Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.
Microalgae are the most promising sources of protein, which have high potential due to their high-value protein content. Conventional methods of protein harnessing required multiple steps, and they are generally complex, time consuming, and expensive. Currently, the study of integration methods for microalgae cell disruption and protein recovery process as a single-step process is attracting considerable interest. This study aims to investigate the novel approach of integration method of electrolysis and liquid biphasic flotation for protein extraction from wet biomass of Chlorella sorokiniana CY-1 and obtaining the optimal operating conditions for the protein extraction. The optimized conditions were found at 60% (v/v) of 1-propanol as top phase, 250 g/L of dipotassium hydrogen phosphate as bottom phase, crude microalgae loading of 0.1 g, air flowrate of 150 cc/min, flotation time of 10 min, voltage of 20 V and electrode's tip touching the top phase of LBEF. The protein recovery and separation efficiency after optimization were 23.4106 ± 1.2514% and 173.0870 ± 4.4752%, respectively. Comparison for LBEF with and without the aid of electric supply was also conducted, and it was found that with the aid of electrolysis, the protein recovery and separation efficiency increased compared to the LBEF without electrolysis. This novel approach minimizes the steps for overall protein recovery from microalgae, time consumption, and cost of operation, which is beneficial in bioprocessing industry.
Analyses of the mitochondrial cox1, the nuclear-encoded large subunit (LSU), and the internal transcribed spacer 2 (ITS2) RNA coding region of Pseudo-nitzschia revealed that the P. pseudodelicatissima complex can be phylogenetically grouped into three distinct clades (Groups I-III), while the P. delicatissima complex forms another distinct clade (Group IV) in both the LSU and ITS2 phylogenetic trees. It was elucidated that comprehensive taxon sampling (sampling of sequences), selection of appropriate target genes and outgroup, and alignment strategies influenced the phylogenetic accuracy. Based on the genetic divergence, ITS2 resulted in the most resolved trees, followed by cox1 and LSU. The morphological characters available for Pseudo-nitzschia, although limited in number, were overall in agreement with the phylogenies when mapped onto the ITS2 tree. Information on the presence/absence of a central nodule, number of rows of poroids in each stria, and of sectors dividing the poroids mapped onto the ITS2 tree revealed the evolution of the recently diverged species. The morphologically based species complexes showed evolutionary relevance in agreement with molecular phylogeny inferred from ITS2 sequence-structure data. The data set of the hypervariable region of ITS2 improved the phylogenetic inference compared to the cox1 and LSU data sets. The taxonomic status of P. cuspidata and P. pseudodelicatissima requires further elucidation.