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In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

We aimed to develop an amino acid sequence-dependent analytical method using near-infrared (NIR) spectroscopy. The detailed analysis of the NIR spectra of eight different amino acid aqueous solutions (glycine, alanine, serine, glutamine, lysine, phenylalanine, tyrosine, and proline) revealed different spectral patterns characteristic of different amino acid residues in the 6200-5700 and 5000-4200 cm

Using near-infrared (NIR) spectroscopy, we aimed to develop a method of monitoring the increasing number of amide bonds with the elongation of the chain length of peptides. Because peptide synthesis can be monitored by evaluating the increasing number of amide bonds with dehydration occurring between amino acids, polyglycine, which has the simplest structure among polyamino acids, was studied, and the key bands whose absorption intensities increased with the elongation of the chain length, such as the bands attributed to glycine, diglycine, triglycine, and tetraglycine, were searched. The bands due to the combinations of the amide A and amide II/III modes in the region of 5000-4500 cm-1 were revealed to be good candidates for key bands, their second derivative intensities increased as the number of amide bonds increased, regardless of pH, solvent species, and the presence of protecting groups. The number of amide bonds was evaluated by a partial least square regression using the abovementioned combination bands, and a calibration model with a high determination coefficient (≥0.99) was constructed. These results not only have demonstrated the usefulness of NIR spectroscopy as a process analytical technology tool for the process of synthesizing the peptide in a microflow reactor but also have provided basic knowledge for analyzing amide bonds in the NIR spectra of proteins, polyamino acids, polypeptides, and polyamides.

We present a rapid switching system between a newly developed near-infrared (NIR) spectrometer and its imaging system to select the spot size of a diffuse reflectance (DR) probe. In a previous study, we developed a portable NIR imaging system, known as D-NIRs, which has significant advantages over other systems. Its high speed, high spectral resolution, and portability are particularly useful in the process of monitoring pharmaceutical tablets. However, the spectral accuracies relating to the changes in the formulation of the pharmaceutical tablets have not been fully discussed. Therefore, we improved the rapid optical switching system and present a new model of D-NIRs (ND-NIRs) here. This system can automatically switch the optical paths of the DR and NIR imaging probes, greatly contributing to the simultaneous measurement of both the imaging and spot. The NIR spectra of the model tablets, including 0–10% ascorbic acid, were measured and simultaneous NIR images of the tablets were obtained. The predicted results using spot sizes for the DR probe of 1 and 5 mm diameter, resulted in concentrations of R2 = 0.79 and 0.94, with root mean square errors (RMSE) of 1.78 and 0.89, respectively. For tablets with a high concentration of ascorbic acid, the NIR imaging results showed inhomogeneity in concentration. However, the predicted values for the low concentration samples appeared higher than the known concentration of the tablets, although the homogeneity of the concentration was confirmed. In addition, the optimal spot size using NIR imaging data was estimated to be 5–7 mm. The results obtained in this study show that the spot size of the fiber probe, attached to a spectrometer, is important in developing a highly reliable model to determine the component concentration of a tablet.

The present study aims to suggest a method to identify defective tablets by near infrared (NIR) imaging. A newly developed portable imaging system (D-NIRs) was used in this study, in which the spectrometer is equipped with a high- density photodiode array detector to record high-quality spectra with 1.25 nm spectral resolution. This system is highly portable and allows an image of a target tablet to be developed in approximately 10 s. Normal tablets containing 0.1–20 % magnesium stearate, ascorbic acid, corn starch and talc were prepared. NIR spectra in the 950–1700 nm region of each pixel in a tablet were measured, and NIR images were generated from the second derivative of the spectra at 1213 nm. It was confirmed that the spectral distribution in a tablet passed as a normal distribution by the goodness-of-fit test (p ≤ 0.05). Consequently, the average of the spectra obtained from each pixel of the whole tablet was used to predict the concentration of magnesium stearate. The quantitative accuracy of the prediction model by the second derivative spectra achieved R2 = 0.931 and RMSE = 1.90 %. Defective tablets were prepared with localised magnesium stearate. The skewness of the second derivative in the defective tablet was larger than that of the standard distribution. Specifically, the distribution of defective tablets was biased to the right as compared to the standard distribution. The results of the presented study suggest that spectral imaging combined with distribution analysis is an effective method to identify defective tablets.

An alternative baseline correction method for diffuse reflection near-infrared (NIR) spectra, searching region standard normal variate (SRSNV), was proposed. Standard normal variate (SNV) is an effective pretreatment method for baseline correction of diffuse reflection NIR spectra of powder and granular samples; however, its baseline correction performance depends on the NIR region used for SNV calculation. To search for an optimal NIR region for baseline correction using SNV, SRSNV employs moving window partial least squares regression (MWPLSR), and an optimal NIR region is identified based on the root mean square error (RMSE) of cross-validation of the partial least squares regression (PLSR) models with the first latent variable (LV). The performance of SRSNV was evaluated using diffuse reflection NIR spectra of mixture samples consisting of wheat flour and granular glucose (0–100% glucose at 5% intervals). From the obtained NIR spectra of the mixture in the 10 000–4000 cm−1 region at 4 cm intervals (1501 spectral channels), a series of spectral windows consisting of 80 spectral channels was constructed, and then SNV spectra were calculated for each spectral window. Using these SNV spectra, a series of PLSR models with the first LV for glucose concentration was built. A plot of RMSE versus the spectral window position obtained using the PLSR models revealed that the 8680–8364 cm−1 region was optimal for baseline correction using SNV. In the SNV spectra calculated using the 8680–8364 cm−1 region (SRSNV spectra), a remarkable relative intensity change between a band due to wheat flour at 8500 cm−1 and that due to glucose at 8364 cm−1 was observed owing to successful baseline correction using SNV. A PLSR model with the first LV based on the SRSNV spectra yielded a determination coefficient (R2) of 0.999 and an RMSE of 0.70%, while a PLSR model with three LVs based on SNV spectra calculated in the full spectral region gave an R2 of 0.995 and an RMSE of 2.29%. Additional evaluation of SRSNV was carried out using diffuse reflection NIR spectra of marzipan and corn samples, and PLSR models based on SRSNV spectra showed good prediction results. These evaluation results indicate that SRSNV is effective in baseline correction of diffuse reflection NIR spectra and provides regression models with good prediction accuracy.

The feasibility of real-time release testing of bilayer tablets was investigated using near infrared (NIR) spectroscopy. The newly developed polychromator-type NIR spectrometer was used to compare the diffuse reflectance (DR) and transmittance (Tr) NIR spectroscopic techniques. This spectrometer not only performs highly sensitive NIR measurements but also yields the NIR spectra of an intact tablet on a millisecond (ms) timescale; i.e. 500 ms for the DR-NIR measurements and 400 ms for the Tr-NIR measurements. The bilayer tablets were prepared with the first layer comprising 0–10% ascorbic acid (AsA), 20% corn starch, 5% talc, 30% microcrystalline cellulose and 45–35% lactose, and the second layer comprising 20% corn starch, 5% talc, 30% microcrystalline cellulose and 45% lactose; their DR-and Tr-NIR spectra were acquired from both sides of the tablet. With these spectra, the feasibility of DR- and Tr-NIR spectroscopy for the quantitative analysis of AsA in the bilayer tablets was compared. The DR- and Tr-NIR spectra of the bilayer tablets and their second-derivative spectra were studied. The AsA bands were not identified in the DR- and Tr-NIR spectra. However, the AsA bands at 995 nm and 1458 nm were observed in the second-derivative spectra. All the developed regression models predicted the AsA concentration, and regression vectors indicated that the prediction was based on the AsA bands. In addition, the model using the Tr-NIR spectra was able to predict the AsA concentration, even when the bilayer tablet was flipped.

We have recently developed a novel portable NIR imaging device (D-NIRs), which has a high speed and high wavelength resolution. This NIR imaging approach has been developed by utilizing D-NIRs for studying the dissolution of a model tablet containing 20 % ascorbic acid (AsA) as an active pharmaceutical ingredient and 80 % hydroxypropyl methylcellulose, where the tablet is sealed by a special cell. Diffuse reflectance NIR spectra in the 1,000 to 1,600 nm region were measured during the dissolution of the tablet. A unique band at around 1,361 nm of AsA was identified by the second derivative spectra of tablet and used for AsA distribution NIR imaging. Two-dimensional change of AsA concentration of the tablet due to water penetration is clearly shown by using the band-based image at 1,361 nm in NIR spectra obtained with high speed. Moreover, it is significantly enhanced by using the intensity ratio of two bands at 1,361 and 1,354 nm corresponding to AsA and water absorption, respectively, showing the dissolution process. The imaging results suggest that the amount of AsA in the imaged area decreases with increasing water penetration. The proposed NIR imaging approach using the intensity of a specific band or the ratio of two bands combined with the developed portable NIR imaging instrument, is a potentially useful practical way to evaluate the tablet at every moment during dissolution and to monitor the concentration distribution of each drug component in the tablet.