Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

Follow-up of solid-state fungal wood pretreatment by a novel near-infrared spectroscopy-based lignin calibration model

Lignin removal plays a crucial role in the efficient bioconversion of lignocellulose to fermentable sugars. As a delignification process, fungal pretreatment has gained great interest due to its environmental friendliness and low energy consumption. In our previous study, a positive linear correlation between acid-insoluble lignin degradation and the achievable enzymatic saccharification yield has been found, hereby highlighting the importance of the close follow-up of lignin degradation during the solid-state fungal pretreatment process. However, the standard quantification of lignin, which relies on the two-step acid hydrolysis of the biomass, is highly laborious and time-consuming. Vibrational spectroscopy has been proven as a fast and easy alternative; however, it has not been extensively researched on lignocellulose subjected to solid-state fungal pretreatment. Therefore, the present study examined the suitability of near-infrared spectroscopy (NIR) for the rapid and easy assessment of lignin content in poplar wood pretreated with Phanerochaete chrysosporium. Furthermore, the predictive power of the obtained calibration model and the recently published ATR-FTIR spectroscopy-based model were compared for the first time using the same fungus-treated wood data set. PLSR was used to correlate the NIR spectra to the acid-insoluble lignin contents (19.9%-27.1%) of pretreated wood. After normalization and second derivation, a PLSR model with a good coefficient of determination (R_CV² = 0.89) and a low root mean square error (RMSE_CV = 0.55%) were obtained despite the heterogeneous nature of the fungal solid-state fermentation. The performance of this PLSR model was comparably good to the one obtained by ATR-FTIR (R_CV² = 0.87) while it required more extensive spectral pre-processing. In conclusion, both methods will be highly useful for the high-throughput and user-friendly monitoring of lignin degradation in a solid-state fungal pretreatment-based biorefinery concept.

Rapid lignin quantification for fungal wood pretreatment by ATR-FTIR spectroscopy

Lignin determination in lignocellulose with the conventional two-step acid hydrolysis method is highly laborious and time-consuming. However, its quantification is crucial to monitor fungal pretreatment of wood, as the increase of acid-insoluble lignin (AIL) degradation linearly correlates with the achievable enzymatic saccharification yield. Therefore, in this study, a new attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy method was developed to track fungal delignification in an easy and rapid manner. Partial least square regression (PLSR) with cross-validation (CV) was applied to correlate the ATR-FTIR spectra with the AIL content (19.9 %-27.1 %). After variable selection and normalization, a PLSR model with a high coefficient of determination (R_CV² = 0.87) and a low root mean square (RMSECV = 0.60 %) were obtained despite the heterogeneous nature of the fungal solid-state fermentation. These results show that ATR-FTIR can reliably predict the AIL content in fungus-treated wood while being a high-throughput method. This novel method can facilitate the transition to the wood-based economy.

Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes

Introduction: Lung cancer is the most common malignancy worldwide. Squamous cell carcinoma (SQ) and adenocarcinoma (LUAD) are the two most frequent histological subtypes. Small cell carcinoma (SCLC) subtype has the worst prognosis. Differential diagnosis is essential for proper oncological treatment. Life science associated mid- and near-infrared based microscopic techniques have been developed exponentially, especially in the past decade. Vibrational spectroscopy is a potential non-destructive approach to investigate malignancies.

Aims: Our goal was to differentiate lung cancer subtypes by their label-free mid-infrared spectra using supervised multivariate analyses.

Material and Methods: Formalin-fixed paraffin-embedded (FFPE) samples were selected from the archives. Three subtypes were selected for each group: 10-10 cases SQ, LUAD and SCLC. 2 μm thick sections were cut and laid on aluminium coated glass slides. Transflection optical setup was applied on Perkin-Elmer infrared microscope. 250 × 600 μm areas were imaged and the so-called mid-infrared fingerprint region (1800-648cm⁻¹) was further analysed with linear discriminant analysis (LDA) and support vector machine (SVM) methods.

Results: Both “patient-based” and “pixel-based” approaches were examined. Patient-based analysis by using 3 LDA models and 2 SVM models resulted in different separations. The higher the cut-off value the lower is the accuracy. The linear C-support vector classification (C-SVC) SVM resulted in the best (100%) accuracy for the three subtypes using a 50% cut-off value. The pixel-based analysis gave, similarly, the linear C-SVC SVM model to be the most efficient in the statistical indicators (SQ sensitivity 81.65%, LUAD sensitivity 82.89% and SCLC sensitivity 88.89%). The spectra cut-off, the kernel function and the algorithm function influence the accuracy.

Conclusion: Mid-Infrared imaging could be used to differentiate FFPE lung cancer subtypes. Supervised multivariate tools are promising to accurately separate lung tumor subtypes. The long-term perspective is to develop a spectroscopy-based diagnostic tool, revolutionizing medical differential diagnostics, especially cancer identification.