Spectra Of Biological Samples Research Articles

Plastic spectral interference in the biological characterization by Raman or SERS spectroscopy

AbstractRaman spectroscopy and surface‐enhanced Raman spectroscopy (SERS) have been widely applied for characterizing biological samples. Nowadays, it is routine to use plastic products for biological sample preparation and analysis during spectral acquisition. However, plastic containers produce undesirable signals severely interfering with the spectra of biological samples, which is neglectable and misleading. Herein, we unveiled the physical and chemical interference of plastic products in the process of Raman spectroscopy characterization. Firstly, we revisited a representative case of interference from polystyrene plastics on normal Raman and SERS spectra of cysteine and demonstrated its physical interference due to misfocusing. Furthermore, we testified SERS spectra of aqueous solution stored in several plastic materials commonly used in biological experiments. The spectra indicate strong spectral interference from the leaching chemicals. To address these problems, we proposed a series of strategies to avoid interference from plastic products in the Raman and SERS characterization of biological samples.

Optimized preprocessing and machine learning for quantitative Raman spectroscopy in biology

AbstractRaman spectroscopy's capability to provide meaningful composition predictions is heavily reliant on a preprocessing step to remove insignificant spectral variation. This is crucial in biofluid analysis. Widespread adoption of diagnostics using Raman requires a robust model that can withstand routine spectra discrepancies due to unavoidable variations such as age, diet, and medical background. A wealth of preprocessing methods are available, and it is often up to trial‐and‐error or user experience to select the method that gives the best results. This process can be incredibly time consuming and inconsistent for multiple operators. In this study, we detail a method to analyze the statistical variability within a set of training spectra and determine suitability to form a robust model. This allows us to selectively qualify or exclude a preprocessing method, predetermine robustness, and simultaneously identify the number of components that will form the best predictive model. We demonstrate the ability of this technique to improve predictive models of two artificial biological fluids. Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routine health monitoring that maximizes comfort is increasingly crucial, particularly in epidemic‐level diabetes diagnoses. High variability in spectra of biological samples can hinder Raman's adoption for these methods. Our technique allows the decision of optimal pretreatment method to be determined for the operator; model performance is no longer a function of user experience. We foresee this statistical technique being an instrumental element to widening the adoption of Raman as a monitoring tool in a field of biofluid analysis.

Quantum dots photodetector and micro-area spectra application

ABSTRACTIn this paper, the authors mainly studies on optical and electrical characteristics of 64 pixels quantum dots photodetector, and explore the application of micro-spectrometer as well. The photodetector has AlAs double barrier structures with a layer self-assembled InAs quantum dots and In0.15Ga0.85 As quantum well. The capture of photo-excited carriers on InAs quantum dots and In0.15Ga0.85As quantum well enable local modulation of the electrostatic potential, which help increase the photodetector sensitivity. The photocurrent responsivity is about 5.69E7 A/W with 0.01 picowatts power He-Ne laser illumination and -3 V bias voltage at liquid nitrogen temperature, wherein the internal gain of the photodetector is even higher than 1.11E8. We have designed readout circuits and an android-based micro-spectrometer based on quantum dots photodetector. In order to study micro-area spectra of biological samples, the mico-spectrometer has been mounted in a microscopy hyperspectral imaging system with C-Mount optical path switcher. The experimental results show that the red blood cell transmittance spectra obtained by the two spectral measurement systems are well coincided, and the sensitivity of quantum dot micro-spectrometer is much higher than the commercial CCD spectrometer.

Application of 19F NMR Spectroscopy Using a Novel a-Tocopherol Derivative as a 19F NMR Probe for a Pharmacokinetic Study of Lipid Nano-Emulsions in Mice

Objective: The aim of our study was to demonstrate the usefulness of 19F nuclear magnetic resonance (NMR) spectroscopy as an analytical technique applicable for the pharmacokinetic studies of lipid nano-emulsions (LNEs) using a mixture of soybean oil, phosphatidylcholine and sodium palmitate as drug carriers. Methods: A α−tocopherol derivative, 19F-TP, in which a 4-(trifluoromethyl) benzoyl group was introduced to the hydroxyl group of α-tocopherol was newly synthesized as a 19F NMR probe. Three different LNEs containing 19F-TP, denoted 19F-TP-LNEs (Small-LNE, Large-LNE, and polyethylene glycol-modified LNE (PEG-LNE)) were prepared by the sonication method and characterized using a dynamic light-scattering method and zeta potential analysis. The concentrations of the three 19F-TP-LNEs in the blood, liver and kidneys of mice were periodically evaluated based on the 19F NMR signal intensity ratio of 19F-TP using 0.1 mM of trifluoromethane sulfonic acid sodium salt as an internal reference. Results: 19F-TP was easily synthesized with a high yield of 96% in a one-step procedure. Small-LNE, Large- LNE and PEG-LNE had the mean particle sizes of 58, 157 and 174 nm and zeta potentials of –34, –53 and –32 mV, respectively. A single signal attributable to 19F-TP in 19F-TP-LNEs was observed at 15.4 ppm in the 19F NMR spectra of biological samples, but was observed to decrease over time. From the change of 19F NMR signal of 19F-TP in biological samples, it was shown that three 19F-TP-LNEs had different pharmacokinetic characteristics because of their droplet sizes and surface physical properties. Conclusion: Based on these results, the 19F NMR method was confirmed to be a convenient and useful tool for assessing the pharmacokinetics of LNEs without the need for complicated pretreatment procedures such as the deproteination of the matrix and extraction of the target compound before the 19F NMR measurements.

Ascertainment of the number of samples in the validation set in Monte Carlo cross validation and the selection of model dimension with Monte Carlo cross validation

Monte Carlo cross validation (MCCV) is used in two data sets including 125 and 1643 near-infrared (NIR) spectra of biological samples, respectively, to ascertain the number of samples left out for validation in MCCV and the dimension of PLS models consequently. With the selected number of samples in validation set, the suitable number of latent variables (LV) may be chosen correctly. The results obtained show that root mean squared error of calibration (RMSEC), root mean squared error of cross validation (RMSECV) and LV number are sensitive to the number of samples left out for validation when too many samples are left out. Based on this, RMSEC and RMSECV are suggested as criteria to assist the ascertainment of the number of samples left out for validation in MCCV. This method is easy and convenient to use. For a larger data set, more samples may be left out, but the suitable number of samples left out will decrease if the measurement error level is high.

On-Probe Sample Preparation without Washes for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Using an Anion Exchange Medium

When the mass spectra of biological samples (proteins, peptides, and so on) are obtained routinely by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), a serious problem is the reduction of the ionization efficiency by impurities, such as buffer salts and detergents. We focused our attention on devising a method to maintain the ionization efficiency of protein samples, even in the presence of sodium dodecyl sulfate (SDS), without any extra purification step. Although no protein ion peaks are observed in the presence of 2.5% SDS with the usual methods, the addition of a granular anion exchange silica gel to the matrix solution allowed the protein ion peaks to be obtained with an excellent signal-to-noise ratio. Together with other supporting experiments, we suggest that the positively charged surface (the basic environment derived from the anion exchange groups) and the roughness of the particles were important for good ionization in the presence of a high SDS concentration. For a very uneven surface, the SDS might be absorbed into the particle interiors during the process of cocrystallization with the matrix and analytes, which is known as the molecular sieve effect, and the SDS concentration in the surface crystalline film might be reduced. As a result, we developed an on-probe sample preparation method without washes for MALDI, using a strong anion exchange silica gel. This method is applicable even in the presence of 2.5% SDS, and is not only very simple but also inexpensive, because it can be used with the standard MALDI target plates.

Use of relaxation-edited one-dimensional and two dimensional nuclear magnetic resonance spectroscopy to improve detection of small metabolites in blood plasma

The 1H nuclear magnetic resonance (NMR) spectra of biological samples, such as blood plasma and tissues, are information rich but data complex owing to superposition of the resonances from a multitude of different chemical entities in multiple-phase compartments, hampering detection and subsequent resonance assignments. To overcome these problems, several spectral-editing NMR experiments are described here, combining spin-relaxation filters (based on T 1, T ρ, and T 2) with both one-dimensional and two-dimensional (2D) NMR spectroscopy. These techniques enable the separation of NMR resonances based on their relaxation times and allow simplification of the complex spectra. In this paper, the approach is exemplified using a control human blood plasma, which is a complex mixture of proteins, lipoproteins, and small-molecule metabolites. In the case of T 1ρ- and T 2-edited 2D NMR experiments, a “flip-back” pulse was introduced after the relaxation editing to make the phase cycling of the “relaxation filter” and the 2D NMR part independent, thus enabling easy implementation of the phase-sensitive 2D NMR experiments. These methods also permit much higher receiver gains to be used to reduce digitization error, in particular, for the small resonances, which are sometimes vitally important for metabonomics studies. Both pulse sequences and experimental results are discussed for T 1-, T 1ρ-, and T 2-filtered COSY, T 2-filtered phase-sensitive DQF-COSY, and T 1, T 1ρ-, and T 2-filtered TOCSY NMR.

Indirect K+ Ions Determination from Infrared Spectra of Biological Samples Owing to Their Interactions with Biomolecules

Complex-formation between carbohydrates and cations may have important biological implications. Potassium ions and sucrose interactions have been observed by the examination of the spectral patterns obtained by multi-dimensionnal analysis (Principal Component Analysis) applied to infrared spectra of biological samples. Several authors have extensively studied ions-sugars interactions particularly calcium and magnesium-sugar interactions and have shown that these interactions induced shifts and splitting in infrared absorption bands. We here report to use Principal Component Analysis and Principal Component Regression for the quantitative determination of K+ ions in biological solutions by investigating interactions between this cation and sucrose molecules. The spectral pattern that describes the principal component (axis 1), features absorption peaks at 925, 997, 1053, 1112 and 1136 cm-1 that are characteristic of sucrose. The correlation coefficient between axis 1, representative of pure sucrose, and the values of potassium concentrations is 0. 5213. The spectral pattern of the second axis is also associated with the sucrose pattern. The shifts and splittings of the absorption bands that are observed in this second spectral pattern are associated with potassium-sucrose interactions. The correlation coefficient between this second axis (axis 3) and the K+ concentration is equal to 0. 2152. Most of the information relative to potassium ions are contained in this axis. These informations appear as interactions between sucrose and the cation. With the first two axes, the correlation coefficient reaches 0. 7365 and this opens the possibility of quantifying K+ in biological samples. The other axes improved the correlation coefficient, which reached 0.9945 with the first ten axes. For the predicted concentrations of K+, the bias and standard deviation values, 8 × 10-2 and 0.43 respectively, showed that the predicted values are very close to those determined by flame photometry. Hence, even if K+ cation does not have a Mid-infrared spectral fingerprint, interactions between K+ and sucrose in raw sugar cane juices allow the indirect quantitative determination of this ion.

Classification of pyrolysis mass spectra of biological materials using convex cones

AbstractThis work addresses the classification of high‐dimensional time‐dependent pyrolysis mass spectra of biological samples. The aim was the detection and classification of biological agents, and the developed approach resembles mixture analysis. The data were projected on to a low‐dimensional subspace using singular value decomposition. Then a convex cone was formed on this subspace, showing as its corners physically meaningful components of the sample. This technique enabled separation of a biological material signal largely independent of the absolute amount of sample. The detection of the presence of any biological material could be accomplished based on the convex cone alone, without other reference to the mass spectra. Automated clustering of samples was successfully carried out using a minimal spanning tree.