Algal biomass analysis by laser-based analytical techniques--a review

Fatty acid analysis in microalgal mono- and polycultures using diffuse reflectance infrared Fourier transform spectroscopy coupled with partial least squares analysis

Fatty acids are of particular interest for industrial applications of microalgal feedstock, as these have a wide array of different uses such as pharmaceuticals and biofuels. Fourier transform infrared (FTIR) spectroscopic techniques used in combination with multivariate prediction modeling are showing great potential as analytical methods for characterizing microalgal biomass. The present study investigated the use of diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) coupled with partial least squares regression (PLSR) to estimate fatty acid contents in microalgae. A prediction model for microalgal samples was developed using algae cultivated in both Bold's basal medium (BBM) and sterilized municipal wastewater under axenic conditions, as well as algal polycultures cultivated in open raceway ponds using untreated municipal wastewater influent. This universal prediction model was able to accurately predict microalgal samples of either type with high accuracy (RMSEP = 1.38, relative error = 0.14) and reliability (R2 > 0.92). DRIFTS in combination with PLSR is a rapid method for determining fatty acid contents in a wide variety of different microalgal samples with high accuracy. The use of spectral characterization techniques offers a reliable and environmentally friendly alternative to traditional labor intensive techniques based on the use of toxic chemicals.

Comparative Analysis of Laboratory-Based and Spectroscopic Methods Used to Estimate the Algal Density of Chlorella vulgaris

Chlorella vulgaris is of great importance in numerous exploratory or industrial applications (e.g., medicals, food, and feed additives). Rapid quantification of algal biomass is crucial in photobioreactors for the optimization of nutrient management and the estimation of production. The main goal of this study is to provide a simple, rapid, and not-resource-intensive estimation method for determining the algal density of C. vulgaris according to the measured parameters using UV-Vis spectrophotometry. Comparative assessment measurements were conducted with seven different methods (e.g., filtration, evaporation, chlorophyll a extraction, and detection of optical density and fluorescence) to determine algal biomass. By analyzing the entire spectra of diluted algae samples, optimal wavelengths were determined through a stepwise series of linear regression analyses by a novel correlation scanning method, facilitating accurate parameter estimation. Nonlinear formulas for spectrometry-based estimation processes were derived for each parameter. As a result, a general formula for biomass concentration estimation was developed, with recommendations for suitable measuring devices based on algae concentration levels. New values for magnesium content and the average single-cell weight of C. vulgaris were established, in addition to the development of a rapid, semiautomated cell counting method, improving efficiency and accuracy in algae quantification for cultivation and biotechnology applications.

Remote sensing oil in water with an all-fiber underwater single-photon Raman lidar

The detection of oil in water is of great importance for maintaining subsurface infrastructures such as oil pipelines. As a potential technology for oceanic application, an oceanic lidar has proved its advantages for remote sensing of optical properties and subsea materials. However, current oceanic lidar systems are highly power-consuming and bulky, making them difficult to deploy underwater to monitor oil in water. To address this issue, we have developed a compact single-photon Raman lidar by using a single-photon detector with high quantum efficiency and low dark noise. Due to the single-photon sensitivity, the detection of the relatively weak Raman backscattered signal from underwater oil was realized with a laser with a pulse energy of 1 µJ and a telescope with a diameter of 22.4 mm. An experimental demonstration was conducted to obtain the distance-resolved Raman backscatter of underwater oil of different thicknesses up to a distance of 12 m. The results indicate the single-photon Raman lidar's potential for inspecting underwater oil pipelines.

Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning

To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.

Vibrational Spectroscopy as a Tool for Bioanalytical and Biomonitoring Studies

The review briefly describes various types of infrared (IR) and Raman spectroscopy methods. At the beginning of the review, the basic concepts of biological methods of environmental monitoring, namely bioanalytical and biomonitoring methods, are briefly considered. The main part of the review describes the basic principles and concepts of vibration spectroscopy and microspectrophotometry, in particular IR spectroscopy, mid- and near-IR spectroscopy, IR microspectroscopy, Raman spectroscopy, resonance Raman spectroscopy, Surface-enhanced Raman spectroscopy, and Raman microscopy. Examples of the use of various methods of vibration spectroscopy for the study of biological samples, especially in the context of environmental monitoring, are given. Based on the described results, the authors conclude that the near-IR spectroscopy-based methods are the most convenient for environmental studies, and the relevance of the use of IR and Raman spectroscopy in environmental monitoring will increase with time.

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

In-vitro study on the identification of gastrointestinal stromal tumor tissues using laser-induced breakdown spectroscopy with chemometric methods

Early-stage detection of tumors helps to improve patient survival rate. In this work, we demonstrate a novel discrimination method to diagnose the gastrointestinal stromal tumor (GIST) and its healthy formalin fixed paraffin embedded (FFPE) tissues by combining chemometric algorithms with laser-induced breakdown spectroscopy (LIBS). Chemometric methods which include partial least square discrimination analysis (PLS-DA), k-nearest neighbor (k-NN) and support vector machine (SVM) were used to build the discrimination models. The comparison of PLS-DA, k-NN and SVM classifiers shows an increase in accuracy from 94.44% to 100%. The comparison of LIBS signal between the healthy and infected tissues shows an enhancement of calcium lines which is a signature of the presence of GIST in the FFPE tissues. Our results may provide a complementary method for the rapid detection of tumors for the successful treatment of patients.

Microfluidics for microalgal biotechnology

Microalgae have expanded their roles as renewable and sustainable feedstocks for biofuel, smart nutrition, biopharmaceutical, cosmeceutical, biosensing, and space technologies. They accumulate valuable biochemical compounds from protein, carbohydrate, and lipid groups, including pigments and carotenoids. Microalgal biomass, which can be adopted for multivalorization under biorefinery settings, allows not only the production of various biofuels but also other value-added biotechnological products. However, state-of-the-art technologies are required to optimize yield, quality, and the economical aspects of both upstream and downstream processes. As such, the need to use microfluidic-based devices for both fundamental research and industrial applications of microalgae, arises due to their microscale sizes and dilute cultures. Microfluidics-based devices are superior to their competitors through their ability to perform multiple functions such as sorting and analyzing small amounts of samples (nanoliter to picoliter) with higher sensitivities. Here, we review emerging applications of microfluidic technologies on microalgal processes in cell sorting, cultivation, harvesting, and applications in biofuels, biosensing, drug delivery, and nutrition.

A multi-spot laser induced breakdown spectroscopy system based on diffraction beam splitter

A quick simultaneous multispot laser induced breakdown spectroscopy (LIBS) system has been proposed. The basic idea is to combine the Diffraction Beam Splitter (DBS) with the linear-to-linear fiber bundle. The DBS divided the incident laser beam into five sub-beams, and then, a lens focused the incident sub-beam to produce a plasma array, where the distance between the neighbor subplasma was constant and the plasma emission was imaged on the fiber bundle. Each ablated spot on the sample generated the corresponding spectroscopy signals, which were collected by the defined fibers of the fiber bundle, propagated to the spectrograph slit, and then analyzed by the intensified charge-coupled device (ICCD) detector, where the two-dimensional capability of the charge-coupled device detectors was explored for the spectroscopy and position analyses. The five spectroscopy ribbon presented on the ICCD corresponded to the plasma radiation of the five ablated spots. The feasibility of a simultaneous multipoint spectroscopy detection at a single pulse ablation was confirmed by subjecting to the spectroscopy analyses of a copper plane, layered material, and magnesium-molybdenum mixture. The multispot LIBS system can be used in the ejecta research of detonation physics, fluid physics, and so on.

Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy

Fast detection of toxic metals in crops is important for monitoring pollution and ensuring food safety. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the chromium content in rice leaves. We investigated the influence of laser wavelength (532 nm and 1064 nm excitation), along with the variations of delay time, pulse energy, and lens-to-sample distance (LTSD), on the signal (sensitivity and stability) and plasma features (temperature and electron density). With the optimized experimental parameters, univariate analysis was used for quantifying the chromium content, and several preprocessing methods (including background normalization, area normalization, multiplicative scatter correction (MSC) transformation and standardized normal variate (SNV) transformation were used to further improve the analytical performance. The results indicated that 532 nm excitation showed better sensitivity than 1064 nm excitation, with a detection limit around two times lower. However, the prediction accuracy for both excitation wavelengths was similar. The best result, with a correlation coefficient of 0.9849, root-mean-square error of 3.89 mg/kg and detection limit of 2.72 mg/kg, was obtained using the SNV transformed signal (Cr I 425.43 nm) induced by 532 nm excitation. The results indicate the inspiring capability of LIBS for toxic metals detection in plant materials.

Simultaneous analysis of Cr and Pb in contaminated pork by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) as a rapid and green method was used to detect heavy metals Cr and Pb in pork contaminated in the lab. The laser-induced plasma was generated by a Q-switched Nd:YAG laser, and the LIBS signal was collected by a spectrometer with a charge-coupled device detector. The traditional calibration curves (CC) and multivariate partial least squares (PLS) algorithm were applied and compared to validate the accuracy in predicting the content of heavy metals in samples. The results demonstrated that the correlation coefficient of CC is poor by the classical univariate calibration method, so the univariate calibration analysis cannot effectively serve the quantitative purpose in analyzing heavy metals' residue in pork with a complex matrix. The analysis accuracy was improved effectively by the PLS method, and the correlation coefficient is 0.9894 for Cr and 0.9908 for Pb. The concentration of Cr and Pb in samples from a prediction set was obtained using the PLS calibration method, and the average relative errors for the 21 samples in the prediction set are lower than 6.53% and 7.82% for Cr and Pb, respectively. The investigated results display that the matrix effect would be reduced effectively during the quantitative analysis of pork by a LIBS-combined PLS model, and the predictive accuracy would be improved greatly compared to traditional univariate analysis.

Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis

In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

Application of picosecond laser-induced breakdown spectroscopy to quantitative analysis of boron in meatballs and other biological samples

This report presents the results of laser-induced breakdown spectroscopy (LIBS) study on biological and food samples of high water content using a picosecond (ps) laser at low output energy of 10 mJ and low-pressure helium ambient gas at 2 kPa. Evidence of excellent emission spectra of various analyte elements with very low background is demonstrated for a variety of samples without the need of sample pretreatment. Specifically, limits of detection in the range of sub-ppm are obtained for hazardous Pb and B impurities in carrots and meatballs. This study also shows the inferior performance of LIBS using a nanosecond laser and atmospheric ambient air for a soft sample of high water content and thereby explains its less successful applications in previous attempts. The present result has instead demonstrated the feasibility and favorable results of employing LIBS with a ps laser and low-pressure helium ambient gas as a less costly and more practical alternative to inductively coupled plasma for regular high sensitive inspection of harmful food preservatives and environmental pollutants.

Optimization of liquid jet system for laser-induced breakdown spectroscopy analysis

A complex optimization of geometrical and temporal parameters of a jet system (developed in Laser-induced breakdown spectroscopy (LIBS) laboratory of Brno University of Technology) for direct elemental analysis of samples in a liquid state of matter using LIBS was carried out. First, the peristaltic pump was synchronized with the flashlamp of the ablation laser, which reduced variation of the ablated sample amount. Also, the fluctuation of the laser ray angle incident on the jet surface was diminished. Such synchronization reduced signal standard deviations and thus increased repeatability of the measurements. Then, laser energy and distance of the focusing lens from the sample were optimized. The gate delay time and the gate width were optimized for single pulse (SP) experiments; the gate delay time and the inter-pulse delay were optimized for the use of double pulse (DP) variant. Results were assessed according to the highest signal to noise ratios and the lowest relative standard deviations of the signal. The sensitivity of the single pulse and the double pulse LIBS for the detection of heavy metals traces, copper (Cu i at 324.754 nm) and lead (Pb i at 405.781 nm), in aqueous solution of copper (ii) sulfate and lead (ii) acetate, was estimated in terms of limits of detection (LODs). As a result, sensitivity improvement of DP LIBS system was observed, the LOD of Cu obtained with DP was calculated 40% lower than LOD gained from SP technique.

Live diatoms facing Ag nanoparticles: surface enhanced Raman scattering of bulk cylindrotheca closterium pennate diatoms and of the single cells

Live diatoms exposed to AgNPs revealed SERS mechanism dependent on the nanoparticles type while the SERS output allowed detection of extracellular substances.

An integrative Raman microscopy-based workflow for rapid in situ analysis of microalgal lipid bodies

BACKGROUND

Oils and bioproducts extracted from cultivated algae can be used as sustainable feedstock for fuels, nutritional supplements, and other bio-based products. Discovery and isolation of new algal species and their subsequent optimization are needed to achieve economical feasibility for industrial applications. Here we describe and validate a workflow for in situ analysis of algal lipids through confocal Raman microscopy. We demonstrate its effectiveness to characterize lipid content of algal strains isolated from the environment as well as algal cells screened for increased lipid accumulation through UV mutagenesis combined with Fluorescence Activated Cell Sorting (FACS).

RESULTS

To establish and validate our workflow, we refined an existing Raman platform to obtain better discrimination in chain length and saturation of lipids through ratiometric analyses of mixed fatty acid lipid standards. Raman experiments were performed using two different excitation lasers (λ = 532 and 785 nm), with close agreement observed between values obtained using each laser. Liquid chromatography coupled with mass spectrometry (LC-MS) experiments validated the obtained Raman spectroscopic results. To demonstrate the utility and effectiveness of the improved Raman platform, we carried out bioprospecting for algal species from soil and marine environments in both temperate and subtropical geographies to obtain algal isolates from varied environments. Further, we carried out two rounds of mutagenesis screens on the green algal model species, Chlamydomonas reinhardtii, to obtain cells with increased lipid content. Analyses on both environmental isolates and screened cells were conducted which determined their respective lipids. Different saturation states among the isolates as well as the screened C. reinhardtii strains were observed. The latter indicated the presence of cell-to cell variations among cells grown under identical condition. In contrast, non-mutagenized C. reinhardtii cells showed no significant heterogeneity in lipid content.

CONCLUSIONS

We demonstrate the utility of confocal Raman microscopy for lipid analysis on novel aquatic and soil microalgal isolates and for characterization of lipid-expressing cells obtained in a mutagenesis screen. Raman microscopy enables quantitative determination of the unsaturation level and chain lengths of microalgal lipids, which are key parameters in selection and engineering of microalgae for optimal production of biofuels.

Comparison of the detection characteristics of trace species using laser-induced breakdown spectroscopy and laser breakdown time-of-flight mass spectrometry

The rapid and precise element measurement of trace species, such as mercury, iodine, strontium, cesium, etc. is imperative for various applications, especially for industrial needs. The elements mercury and iodine were measured by two detection methods for comparison of the corresponding detection features. A laser beam was focused to induce plasma. Emission and ion signals were detected using laser-induced breakdown spectroscopy (LIBS) and laser breakdown time-of-flight mass spectrometry (LB-TOFMS). Multi-photon ionization and electron impact ionization in the plasma generation process can be controlled by the pressure and pulse width. The effect of electron impact ionization on continuum emission, coexisting molecular and atomic emissions became weakened in low pressure condition. When the pressure was less than 1 Pa, the plasma was induced by laser dissociation and multi-photon ionization in LB-TOFMS. According to the experimental results, the detection limits of mercury and iodine in N₂ were 3.5 ppb and 60 ppb using low pressure LIBS. The mercury and iodine detection limits using LB-TOFMS were 1.2 ppb and 9.0 ppb, which were enhanced due to different detection features. The detection systems of LIBS and LB-TOFMS can be selected depending on the condition of each application.

Monitoring of Microalgal Processes

Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

Rapid elemental analysis and provenance study of Blumea balsamifera DC using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera.