FT-Raman Spectroscopy Study of the Remineralization of Microwave-Exposed Artificial Caries.

Dental caries is a microbially mediated disease that can result in significant tooth structure degradation. Although the preponderance of lesions is treated by surgical intervention, various strategies have been developed for its noninvasive management. Here, we use a novel approach for noninvasive treatment based on killing Streptococcus mutans with high-frequency microwave energy (ME). The rationale for this approach is based on modulating the pH of caries to a physiological state to enable spontaneous tooth remineralization from exogenous sources. In the present study, after demonstrating that ME kills >99% of S. mutans in planktonic cultures, 8 enamel slabs were harvested from a single tooth. Baseline mineral concentration at each of 12 points per slab was obtained using Fourier transform (FT)-Raman spectroscopy. Surface demineralization was subsequently promoted by subjecting all samples to an S. mutans acidic biofilm for 6 d. Half of the samples were then exposed to high-frequency ME, and the other half were used as controls. All samples were next subjected to a remineralization protocol consisting of two 45-min exposures per 24-h period in tryptic soy broth followed by immersion in a remineralizing solution for the remaining period. After 10 d, samples were removed and cleaned. FT-Raman spectra were again obtained at the same 12 points per sample, and the mineral concentration was determined. The effect of the remineralization protocol on the demineralized slabs was expressed as a percentage of mineral loss or gain relative to baseline. The mineral concentration of the microwave-exposed group collectively approached 100% of baseline values, while that of the control group was in the order of 40%. Differences between groups were significant (P = 0.001, Mann-Whitney U test). We concluded that killing of S. mutans by ME promotes effective remineralization of S. mutans-demineralized enamel compared with controls.

Prediction of drug content and hardness of intact tablets using artificial neural network and near-infrared spectroscopy.

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2% w/w) prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.

A comparison of reflectance and transmittance near-infrared spectroscopic techniques in determining drug content in intact tablets.

Drug contents of intact tablets were determined using non-destructive near infrared (NIR) reflectance and transmittance spectroscopic techniques. Tablets were compressed from blends of Avicel PH-101 and 0.5% w/w magnesium stearate with varying concentrations of anhydrous theophylline (0, 1, 2, 5, 10, 20 and 40% w/w). Ten tablets from each drug content batch were randomly selected for spectral analysis. Both reflectance and transmittance NIR spectra were obtained from these intact tablets. Actual drug contents of the tablets were then ascertained using a UV-spectrophotometer at 268 nm. Multiple linear regression (MLR) models at 1116 nm and partial least squares (PLS) calibration models were generated from the second derivative spectral data of the tablets in order to predict drug contents of intact tablets. Both the reflectance and the transmittance techniques were able to predict the drug contents in intact tablets over a wide range. However, a comparison of the results of the study indicated that the lowest percent errors of prediction were provided by the PLS calibration models generated from spectral data obtained using the transmittance technique.

Application of near-infrared spectroscopy for nondestructive analysis of Avicel powders and tablets.

The purpose of this study was to use near-infrared spectroscopy (NIRS) as a nondestructive technique to (a) differentiate three Avicel products (microcrystalline cellulose [MCC] PH-101, PH-102, and PH-200) in powdered form and in compressed tablets with and without 0.5% w/w magnesium stearate as a lubricant; (b) determine the magnesium stearate concentrations in the tablets; and (c) measure hardness of tablets compressed at several compression forces. Diffuse reflectance NIR spectra from Avicel powders and tablets (compression forces ranging from 0.2 to 1.2 tons) were collected and distance scores calculated from the second-derivative spectra were used to distinguish the different Avicel products. A multiple linear regression model was generated to determine magnesium stearate concentrations (from 0.25 to 2% w/w), and partial least squares (PLS) models were generated to predict hardness of tablets. The NIRS technique could distinguish between the three different Avicel products, irrespective of lubricant concentration, in both the powdered form and in the compressed tablets because of the differences in the particle size of the Avicel products. The percent error for predicting the lubricant concentration of tablets ranged from 0.2 to 10% w/w. The maximum percent error of prediction of hardness of tablets compressed at the various compression forces was 8.8% for MCC PH-101, 5.3% for MCC PH-102, and 4.6% for MCC PH-200. The NIRS nondestructive technique can be used to predict the Avicel type in both powdered and tablet forms as well as to predict the lubricant concentration and hardness.