Assessment of physiological responses and growth phases of different microalgae under environmental changes by Raman spectroscopy with chemometrics

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Label-free and noninvasive analysis of microorganism surface epistructures at the single-cell level

Cell surface properties of microorganisms provide abundant information for their physiological status and fate choice. However, current methods for analyzing cell surface properties require labeling or fixation, which can alter the cell activity. This study establishes a label-free, rapid, noninvasive, and quantitative analysis of cell surface properties, including the presence and the dimension of epistructure, down to the single-cell level and at the nanometer scale. Simultaneously, electrorotation provides dielectric properties of intracellular contents. With the combined information, the growth phase of microalgae cells can be identified. The measurement is based on electrorotation of single cells, and an electrorotation model accounting for the surface properties is developed to properly interpret experimental data. The epistructure length measured by electrorotation is validated by scanning electron microscopy. The measurement accuracy is satisfactory in particular in the case of microscale epistructures in the exponential phase and nanoscale epistructures in the stationary phase. However, the measurement accuracy for nanoscale epistructures on cells in the exponential phase is offset by the effect of a thick double layer. Lastly, a diversity in epistructure length distinguishes exponential phase from stationary phase.

Smart systems in producing algae-based protein to improve functional food ingredients industries

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.

Response to nutrient variation on lipid productivity in green microalgae captured using second derivative FTIR and Raman spectroscopy

Two green microalgae species Monoraphidium contortum (M. contortum) and Chlamydomonas sp. that were identified to accumulate lipids were subjected to four different nutrient treatments (NP1-NP4), ranging in nitrate (0.05-5 mM N) and phosphate (2.8-264 μM P) concentrations, at a fixed N:P ratio of ∼18. The effect of nutrient variation on lipid productivity in the species was investigated using second derivative (SD) FTIR and Raman spectroscopy of algal biomass. SD spectral analysis revealed high production of lipid in the form of hydrocarbons (CH) (3000-2800 cm^-1), triacylglycerides (TAGs)(∼1740 cm^-1), saturated (SFA)(∼1440 cm^-1), and unsaturated fatty acids (UFA)(∼3010 cm^-1) for the nutrient deplete condition (NP1) in both species. Changes in signals attributed to lipids in proportion to other biochemical components were consistent with physiological changes expected from nutrient depletion. Relative signal intensities for lipids showed a significant increase in NP1, in particular, CH, TAGs in relation to protein signals (in SD-FTIR), and SFA, UFA in relation to carotenoid signals (in SD-Raman). PCA performed on the negative spectral values of the SD-FTIR and SD-Raman data for the four NP treatments enabled discrimination not only between the species but also between the NP treatments and the timing of harvest. M. contortum was found to contain a relatively higher proportion of CH, TAGs, SFA, and UFA compared to Chlamydomonas sp. Peak areas from the negative SD spectra, informed by PCA analysis, enabled capturing quantifiable changes in a manner that is consistent with known microalgal physiology. SD-FTIR and SD-Raman spectroscopy have been shown to possess superior potential to capture relevant microalgal physiological changes.

Smart microalgae farming with internet-of-things for sustainable agriculture

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.

Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization

Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes

Microalgae are among the most genetically and metabolically diverse organisms on earth, yet their identification and metabolic profiling have generally been slow and tedious. Here, we established a reference ramanome database consisting of single-cell Raman spectra (SCRS) from >9000 cells of 27 phylogenetically diverse microalgal species, each under stationary and exponential states. When combined, prequenching ("pigment spectrum" (PS)) and postquenching ("whole spectrum" (WS)) signals can classify species and states with 97% accuracy via ensemble machine learning. Moreover, the biosynthetic profile of Raman-sensitive metabolites was unveiled at single cells, and their interconversion was detected via intra-ramanome correlation analysis. Furthermore, not-yet-cultured cells from the environment were functionally characterized via PS and WS and then phylogenetically identified by Raman-activated sorting and sequencing. This PS-WS combined approach for rapidly identifying and metabolically profiling single cells, either cultured or uncultured, greatly accelerates the mining of microalgae and their products.

Raman spectroscopy applied to diatoms (microalgae, Bacillariophyta): Prospective use in the environmental diagnosis of freshwater ecosystems

Diatom species are good pollution bioindicators due to their large distribution, fast response to changes in environmental parameters and different tolerance ranges. These organisms are used in ecological water assessment all over the world using autoecological indices. Such assessments commonly rely on the taxonomic identification of diatom species-specific shape and frustule ornaments, from which cell counts, species richness and diversity indices can be estimated. Taxonomic identification is, however, time-consuming and requires years of expertise. Additionally, though the diatom autoecological indices are region-specific, they are often applied indiscriminately across regions. Raman spectroscopy is a simpler, fast and label-free technique that can be applied to environmental diagnosis with diatoms. However, this approach has been poorly explored. This work reviews Raman spectroscopy studies involving the structure, location and conformation of diatom cell components and their variation under different conditions. A critical appreciation of the pros and cons of its application to environmental diagnosis is also given. This knowledge provides a strong foundation for the development of environmental protocols using Raman spectroscopy in diatoms. Our work aims at stimulating further research on the application of Raman spectroscopy as a tool to assess physiological changes and water quality under a changing climate.

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning

A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm, respectively. We performed hyperspectral imaging (HSI) of three species of microalgae to verify their absorption characteristics. Transmission spectra were analyzed using principal component analysis (PCA) and peak ratio algorithms for dimensionality reduction and feature extraction, and a support vector machine (SVM) model was used for classification. The average accuracy, sensitivity and specificity to distinguish one species from the other two species were found to be 94.4%, 94.4% and 97.2%, respectively. A species identification experiment for a group of mixed microalgae in solution demonstrates the usability of the classification method. Using a random forest (RF) model, the growth stage in a phaeocystis growth cycle cultivated under laboratory conditions was predicted with an accuracy of 98.1%, indicating the feasibility to evaluate the growth state of microalgae through their transmission spectra. Experimental results show that the THMI system has the capability for classification, identification and growth stage estimation of microalgae, with strong potential for in-situ marine environmental monitoring and early warning detection applications.

Characterization of physiological states of the suspended marine microalgae using polarized light scattering

Physiological states of marine microalgal cells can influence photosynthesis efficiency, which affects approximately half of global carbon fixation. The detection of the algae physiological profiles is important for marine ecology and economy. In this paper, we propose a polarized light-scattering method to detect sensitive changes in the physiological states of the suspended marine microalgal cells. Our experimental setup is designed to measure the scattered polarization parameters of the cells suspended individually in the seawater. Two species of microalgal cells cultured in the laboratory were measured for several days. Experimental results showed that both species display distinctive changes in their polarized photon scattering features corresponding to changes in their physiological states. The changes are far more prominent than those displayed in unpolarized light scattering. Microscopy observations, simulations for microspheres of different diameters and refractive indices, or different shapes, indicated that the polarization features of the scattered photons are sensitive to the submicrometer microstructures of the cells. This study demonstrates the potential of the polarized light-scattering technique to characterize the physiological states of suspended marine microalgae.

Biomolecular transitions and lipid accumulation in green microalgae monitored by FTIR and Raman analysis

Fourier transform infrared (FTIR) and Raman spectroscopic techniques were employed to analyze the biomolecular transitions and lipid accumulation in three freshwater green microalgal species, Monoraphidium contortum (M. contortum), Pseudomuriella sp. and Chlamydomonas sp. during various phases of their growth. Biomolecular transitions and lipid [hydrocarbons, triacylglycerides (TAGs)] accumulation within the microalgal cells were identified using second derivatives of the FTIR absorption spectroscopy. Second derivative analysis normalized and resolved the original spectra and led to the identification of smaller, overlapping bands. Both relative and absolute content of lipids were determined using the integrated band area. M. contortum exhibited higher accumulation of lipids than the other two species. The integrated band area of the vibrations from saturated (SFA) and unsaturated lipids (UFA) enabled quantification of fatty acids. The percentage of SFA and UFA was determined using GC, FTIR and Raman spectroscopy. From the spectral data, the order of increasing concentration of SFA among the three microalgal species was M. contortum > Chlamydomonas sp. >Pseudomuriella sp. The spectral results on fatty acids were consistent with the separation of lipids by gas chromatography. The results emphasized the significance of FTIR and Raman spectroscopic methods in monitoring the biomolecular transitions and rapid quantification of lipids, without the need for extraction of lipids.

Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity

Microalgae are swift replicating photosynthetic microorganisms with several applications for food, chemicals, medicine and fuel. Microalgae have been identified to be suitable for biofuels production, due to their high lipid contents. Microalgae-based biofuels have the potential to meet the increasing energy demands and reduce greenhouse gas (GHG) emissions. However, the present state of technology does not economically support sustainable large-scale production. The biofuel production process comprises the upstream and downstream processing phases, with several uncertainties involved. This review examines the various production and processing stages, and considers the use of chemometric methods in identifying and understanding relationships from measured study parameters via statistical methods, across microalgae production stages. This approach enables collection of relevant information for system performance assessment. The principal benefit of such analysis is the identification of the key contributing factors, useful for decision makers to improve system design, operation and process economics. Chemometrics proffers options for time saving in data analysis, as well as efficient process optimization, which could be relevant for the continuous growth of the microalgae industry.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.