Near infrared spectroscopy, cluster and multivariate analysis hyphenated to thin layer chromatography for the analysis of amino acids

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Near-infrared spectroscopy based monitoring of all 20 amino acids in mammalian cell culture broth

The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL^-1), lactate (0-2.7 mg mL^-1), and trastuzumab (0-2.5 mg mL^-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm^-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (R_c² = 0.94-0.99) and prediction (R_p² = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (R_EV² = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.

Exploring the use of NIR reflectance spectroscopy in prediction of free L-Asparagine in solanaceae plants

Much researches of Near-infrared spectroscopy modeling methods that are utilized to analyze the trace amount components, especially indirect modeling on complex system, have gained widely attraction in recent years. Amino acids in plants are essential nutrients of maintaining growth and ensuring health. As the important participants in various biochemical reactions in plants, nondestructive detection of free amino acids will provide meaningful observation on physiological changing in different steps of plant growth. In this research, two hundred and twenty-two samples were measured to obtain the concentration of free L-Asparagine in plant by amino acid analyzer. NIR spectra were also collected for conducting chemometrics modeling. Different spectral pretreatments and variables selecting methods were employed to optimize the NIR models. Independent validation set as well as unknown samples from different years were successfully predicted by using the slope intercept correction. Results in this study demonstrated that fast analysis of free L-Asparagine can be established by NIR modeling approach.

Calibration and prediction of amino acids in stevia leaf powder using near infrared reflectance spectroscopy

The use of stevia as animal feed additive has been researched over the years, but how to rapidly predict its amino acid contents has not been studied yet by using near-infrared reflectance spectroscopy. In the present study, 301 samples of stevia leaf powder were defined as the calibration set from which calibration models were optimized, and the performance of prediction was evaluated. Compared with other mathematical treatments, the models developed with the "1, 12, 12, 1" treatment, combined with modified partial least-squares regression and standard normal variance with de-trending, had a significant potential in predicting amino acid contents, such as threonine, serine, etc. Six spectral regions were found to possess large spectrum variation and show high contribution to calibration models. From the present study, the calibration models of amino acids in stevia were successfully developed and could be applied to quality control in feed processing, breeding selection and mutant screening.

A strategy for enhancing the quantitative determination ability of the diffuse reflectance near-infrared spectroscopy

Near-infrared diffuse reflectance spectroscopy (NIRDRS) has been proved to be a convenient and fast quantitative method for complex samples. The high detection limit or the low sensitivity of the method, however, is a big problem obstructing its application in the analysis of low concentration samples. A strategy for quantitative determination of low concentration samples was developed by using NIRDRS. The method takes an adsorbent as a substrate for gathering the analytes from a solution, and uses the multivariate calibration technique for quantitative calculation. So, the detection limit can be improved and the interferences can be eliminated when complex samples are analyzed. Taking benzoic and sorbic acids as the analyzing targets and the alumina as the adsorbent, partial least squares (PLS) model is built from the NIRDRS of the adsorbates. The results show that the concentrations that can be quantitatively detected are as low as 0.011 and 0.013 mg mL(-1) for benzoic and sorbic acids, respectively, and the co-adsorbates do not interfere each other.

Nano-structured support materials, their characterisation and serum protein profiling through MALDI/TOF-MS

In the bioanalytical era, novel nano-materials for the selective extraction, pre-concentration and purification of biomolecules prior to analysis are vital. Their application as affinity binding in this regard is needed to be authentic. We report here the comparative application of derivatised materials and surfaces on the basis of nano-crystalline diamond, carbon nanotubes and fullerenes for the analysis of marker peptides and proteins by material enhanced laser desorption ionisation mass spectrometry MELDI-MS. In this particular work, the emphasis is placed on the derivatization, termed as immobilised metal affinity chromatography (IMAC), with three different support materials, to show the effectiveness of MELDI technique. For the physicochemical characterisation of the phases, near infrared reflectance spectroscopy (NIRS) is used, which is a well-established method within the analytical chemistry, covering a wide range of applications. NIRS enables differentiation between silica materials and different fullerenes derivatives, in a 3-dimensional factor-plot, depending on their derivatizations and physical characteristics. The method offers a physicochemical quantitative description in the nano-scale level of particle size, specific surface area, pore diameter, pore porosity, pore volume and total porosity with high linearity and improved precision. The measurement takes only a few seconds while high sample throughput is guaranteed.