Determination of binding parameters such as the number of ligands and the respective binding constants require a considerable number of experiments to be performed. These involve accurate determination of either free and/or bound ligand concentration irrespective of the measurement technique applied. Then, an appropriate theoretical model is used to fit the experimental data, and to extract the binding parameters. In this work, the interaction between bovine serum albumin (BSA) and 1-anilino-8-naphthalene sulphonate (ANS) is revisited. Using steady state fluorescence spectroscopy, the binding isotherm of BSA/ANS was obtained applying the Halfman-Nishida approach. The binding parameters, site number, and binding site association constants, were determined from the stoichiometric Adair model and Job's plot. The binding parameters obtained were then correlated to the distance of the respective binding site to the tryptophan residues using the energy transfer technique. This approach, that uses both tryptophans independently from each other, is presented as a tool to help understand the binding mechanism of the albumin fluorescent complex. The results show that ANS molecules bind to BSA in up to five different binding sites. Energy transfer from the tryptophan residues to the BSA/ANS complex shows that the four highest affinity binding sites (>10(4) M(-1)) are located at a reasonably close distance (18-27 A) to at least one of two tryptophan residues, while the lowest affinity binding site (approximately 10(4) M(-1)) is located over 34 A away from the both tryptophans.

. (2010) 'Rapid characterization and quality control of complex cell culture media solutions using Raman spectroscopy and chemometrics'. Biotechnology And Bioengineering, 107 (2):290-301. Sirimuthu, and A.G. Ryder. Biotechnology and Bioengineering, 107(2), 290-301, (2010). DOI: 10.1002/bit.22813 . 1 R A P I D C H A R A C T E R I S A T I O N A N D Q U A L I T Y C O N T R O L O F C O M P L E X C E L L C U L T U R E M E D I A S O L U T I O N S U S I N G R A M A N S P E C T R O S C O P Y A N D C H E M O M E T R I C S . Abstract:The use of Raman spectroscopy coupled with chemometrics for the rapid identification, characterisation, and quality assessment of complex cell culture media components used for industrial mammalian cell culture was investigated. Raman spectroscopy offers significant advantages for the analysis of complex, aqueous based materials used in biotechnology because there is no need for sample preparation and water is a weak Raman scatterer. We demonstrate the efficacy of the method for the routine analysis of dilute aqueous solution of five different chemically defined, commercial media components used in a Chinese Hamster Ovary (CHO) cell manufacturing process for recombinant proteins.The chemometric processing of the Raman spectral data is the key factor in developing robust methods. Here we discuss the optimum methods for eliminating baseline drift, background fluctuations and other instrumentation artefacts to generate reproducible spectral data. Principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) were then employed in the development of a robust routine for both identification and quality evaluation of the five different media components. These methods have the potential to be extremely useful in an industrial context for "in house" sample handling, tracking and quality control.

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein-and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>E35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.

Raman spectroscopy offers the potential for the identification of illegal narcotics in seconds by inelastic scattering of light from molecular vibrations. In this study cocaine, heroin, and MDMA were analyzed using near-IR (785 nm excitation) micro-Raman spectroscopy. Narcotics were dispersed in solid dilutants of different concentrations by weight. The dilutants investigated were foodstuffs (flour, baby milk formula), sugars (glucose, lactose, maltose, mannitol), and inorganic materials (Talc powder, NaHCO3, MgSO4·7H2O). In most cases it was possible to detect the presence of drugs at levels down to ∼10% by weight. The detection sensitivity of the Raman technique was found to be dependent on a number of factors such as the scattering cross-sections of drug and dilutant, fluorescence of matrix or drug, complexity of dilutant Raman spectrum, and spectrometer resolution. Raman spectra from a series of 20 mixtures of cocaine and glucose (0–100% by weight cocaine) were collected and analyzed using multivariate analysis methods. An accurate prediction model was generated using a Partial Least Squares (PLS) algorithm that can predict the concentration of cocaine in solid glucose from a single Raman spectrum with a root mean standard error of prediction of 2.3%.

This work offers a real-world comparison of derivative preprocessing and a new polynomial method described by Lieber and Mahadevan-Jansen (LMJ) for baseline correction of Raman spectra with widely varying backgrounds. This comparison is based on their outcomes in factor analysis, analyte discrimination, and quantification. Both correction methods are applied to a Raman spectra data set taken from 85 solid samples of illegal narcotics diluted with various materials. It is found that neither approach outperforms the other, as they give similar principal component analysis (PCA) models and quantification errors: cocaine and heroin show cross-validation errors of approximately 8%, while MDMA is quantified to a cross-validation error of approximately 3-4%. The LMJ method does offer several other advantages, the most significant being the retention of original peak shapes after the correction, which simplifies the interpretation of the preprocessed spectra. The LMJ method is therefore recommended for use as a baseline correction method in future research with Raman spectroscopy.

Near-infrared (785 nm) excitation was used to obtain Raman spectra from a series of 33 solid mixtures containing cocaine, caffeine and glucose (9.8-80.6% by weight cocaine), which were then analysed using chemometric methods. Principal component analysis of the data was employed to ascertain what factors influenced the spectral variation across the concentration range. It was found that 98% of the spectral variation was accounted for by three principal components. Analysis of the score and loadings plots for these components showed that the samples can be clearly classified on the basis of cocaine concentration. Discrimination on the basis of caffeine and glucose concentrations was also possible. Quantitative calibration models were generated using partial least-squares algorithms which predicted the concentration of cocaine in the solid mixtures containing caffeine and glucose from the Raman spectrum with a root mean standard error of prediction (RMSEP) of 4.1%. Caffeine and glucose concentrations were estimated with RMSEPs of 5.2 and 6.6%, respectively. These measurements demonstrate the feasibility of using near-IR Raman spectroscopy for rapid quantitative characterization of illegal narcotics.

Light and moleculesThe Perrin-Jablonski diagram (figure 1) is convenient for visualizing the different processes involved in the

Abstract. The classification of high dimensional data, such as images, geneexpression data and spectral data, poses an interesting challenge to machine learning, as the presence of high numbers of redundant or highly correlated attributes can seriously degrade classification accuracy. This paper investigates the use of Principal Component Analysis (PCA) to reduce high dimensional data and to improve the predictive performance of some well known machine learning methods. Experiments are carried out on a high dimensional spectral dataset, in which the task is to identify a target material within a mixture. These experiments employ the NIPALS (Non-Linear Iterative Partial Least Squares) PCA method, a method that has been used in the field of chemometrics for spectral classification, and is a more efficient alternative than the widely used eigenvector decomposition approach. The experiments show that the use of this PCA method can improve the performance of machine learning in the classification of high dimensionsal data.