Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
The synergistic effects of antimicrobial nanostructures with antibiotics present a promising solution for overcoming resistance in methicillin-resistant Staphylococcus aureus (MRSA). Previous studies have introduced iron as a novel coating for silver nanoparticles (AgNPs) to enhance both economic efficiency and potency against S. aureus. However, there are currently no available data on the potential of these novel nanostructures to reverse MRSA resistance. To address this gap, a population study was conducted within the MRSA community, collecting a total of 48 S. aureus isolates from skin lesions. Among these, 21 isolates (43.75%) exhibited cefoxitin resistance as determined by agar disk diffusion assay. Subsequently, a PCR test confirmed the presence of the mecA gene in 20 isolates, verifying them as MRSA. These results highlight the cefoxitin disk diffusion susceptibility test as an accurate screening method for predicting mecA-mediated resistance in MRSA. Synergy tests were performed on cefoxitin, serving as a marker antibiotic, and iron-coated AgNPs (Fe@AgNPs) in a combination study using the checkerboard assay. The average minimal inhibitory concentration (MIC) and fractional inhibitory concentration (FIC) of cefoxitin were calculated as 11.55 mg/mL and 3.61 mg/mL, respectively. The findings indicated a synergistic effect (FIC index
Polyhydroxyalkanoates (PHAs) are biodegradable and biocompatible polyester which are biosynthesized from the intracellular cells of microalgae through the cultivation of organic food waste medium. Before cultivation process, food waste must undergo several pre-treatment techniques such as chemical, biological, physical or mechanical in order to solubilize complex food waste matter into simpler micro- and macronutrients in which allow bio-valorisation of microalgae and food waste compound during the cultivation process. This work reviews four microalgae genera namely Chlamydomonas, Chlorella, Spirulina, and Botryococcus, are selected as suitable species due to rapid growth rate, minimal nutrient requirement, greater adaptability and flexibility prior to lower the overall production cost and maximized the production of PHAs. This study also focuses on the different mode of cultivation for the accumulation of PHAs followed by cell wall destabilization, extraction, and purification. Nonetheless, this review provides future insights into enhancing the productivity of bioplastic derived from microalgae towards low-cost, large-scale, and higher productivity of PHAs.
The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
Microalgae have emerged as an effective dual strategy for bio-valorisation of food processing wastewater and food waste hydrolysate which favours microalgae cultivation into producing value-added by products mainly lipids, carbohydrates, and proteins to the advantages of bioplastic production. Moreover, various microalgae have successfully removed high amount of organic pollutants from food processing wastewater prior discharging into the environment. Innovation of microalgae cultivating in food processing wastewater greatly reduced the cost of wastewater treatment compared to conventional approach in terms of lower carbon emissions, energy consumption, and chemical usage while producing microalgae biomass which can benefit low-cost fertilizer and bioplastic applications. The study on several microalgae species has all successfully grown on food waste hydrolysates showing high exponential growth rate and biomass production rich in proteins, lipids, carbohydrates, and fatty acids. Multiple techniques have been implemented for the extraction of food wastes to be incorporate into the bioplastic production.