Determination of oxygen saturation and hematocrit of flowing human blood using two different spectrally resolving sensors.

The blood parameters oxygen saturation and hematocrit were determined by two different spectral sensors using reflectance spectra from 550 to 900 nm and partial transmission spectra centered at 660 nm. The spectra were analyzed by the method of partial least squares. One sensor consists of a miniature integrating sphere, while the other was fiber-guided. The results show that the geometry of the sensors and different blood flows do not influence the spectral analysis significantly. Independent of the sensor geometry, both hematocrit and oxygen saturation could be determined with an absolute predicted root mean square error of less than 3%. Furthermore, the analysis showed that hematocrit prediction requires eight wavelength regions and oxygen saturation prediction requires four wavelength regions using reflectance spectroscopy. This implies that if the measurement is restricted to reflectance, a spectrometer is indispensable for determining both blood parameters. Hematocrit determination could be improved using reflectance measurements in combination with transmission.     

Determination of total protein in hyperimmune serum samples by near-infrared spectrometry and multivariate calibration

In the present work, the determination of the total protein concentration in hyperimmune serum samples was performed through a partial least-squares near-infrared (NIR-PLS) method. The method was based on the chemometric treatment of the NIR spectra of samples. The influences of spectra preprocessing and spectral window utilized in the construction of PLS model were studied. Models were built using reference data of 19 samples selected through the use of hierarchical cluster analysis (HCA) of NIR spectra of samples and another 24 samples were employed for external validation of the method. A model with better prediction capacity was obtained after whole spectra preprocessing by multiplicative scattering correction (MSC) algorithm and using data in the spectral range of 2158-2209 nm. Under optimized conditions a RMSEP of 0.21 g dl⁻¹ and a quality coefficient value (QC) of only 5.8% were obtained for the prediction of total protein content in the samples used for external validation. Also, a determination coefficient, r², of 0.97 was obtained in the correlation of predicted and reference data of samples situated in the validation set.     

Quantitation of drug content in a low dosage formulation by transmission near infrared spectroscopy

A transmission near infrared (NIR) spectroscopic method has been developed for the nondestructive determination of drug content in tablets with less than 1% weight of active ingredient per weight of formulation (m/m) drug content. Tablets were manufactured with drug concentrations of ∼0.5%, 0.7%, and 1.0% (m/m) and ranging in drug content from 0.71 to 2.51 mg per tablet. Transmission NIR spectra were obtained for 110 tablets that constituted the training set for the calibration model developed with partial least squares regression. The reference method for the calibration model was a validated UV spectrophotometric method. Several data preprocessing methods were used to reduce the effect of scattering on the NIR spectra and base the calibration model on spectral changes related to the drug concentration changes. The final calibration model included the spectral range from 11 216 to 8662 cm⁻¹ the standard normal variate (SNV), and first derivative spectral pretreatments. This model was used to predict an independent set of 48 tablets with a root mean standard error of prediction (RMSEP) of 0.14 mg, and a bias of only −0.05 mg per tablet. The study showed that transmission NIR spectroscopy is a viable alternative for nondestructive testing of low drug content tablets, available for the analysis of large numbers of tablets during process development and as a tool to detect drug agglomeration and evaluate process improvement efforts. Published: March 24, 2006     

Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens

MOTIVATION: Application of mass spectrometry in proteomics is a breakthrough in high-throughput analyses. Early applications have focused on protein expression profiles to differentiate among various types of tissue samples (e.g. normal versus tumor). Here our goal is to use mass spectra to differentiate bacterial species using whole-organism samples. The raw spectra are similar to spectra of tissue samples, raising some of the same statistical issues (e.g. non-uniform baselines and higher noise associated with higher baseline), but are substantially noisier. As a result, new preprocessing procedures are required before these spectra can be used for statistical classification. RESULTS: In this study, we introduce novel preprocessing steps that can be used with any mass spectra. These comprise a standardization step and a denoising step. The noise level for each spectrum is determined using only data from that spectrum. Only spectral features that exceed a threshold defined by the noise level are subsequently used for classification. Using this approach, we trained the Random Forest program to classify 240 mass spectra into four bacterial types. The method resulted in zero prediction errors in the training samples and in two test datasets having 240 and 300 spectra, respectively.     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.     

Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis. Published: October 6, 2005 The views presented in this article do not necessarily reflect those of the Food and Drug Administration.     

Collagen concentrations in dissected tissue compartments of rat uterus on days 6, 7 and 8 of pregnancy.

Implantation and non-implantation sites were dissected into myometrial and stromal components; a decidual/embryonic region was obtained on Days 7 and 8 of pregnancy. The concentration of collagen (as a percentage of the dry weight of tissue), measured by hydroxyproline analysis, was significantly lower in the implantation regions than in the non-implant regions in all areas studied. The concentrations in the antimesometrial myometrium and stroma of the implantation region remained the same over the days studied. In contrast, the mesometrial collagen concentration in the implantation region declined from Day 6 to Day 8 of pregnancy. Collagen concentration was low within the decidual/embryonic tissue on Days 7 and 8 of pregnancy. Remodelling of collagen within the embryonic area appears to be an important feature of the uterine response to implantation in rats.     

Automatic amide I frequency selection for rapid quantification of protein secondary structure from Fourier transform infrared spectra of proteins

Here we report the development of a new neural network based approach for rapid quantification of protein secondary structure from Fourier transform infrared (FTIR) spectra of proteins. A technique for efficiently reducing the amount of spectral data by almost 90% is suggested to facilitate faster neural network analysis. Additionally, an automatic procedure is introduced for selecting only those regions within the amide I band of protein FTIR spectra, which can be best related to secondary structure contents by subsequent neural network analysis. Based on a given reference set of FTIR spectra from proteins with known secondary structure, a subset of merely 29 out of 101 amide I absorbance values could be identified, which lead to an improved prediction accuracy. The average prediction accuracy achieved for helix, sheet, turn, bend, and other is 4.96% which is better than that achieved by alternative methods that have been previously reported indicating the significant potential of this approach. Our suggested automatic amide I frequency selection procedure may be easily extended to identify promising regions from spectral data recorded by other spectroscopic techniques, like for example circular dichroism spectroscopy.     

Quantitative determination of cellulose dissolved in 1-ethyl-3-methylimidazolium acetate using partial least squares regression on FTIR spectra

Rapid and quantitative measurements of cellulose concentrations in ionic liquids (ILs) are difficult. In this study, FTIR operated in attenuated total reflectance (ATR) mode was investigated as a tool to measure cellulose concentration in 1-ethyl-3-methylimidazolium acetate ([emim][OAc]) and the spectra were subjected to partial least squares (PLS) regression for the quantitative determination of cellulose content. Additionally, the spectra were subjected to 7 data preprocessing methods to reduce physical effects in the spectra. Peak normalization was found to be the technique that most improved the prediction of dissolved cellulose in [emim][OAc]. When peak normalization was used for data preprocessing, a model for the quantitative estimation of cellulose content between 0 wt.% and 4 wt.% with an error of 0.53 wt.% was generated. The methods described here provide the basis for a rapid and facile technique for the determination of dissolved cellulose content in [emim][OAc].     

A nuclear magnetic resonance study of secondary and tertiary structure in yeast tRNAPhe.

We present experimental evidence which confirms recently proposed ring current prediction methods for assigning hydrogen-bond proton nuclear magnetic resonance (NMR) spectra from tRNA (Robillard, G. T., Tarr, C. E., Vosman, F., & Berendsen, H. J. C. (1976) Nature (London) 262, 363-369; Robillard, G. T., Tarr, C. E., Vosman, F., & Sussman, J. L. (1977) Biophys. Chem. 6, 291-298). The evidence is a series of temperature-dependent studies on yeast tRNAPhe monitoring both the high- and low-field NMR spectral regions, which are correlated with independent optical and temperature-jump (temp-jump) studies performed under identical ionic strength conditions. Using assignments derived from the new prediction methods, the melting patterns of the hydrogen-bonded resonances agree with those expected on the basis of optical, temp-jump, and NMR studies on the high-field spectral region. The implication of these results is that previous assignment procedures are at least partially incorrect and, therefore, studies based on those procedures must be reexamined.     

The use of Quantified Maximum Entropy Methods for Optimising Information from Electron Paramagnetic Resonance Spectroscopy

A quantified maximum entropy method is applied to the optimisation of analytical information from FPR spectra of free radicals. Statistically meaningful errors are produced for the positions and intensities of all spectral peaks and considerable improvements in sensitivity compared with conventional spectral enhancement procedures are obtained with measurements of the intensities of spectra of known radicals.     

Chemometric tools for classification and elucidation of protein secondary structure from infrared and circular dichroism spectroscopic measurements

Protein classification and characterization often rely on the information contained in the protein secondary structure. Protein class assignment is usually based on X-ray diffraction measurements, which need the protein in a crystallized form, or on NMR spectra, to obtain the structure of a protein in solution. Simple spectroscopic techniques, such as circular dichroism (CD) and infrared (IR) spectroscopies, are also known to be related to protein secondary structure, but they have seldom been used for protein classification. To see the potential of CD, IR, and combined CD/IR measurements for protein classification, unsupervised pattern recognition methods, Principal Component Analysis (PCA) and cluster analysis, are proposed first to check for natural grouping tendencies of proteins according to their measured spectra. Partial Least Squares Discriminant Analysis (PLS-DA), a supervised pattern recognition method, is used afterwards to test the possibility to model explicitly each protein class and to test these models in class assignment of unknown proteins. Determination of the protein secondary structure, understood as the prediction of the abundance of the different secondary structure motifs in the biomolecule, was carried out with the local regression method interval Partial Least Squares (iPLS). CD, IR, and CD/IR measurements were correlated to the fraction of the motif to be predicted, determined from X-ray measurements. iPLS builds models extracting the spectral information most correlated to a specific secondary motif and avoids the use of irrelevant spectral regions. Spectral intervals chosen by iPLS models provide structural information which can be used to confirm previous biochemical assignments or identify new motif-related spectral features. The predictive ability of the models built with the selected spectral regions has a quality similar to previous classical approaches.     

Identification of insulin variants using Raman spectroscopy

Drop coating deposition Raman (DCDR) spectroscopy is used to obtain high-quality normal Raman spectra from small volumes (10 microl) of dilute insulin solutions (3-400 microM) for spectral identification and chromatographic detection. The results are used to demonstrate the spectroscopic classification (identification) of three natural insulin variants-human, bovine, and porcine-that differ by between one and three amino acid residues. DCDR measurements were performed on solutions obtained from reverse phase high-performance liquid chromatography (RP-HPLC) eluent fractions, either before or after lyophilization. Classification is demonstrated using replicate DCDR measurements, followed by normalized Savitsky-Golay second derivative preprocessing and partial least squares training with either leave-one-out or batch-to-batch testing.     

Increased uterine vascular permeability at the time of embryonic attachment in the pig

The temporal relationship between embryonic attachment and endometrial vascular permeability was investigated in the gilt. Light and electron microscopy failed to reveal structural differences between Day 10 cycling and pregnant maternal epithelia, including evidence of blastocyst contact. Chorionic adhesion was preserved at mesometrial regions in 3 of 5 Day 13 pregnant animals and appeared to be related to localized differentiation of the underlying maternal epithelium. In order to study uterine vascular permeability, 44 gilts between Days 11 and 19 of the cycle and pregnancy were injected i.v. with a 0.5% solution of Evans Blue (2.5 ml/kg body weight). Examination of excised uteri under ultraviolet light revealed a well-defined zone of endometrial fluorescence corresponding to extravascular content of the dye. Exclusive to pregnant gilts, this response appeared in conjunction with blastocyst elongation at Day 12, and was consistently confined to areas of embryonic membrane contact thereafter. The changes in endometrial morphology and vascular permeability suggest involvement of some embryonic factor(s) acting in a localized manner. Increased histotrophe production is probably facilitated by the flux of plasma constituents to maternal epithelial cells. Coincidence of increased uterine vascular permeability at the site of attachment with elevated blood flow would enhance transport of nutrients toward the conceptus and allow access of blastocyst-induced products to the maternal circulation.     

Determination of causes of death via spectrochemical analysis of forensic autopsies‐based pulmonary edema fluid samples with deep learning algorithm

This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network‐based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR was generally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocol‐based datasets in our study. In conclusion, this study demonstrates that the deep learning‐equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death.     

Effects of embryo size at transfer (whole versus demi) and early pregnancy progesterone supplementation on embryo growth and pregnancy-specific protein bovine concentrations in recipient dairy heifers

The objectives of this study were to evaluate embryonic size and survival, plasma progesterone (P4) and pregnancy-specific protein bovine (PSPB) concentrations in early pregnancies (n = 99) following the transfer of one whole (n = 66) or one demi (n = 33) embryo to recipient virgin dairy heifers. The experiment was designed to evaluate the fixed effects of embryo size at transfer (whole or demi embryo) on Day 7 of the estrous cycle (Day 0 = estrus) and P4 supplementation between Days 7 to 19 through an intravaginal device (yes or no) on plasma P4 and PSPB concentrations and on embryo measurements. Plasma P4 concentrations were measured by RIA on Days 0, 7, 14, 19, 21, 25, 35, 42, 49, 56 and 63 of pregnancy and, PSPB concentrations were measured by ELISA on Days 7, 21, 25, 35, 42, 49, 56 and 63. The presence of an embryonic vesicle was detected on Day 25, embryonic/fetal movements and heartbeat were evaluated on Days 42 and 63 and embryo measurements [crown-rump length (CRL) and width at mid body] were obtained on Day 42 through ultrasonography.In non-supplemented pregnancies, Day 42 whole embryos had higher (P < 0.05) CRL and width than demi embryos, but the difference averaged only 1 to 2 mm. In P4 supplemented pregnancies, whole and demi embryos attained a similar size on Day 42 of pregnancy. Embryo size at transfer, early exogenous P4 supplementation and their interactions had no effects (P > 0.05) on plasma P4 concentrations. However, the post-hoc LSD evaluation showed that plasma P4 concentrations on Day 25 were higher (P < 0.001) in whole than in demi embryo derived pregnancies and, that exogenous P4 supplementation increased (P < 0.05) plasma P4 concentrations on Day 19 of pregnancy. The plasma PSPB detection rate on Days 7 to 63 of pregnancy was similar in pregnancies resulting from the transfer of whole and demi embryos. From a total of 93 recipients remaining pregnant until Day 63, plasma PSPB was constantly undetectable on Day 7, was detected in 4% of Day 21 samples, 41% of Day 25, 95% of Day 35, 96% of Day 42, 99% of Day 49 and in 100% of samples of Days 56 and 63. Concentrations of PSPB increased (P < 0.05) from Days 21 to 42 and from Days 56 to 63, with a plateau between Days 42 to 56. Demi embryo pregnancies had higher (P < 0.05) plasma PSPB concentrations on Days 35 and 42 than whole embryo pregnancies. Progesterone supplementation had a positive effect (P < 0.01) on PSPB concentrations from Days 35 to 63. Concentrations of PSPB were similar in non-supplemented whole and demi embryo pregnancies from Days 7 to Day 63. In contrast, in supplemented recipients, demi embryo pregnancies had higher (P < 0.05) PSPB concentrations on Days 25 to 42 than whole embryo pregnancies. No significant correlation was found between P4 and PSPB concentrations or between the concentrations of these hormones and embryonic measurements on Day 42. In conclusion, demi embryos experienced a compensatory growth until Day 42 of pregnancy, attaining a similar size to that of whole embryos and originating conceptuses producing similar plasma PSPB concentrations to those of whole embryo derived conceptuses. Embryonic growth and conceptus secretion of PSPB were positively stimulated by early pregnancy exogenous P4 treatment.     

Study of the radiation spectra of fast Z-pinches formed during the implosion of wire arrays in the Angara-5-1 facility

Results are presented from measurements of the radiation spectra of the Z-pinch tungsten plasma produced during the implosion of cylindrical wire arrays with a linear mass of 200?C400 ??g/cm and an initial diameter of 12?C20 mm at a current of ??3 MA in the experiments performed at the Angara-5-1 facility. The radiation spectra in the photon energy range of 50?C900 eV were recorded on a UF-4 X-ray film by using a spectrograph with a transmission grating. The radiation spectrum in the photon energy range of 1?C3 keV was recorded using a crystalline panoramic spectrograph. A curtain shutter was used to protect the transmission grating from fast microparticles produced due to the erosion of high-voltage electrodes. The total radiation yield was measured with a thermocouple calorimeter. It is shown that most of the tungsten plasma radiation energy is emitted in the photon energy range of 80?C300 eV. Measurements of the spectral intensity of pinch radiation with a spatial resolution along the pinch radius showed that the effective transverse diameter of the pinch did not exceed 2 mm, which agrees with independent current measurements of the pinch size. The results of measurements of the spectral intensity of pinch radiation were compared with calculations per-formed under the assumption of a stationary homogeneous plasma.     

Conceptus development in large white and prolific Chinese Meishan pigs

Large White (LW) and Meishan (MS) gilts were killed on Days 8, 10, 11, 12, 14 and 30 of gestation. Mean diameters (mm) for MS and LW conceptuses, respectively, were: Day 8, 0.45 and 0.69; Day 10, 2.7 and 1.9; Day 11, 5.3 and 2.7, with the differences among days being affected by breed (P less than 0.01). Variation in diameter among conceptuses from LW gilts was greater (P less than 0.01) than that for MS gilts on Days 8-11, respectively: Day 8, 20 and 46%; Day 10, 29 and 38%; and Day 11, 22 and 44%. Conceptuses had elongated in 3 of 5 MS and 1 of 4 LW gilts on Day 11, 6 and 6 MS and 2 of 4 LW gilts on Day 12 and all gilts of both breeds on Day 14. These results indicate that conceptuses of MS gilts develop more rapidly and more uniformly between Days 8 and 14 of gestation. Overall, embryonic survival for Days 8-12 for gilts not having elongated conceptuses was 90.2% for MS and 73.2% for LW gilts (P less than 0.01). On Day 30 of gestation, embryonic survival was also higher (P less than 0.01) for MS (89%) than LW (55%) gilts. However, embryonic weight, crown-rump length, placental length, allantoic fluid volume, amniotic fluid volume, as well as total glucose, fructose and protein in allantoic fluid were not affected by breed. Placental weight was greater (P less than 0.01) for LW gilts. Uterine development at Day 30 of gestation, based on total length and weight of uterine horns, width of uterine horns, total endometrial surface area and total endometrial weight was greater (P less than 0.01) for LW gilts.(ABSTRACT TRUNCATED AT 250 WORDS)