone was the best extractant solvent with a high R-squared value among the four extractant solvents. Next, the isolation of useful biomolecules, such as proteins, fatty acids, polysaccharides and antioxidants from the microalgae, has been recognized as an alternative to regulating algae bloom. Microalgae are shown to produce bioactive compounds with a variety of biological activities that can be applied in various industries. This study evaluates the biochemical composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp. using the liquid triphasic partitioning (TPP) system. The findings from analytical assays revealed that the biomass consisted of varied concentrations of carbohydrates, protein, and lipids. Phenolic compounds and antioxidant activity were at 60.22 mg/L and 90.69%, respectively.
Plant leaf litter has a major role in the structure and function of soil ecosystems as it is associated with nutrient release and cycling. The present study is aimed to understand how well the decomposing leaf litter kept soil organic carbon and nitrogen levels stable during an incubation experiment that was carried out in a lab setting under controlled conditions and the results were compared to those from a natural plantation. In natural site soil samples, Anacardium. occidentale showed a higher value of organic carbon at surface (1.14%) and subsurface (0.93%) and Azadirachta. indica exhibited a higher value of total nitrogen at surface (0.28%) and subsurface sample (0.14%). In the incubation experiment, Acacia auriculiformis had the highest organic carbon content initially (5.26%), whereas A. occidentale had the highest nitrogen level on 30th day (0.67%). The overall carbon-nitrogen ratio showed a varied tendency, which may be due to dynamic changes in the complex decomposition cycle. The higher rate of mass loss and decay was observed in A. indica leaf litter, the range of the decay constant is 1.26-2.22. The morphological and chemical changes of soil sample and the vermicast were substantained using scanning electron microscopy (SEM) and Fourier transmission infrared spectroscopy (FT-IR).
Microplastics are the small fragments of the plastic molecules which find their applications in various routine products such as beauty products. Later, it was realized that it has several toxic effects on marine and terrestrial organisms. This review is an approach in understanding the microplastics, their origin, dispersal in the aquatic system, their biodegradation and factors affecting biodegradation. In addition, the paper discusses the major engineering approaches applied in microbial biotechnology. Specifically, it reviews microbial genetic engineering, such as PET-ase engineering, MHET-ase engineering, and immobilization approaches. Moreover, the major challenges associated with the plastic removal are presented by evaluating the recent reports available.
The exponential increase in the use and careless discard of synthetic plastics has created an alarming concern over the environmental health due to the detrimental effects of petroleum based synthetic polymeric compounds. Piling up of these plastic commodities on various ecological niches and entry of their fragmented parts into soil and water has clearly affected the quality of these ecosystems in the past few decades. Among the many constructive strategies developed to tackle this global issue, use of biopolymers like polyhydroxyalkanoates as sustainable alternatives for synthetic plastics has gained momentum. Despite their excellent material properties and significant biodegradability, polyhydroxyalkanoates still fails to compete with their synthetic counterparts majorly due to the high cost associated with their production and purification thereby limiting their commercialization. Usage of renewable feedstocks as substrates for polyhydroxyalkanoates production has been the thrust area of research to attain the sustainability tag. This review work attempts to provide insights about the recent developments in the production of polyhydroxyalkanoates using renewable feedstock along with various pretreatment methods used for substrate preparation for polyhydroxyalkanoates production. Further, the application of blends based on polyhydroxyalkanoates, and the challenges associated with the waste valorization based polyhydroxyalkanoates production strategy is elaborated in this review work.
The significance of integrating agricultural by-products such as paddy husk ash (PHA) and potato peels with organic fertilizers lies in enhancing soil fertility, increasing crop yields, and reducing reliance on traditional organic fertilizers like farmyard manure (FYM) or compost alone. Grounded in sustainable agriculture and nutrient management frameworks, this study examines the impact of diverse formulations derived from agricultural waste on productivity, nutrient efficiency, and profitability in a pigeon pea-vegetable mustard-okra cropping system. A two-year field experiment (2020-2022) at ICAR-IARI, New Delhi tested seven nutrient sources viz., (T1) control, (T2) 100% RDN through FYM, (T3) 100% RDN through improved RRC, (T4) 100% RDN through PHA based formulation, (T5) 75% RDN through PHA based formulation, (T6) 100% RDN through PPC based formulation and (T7) 75% RDN through PPC based formulation that were tested in RBD and replicated thrice. Treatment T4 had significant effect on seed yield of pigeon pea (1.89 ± 0.09 and 1.97 ± 0.12 t ha-1), leaf yield of vegetable mustard (81.57 ± 4.59 and 82.97 ± 4.17 t ha-1), and fruit yield of okra (13.54 ± 0.82 and 13.78 ± 0.81 t ha-1) grown in rotation, followed by treatment T6 and T2 during both the years respectively over control. Enhanced system uptake of N, P and K along with system gross and net returns in T4, showed increases of 78.9%, 83.8%, 72.4%, 54.4% and 56.8% in the first year and 77.5%, 80.8%, 77.7%, 54.8% and 57.4% in the second year, respectively, over control. Treatment T4 significantly improved apparent recovery by 66.3% and 69.2% in pigeon pea, 64.7% and 47.9% in vegetable mustard, and 72.7% and 79.4% in okra over T3, averaged across two years. Based on the above findings, (T4) 100% RDN through PHA-based formulation, and (T6) 100% RDN through PPC-based formulation can be recommended for areas with a shortage of FYM but availability of rice husk ash/potato peels for sustainable agricultural wastes and improved sustainability.
Randomly collected food waste results in inaccurate experimental data with poor reproducibility for composting. This study investigated standard food waste samples as replacements for randomly collected food waste. A response surface methodology was utilised to analyse data from a 28-day compost process optimisation experiment using collected food waste, and the optimal combination of composting parameters was derived. Experiments using different standard food waste samples (high oil and salt, high oil and sugar, balanced diet, and vegetarian) were conducted for 28 days under optimal conditions. The ranking of differences between the standard samples and collected food waste was vegetarian > balanced diet > high oil and sugar > high oil and salt. Statistical analysis indicated t-tests for increased oil and salt samples and collected food waste were not significant, and Cohen's d effect values were minimal. High oil and salt samples can be used as replacements for collected food waste in composting experiments.
Potassium, a pivotal macronutrient essential for growth, development, and crop yield, serves as a critical determinant of soil productivity. Its depletion disrupts the equilibrium of soil nutrients, prompting an investigation into integrated potassium management strategies to address this challenge. A field experiment was conducted during the winter season of 2020 using a randomized complete block design, with eight treatments, each replicated three times in Chinese cabbage (Brassica rapa L. subsp. chinensis). These treatments comprised standard (100 %) and reduced (75 % and 50 %) rates of the recommended dose of potassium (RDK) via muriate of potash (MOP). Variations in the inclusion and exclusion of plant growth-promoting rhizobacteria (PGPR), farmyard manure (FYM) as 25 % of the potassium recommendation, and foliar spray of nano potash were systematically implemented. Findings unequivocally demonstrated that the treatmentT8, involving 100 % RDK +25 % K through FYM + PGPR + nano K fertilizer spray at 25 and 40 DAS, yielded significant improvements in both green fodder (64.0 t ha-1) and dry fodder (7.87 t ha-1).Moreover, T8 exhibited the highest values for total ash (8.75 %), total ash yield (68.9 ± 2.88 kg ha-1), ether extract (2.85 %), ether extract yield (22.4 ± 0.88 kg ha-1), crude protein (9.71 %), and total crude protein yield (76.4 ± 3.21 kg ha-1). Conversely, a marked reduction was observed in various fiber components and carbohydrate fractions upon application of the T8 treatment. The lowest values of yield, crude protein content, total ash ether extract were recorded in treatment T1 (control) applied with no potassium. This investigation underscores the inadequacy of the recommended potassium dose in achieving optimal productivity, necessitating a re-evaluation of potassium fertilization levels. The integrated approach involving FYM, PGPR, and nano potash, coupled with the recommended potassium dose through MOP, emerges as a promising avenue for augmenting both yield and quality parameters in Chinese cabbage.