Rapid detection of viable microbes with 5-cyano-2,3-di-(p-tolyl)tetrazolium chloride and 5(6)-carboxyfluorescein diacetate using a fibre fluorescence spectroscopy system.

AIMS: To assess the efficacy of two commercially available viability dyes, 5-cyano-2,3-di-(p-tolyl)tetrazolium chloride (CTC) and 5(6)-carboxyfluorescein diacetate (CFDA), in reporting on viable cell concentration and species using an all-fibre fluorometer. METHODS AND RESULTS: Four bacterial species (two Gram-positive and two Gram-negative) commonly associated with food poisoning or food spoilage (Escherichia coli, Salmonella enterica, Staphylococcus aureus, and Bacillus cereus) were stained with CTC or CFDA and the fibre fluorometer was used to collect full fluorescence emission spectra. A good correlation between concentration and fluorescence intensity was found for Gram-negative bacteria between 107 and 108 colony-forming units (CFU) ml-1. There was no correlation with concentration for Gram-positive bacteria; however, the information in the CTC and CFDA spectra shows the potential to distinguish Gram-negative cells from Gram-positive cells, although it may simply reflect the overall bacterial metabolic activity under staining conditions from this study. CONCLUSIONS: The limit of detection (LoD) is too high in the dip-probe approach for analysis; however, the development of an approach measuring the fluorescence of single cells may improve this limitation. The development of new bacteria-specific fluorogenic dyes may also address this limitation. The ability to differentiate bacteria using these dyes may add value to measurements made to enumerate bacteria using CTC and CFDA.

Continuous separation of bacterial cells from large debris using a spiral microfluidic device.

With the global increase in food exchange, rapid identification and enumeration of bacteria has become crucial for protecting consumers from bacterial contamination. Efficient analysis requires the separation of target particles (e.g., bacterial cells) from food and/or sampling matrices to prevent matrix interference with the detection and analysis of target cells. However, studies on the separation of bacteria-sized particles and defined particles, such as bacterial cells, from heterogeneous debris, such as meat swab suspensions, are limited. In this study, we explore the use of passive-based inertial microfluidics to separate bacterial cells from debris, such as fascia, muscle tissues, and cotton fibers, extracted from ground meat and meat swabs-a novel approach demonstrated for the first time. Our objective is to evaluate the recovery efficiency of bacterial cells from large debris obtained from ground meat and meat swab suspensions using a spiral microfluidic device. In this study, we establish the optimal flow rates and Dean number for continuous bacterial cell and debris separation and a methodology to determine the percentage of debris removed from the sample suspension. Our findings demonstrate an average recovery efficiency of ∼80% for bacterial cells separated from debris in meat swab suspensions, while the average recovery efficiency from ground beef suspensions was ∼70%. Furthermore, approximately 50% of the debris in the ground meat suspension were separated from bacterial cells.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective.

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

Screening for Adulterants in Liquid Milk Using a Portable Raman Miniature Spectrometer with Immersion Probe.

A portable Raman system with an immersion fiber optic probe was assessed for point-of-collection screening for the presence of adulterants in liquid milk. N-rich adulterants and sucrose were measured in this proof-of-concept demonstration. Reproducibility, limit of detection range and other figures of merit such as specificity, sensitivity, ratio of predicted to standard deviation, standard error of prediction and root mean squared error for cross validation were determined from partial least squares (PLS) and partial least squares with discriminant analysis (PLS-DA) calibrations of milk mixtures containing 50-1000 ppm (parts per million) of melamine, ammonium sulphate, Dicyandiamide, urea and sucrose. The spectra were recorded by immersing the fiber optic probe directly in the milk solutions. Despite the high scattering background which was easily and reliably estimated and subtracted, the reproducibility for four N-rich compounds averaged to 11% residual standard deviation (RSD) and to 5% RSD for sucrose. PLS calibration models predicted the concentrations of separate validation sets with standard errors of prediction of between 44 and 76 ppm for the four N-rich compounds and 0.17% for sucrose. The sensitivity and specificity of the PLS-DA calibration were 92% and 89%, respectively. The study shows promise for use of portable mini Raman systems for routine rapid point-of-collection screening of liquid milk for the presence of adulterants, without the need for sample preparation or addition of chemicals.

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy.

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm(-1), respectively) are maintained through a thickness of 60 µm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 µm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation.

Nondestructive Assessment of Engineered Cartilage Composition by Near Infrared Spectroscopy.

Tissue engineering presents a strategy to overcome the limitations of current tissue healing methods. Scaffolds, cells, external growth factors and mechanical input are combined in an effort to obtain constructs with properties that mimic native tissues. However, engineered constructs developed using similar culture environments can have very different matrix composition and biomechanical properties. Accordingly, a nondestructive technique to assess constructs during development such that appropriate compositional endpoints can be defined is desirable. Near infrared spectroscopy (NIRS) analysis is a modality being investigated to address the challenges associated with current evaluation techniques, which includes nondestructive compositional assessment. In the present study, cartilage tissue constructs were grown using chondrocytes seeded onto polyglycolic acid (PGA) scaffolds in similar environments in three separate tissue culture experiments and monitored using NIRS. Multivariate partial least squares (PLS) analysis models of NIR spectra were calculated and used to predict tissue composition, with biochemical assay information used as the reference data. Results showed that for combined data from all tissue culture experiments, PLS models were able to assess composition with significant correlations to reference values, including engineered cartilage water (at 5200 cm(-1), R = 0.68, p = 0.03), proteoglycan (at 4310 cm(-1), R = 0.82, p = 0.007), and collagen (at 4610 cm(-1), R = 0.84, p = 0.005). In addition, degradation of PGA was monitored using specific NIRS frequencies. These results demonstrate that NIR spectroscopy combined with multivariate analysis provides a nondestructive modality to assess engineered cartilage, which could provide information to determine the optimal time for tissue harvest for clinical applications.

Raman imaging of drug delivery systems.

This review article includes an introduction to the principals of Raman spectroscopy, an outline of the experimental systems used for Raman imaging and the associated important considerations and limitations of this method. Common spectral analysis methods are briefly described and examples of interesting published studies which utilised Raman imaging of pharmaceutical and biomedical devices are discussed, along with summary tables of the literature at this point in time.

The contribution of bone and cartilage to the near-infrared spectrum of osteochondral tissue.

Near-infrared (NIR) spectroscopy has been used to assess hyaline cartilage quality in human and animal osteochondral tissues. However, due to the lack of NIR signal from bone phosphate and the relatively deep penetration depth of the radiation, the separate contributions of cartilage and bone to the spectral signatures have not been well defined. The objectives of the current study were (1) to improve the understanding of the contributions of bone and cartilage to NIR spectra acquired from osteochondral tissue and (2) to assess the ability of this nondestructive method to predict cartilage thickness and modified Mankin grade of human tibial plateau articular cartilage. Near-infrared spectra were acquired from samples of bovine bone and cartilage with varying thicknesses and from 22 tibial plateaus harvested from patients undergoing knee replacement surgery. The spectra were recorded from regions of the tibial plateaus with varying degrees of degradation, and the cartilage thickness and modified Mankin grade of these regions were assessed histologically. The spectra from bone and cartilage samples of known thicknesses were investigated to identify spectral regions that were distinct for these two tissues. Univariate and multivariate linear regression methods were used to correlate modified Mankin grade and cartilage thickness with NIR spectral changes. The ratio of the NIR absorbances associated with water at 5270 and 7085 cm(-1) was the best differentiator of cartilage and bone spectra. The NIR prediction models for thickness and Mankin grade calculated using partial least squares regression were more accurate than were univariate-based prediction models, with a root mean square errors of cross-validation of 0.42 mm (for thickness) and 1.3 (for modified Mankin grade). We conclude that NIR spectroscopy may be used to simultaneously assess articular cartilage thickness and modified Mankin grade, based in part on differences in spectral contributions from bone and cartilage.

Assessment of hyaline cartilage matrix composition using near infrared spectroscopy.

Changes in the composition of the extracellular matrix (ECM) are characteristic of injury or disease in cartilage tissue. Various imaging modalities and biochemical techniques have been used to assess the changes in cartilage tissue but lack adequate sensitivity, or in the case of biochemical techniques, result in destruction of the sample. Fourier transform near infrared (FT-NIR) spectroscopy has shown promise for the study of cartilage composition. In the current study NIR spectroscopy was used to identify the contributions of individual components of cartilage in the NIR spectra by assessment of the major cartilage components, collagen and chondroitin sulfate, in pure component mixtures. The NIR spectra were obtained using homogenous pellets made by dilution with potassium bromide. A partial least squares (PLS) model was calculated to predict composition in bovine cartilage samples. Characteristic absorbance peaks between 4000 and 5000 cm(-1) could be attributed to components of cartilage, i.e. collagen and chondroitin sulfate. Prediction of the amount of collagen and chondroitin sulfate in tissues was possible within 8% (w/dw) of values obtained by gold standard biochemical assessment. These results support the use of NIR spectroscopy for in vitro and in vivo applications to assess matrix composition of cartilage tissues, especially when tissue destruction should be avoided.

Fourier transform infrared spectroscopy to quantify collagen and elastin in an in vitro model of extracellular matrix degradation in aorta.

Extracellular matrix (ECM) is a key component and regulator of many biological tissues including aorta. Several aortic pathologies are associated with significant changes in the composition of the matrix, especially in the content, quality and type of aortic structural proteins, collagen and elastin. The purpose of this study was to develop an infrared spectroscopic methodology that is comparable to biochemical assays to quantify collagen and elastin in aorta. Enzymatically degraded porcine aorta samples were used as a model of ECM degradation in abdominal aortic aneurysm (AAA). After enzymatic treatment, Fourier transform infrared (FTIR) spectra of the aortic tissue were acquired by an infrared fiber optic probe (IFOP) and FTIR imaging spectroscopy (FT-IRIS). Collagen and elastin content were quantified biochemically and partial least squares (PLS) models were developed to predict collagen and elastin content in aorta based on FTIR spectra. PLS models developed from FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (RMSE of validation = 8.4% and 11.1% of the range respectively), and IFOP spectra were successfully used to predict elastin content (RMSE = 11.3% of the range). The PLS regression coefficients from the FT-IRIS models were used to map collagen and elastin in tissue sections of degraded porcine aortic tissue as well as a human AAA biopsy tissue, creating a similar map of each component compared to histology. These results support further application of FTIR spectroscopic techniques for evaluation of AAA tissues.

Vibrational spectroscopic analyses of unique yellow feather pigments (spheniscins) in penguins.

Many animals extract, synthesize and refine chemicals for colour display, where a range of compounds and structures can produce a diverse colour palette. Feather colours, for example, span the visible spectrum and mostly result from pigments in five chemical classes (carotenoids, melanins, porphyrins, psittacofulvins and metal oxides). However, the pigment that generates the yellow colour of penguin feathers appears to represent a sixth, poorly characterized class of feather pigments. This pigment class, here termed 'spheniscin', is displayed by half of the living penguin genera; the larger and richer colour displays of the pigment are highly attractive. Using Raman and mid-infrared spectroscopies, we analysed yellow feathers from two penguin species (king penguin, Aptenodytes patagonicus; macaroni penguin, Eudyptes chrysolophus) to further characterize spheniscin pigments. The Raman spectrum of spheniscin is distinct from spectra of other feather pigments and exhibits 17 distinctive spectral bands between 300 and 1700 cm(-1). Spectral bands from the yellow pigment are assigned to aromatically bound carbon atoms, and to skeletal modes in an aromatic, heterocyclic ring. It has been suggested that the penguin pigment is a pterin compound; Raman spectra from yellow penguin feathers are broadly consistent with previously reported pterin spectra, although we have not matched it to any known compound. Raman spectroscopy can provide a rapid and non-destructive method for surveying the distribution of different classes of feather pigments in the avian family tree, and for correlating the chemistry of spheniscin with compounds analysed elsewhere. We suggest that the sixth class of feather pigments may have evolved in a stem-lineage penguin and endowed modern penguins with a costly plumage trait that appears to be chemically unique among birds.

Studies on the lipase-induced degradation of lipid-based drug delivery systems. Part II - Investigations on the mechanisms leading to collapse of the lipid structure.

It has recently been found that lipid composition appears to have a major influence on the rate of lipase-induced degradation of lipid-based extended release drug delivery systems (microparticles, compressed implants and extrudated implants). Previously, we have found that during lipase incubation, depending on the lipid used, lipidic extrudates can lose their physical strength and collapse generating lipid particles in the µm-range. The aim of this study was to characterise the processes leading to collapse of solid lipid-based drug delivery systems during in vitro lipase incubation. Compressed lipid implants were used as model systems. Free fatty acids (FFA) generated in the incubation experiments were derivatised and subsequently analysed via reversed phase-HPLC in order to characterise the degradation behaviour of single lipid components (glyceryltrilaurate (D112), glyceryltrimyristate (D114), glyceryltripalmitate (D116) and glyceryltristearate (D118)) used for the preparation of compressed lipid implants. Further, Raman spectroscopy/microscopy, differential scanning calorimetry, scanning electron and light microscopy were used to investigate the physical and chemical changes in the implants upon lipase incubation. This study revealed that the lipid component D112 plays a major role in the degradation and erosion processes occurring during lipase incubation of lipid implants. The production of D112/lauric acid mixtures, with a melting point below human body temperature, leads to lipid matrices melting and losing their physical integrity.

Influence of grain topography on near infrared hyperspectral images.

Near infrared hyperspectral imaging (NIR-HSI) allows spatially resolved spectral information to be collected without sample destruction. Although NIR-HSI is suitable for a broad range of samples, sizes and shapes, topography of a sample affects the quality of near infrared (NIR) measurements. Single whole kernels of three cereals (barley, wheat and sorghum), with varying topographic complexity, were examined using NIR-HSI. The influence of topography (sample shape and texture) on spectral variation was examined using principal component analysis (PCA) and classification gradients. The greatest source of variation for all three grain types, despite spectral preprocessing with standard normal variate (SNV) transformation, was kernel curvature. Only 1.29% (PC5), 0.59% (PC6) and 1.36% (PC5) of the spectral variation within the respective barley, wheat and sorghum image datasets was explained within the principal component (PC) associated with the chemical change of interest (loss of kernel viability). The prior PCs explained an accumulated total of 91.18%, 89.43% and 84.39% of spectral variance, and all were influenced by kernel topography. Variation in sample shape and texture relative to the chemical change of interest is an important consideration prior to the analysis of NIR-HSI data for non-flat objects.

Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics.

Undesired germination of cereal grains diminishes process utility and economic return. Pre-germination, the term used to describe untimely germination, leads to reduced viability of a grain sample. Accurate and rapid identification of non-viable grain is necessary to reduce losses associated with pre-germination. Viability of barley, wheat and sorghum grains was investigated with near-infrared hyperspectral imaging. Principal component analyses applied to cleaned hyperspectral images were able to differentiate between viable and non-viable classes in principal component (PC) five for barley and sorghum and in PC6 for wheat. An OH stretching and deformation combination mode (1,920-1,940 nm) featured in the loading line plots of these PCs; this water-based vibrational mode was a major contributor to the viable/non-viable differentiation. Viable and non-viable classes for partial least squares-discriminant analysis (PLS-DA) were assigned from PC scores that correlated with incubation time. The PLS-DA predictions of the viable proportion correlated well with the viable proportion observed using the tetrazolium test. Partial least squares regression analysis could not be used as a source of contrast in the hyperspectral images due to sampling issues.

Analysis of matrix dosage forms during dissolution testing using raman microscopy.

Matrix dosage forms are widely used for sustained drug release. As both the distribution of the matrix components and physical changes during dissolution can impact drug release behavior, a comprehensive investigation of these phenomena is required during matrix development. In this study, Raman microscopy was used to investigate different extrudate formulations in terms of component distribution and structural changes during dissolution testing. Two systems containing the model drug theophylline anhydrate were investigated: a binary system, based on a tripalmitin matrix, and a ternary system, containing tripalmitin and polyethylene glycol. The distribution of the drug and the soluble and insoluble matrix components were mapped during dissolution testing. Although a receding drug boundary was observed, it was not uniformly distant from the matrix edge. The lipid structure remained intact, whereas the water-soluble polymer rapidly dissolved and diffused from the matrix leaving a more extensive network of channels through which the dissolution medium could penetrate and the drug could diffuse. Raman mapping can be considered a useful aid in the direct analysis of multiple matrix components during drug release, and therefore a deeper understanding of factors affecting drug release can be obtained during the development of sustained-release matrices.

A review of triticale uses and the effect of growth environment on grain quality.

Triticale (× Triticosecale sp. Wittmack ex A. Camus 1927) is an anthropogenic cereal designed to incorporate the functionality and high yield of wheat (Triticum spp. Linnaeus 1753) and durability of rye (Secale cereale Linnaeus 1753). The potential of triticale has remained largely unrealised, and in the 135 years since A. Stephen Wilson first crossed wheat and rye, triticale has mostly been used as animal feed. Growing demand for food resources has led to an increased interest in triticale development. Efforts to breed cultivars appropriate for baking have met with difficulty, although relatively new approaches to triticale end-use propose greater applicability for human consumption. Further, environmental awareness has generated interest in the use of triticale within biofuel production. We review environmental and genetic effects on triticale yield with a view towards increased demand on a hardy and useful cereal crop. We find triticale could satisfy many of the hopes originally placed upon it, and may be useful in foodstuffs and fuel, but only when growth environment is carefully considered.

Raman mapping of pharmaceuticals.

Raman spectroscopy may be implemented through a microscope to provide fine scale axial and lateral chemical maps. The molecular structure of many drugs makes Raman spectroscopy particularly well suited to the investigation of pharmaceutical systems. Chemometric methods currently used to assess bulk Raman spectroscopic data are typically applied to Raman mapping data from pharmaceuticals; few reports exist where the spatial information inherent to a mapped dataset is used for the calculation of chemical maps. Both univariate and multivariate methods have been applied to Raman mapping data to determine the distribution of active pharmaceutical ingredients (APIs) in tablets, solid dispersions for increased solubility and controlled release devices. The ability to axially (depth) profile using Raman mapping has been used in studies of API penetration through membranes, cellular uptake of drug delivery liposomes, and initial API distribution and subsequent elution from coatings of medical devices. New instrumental developments will increase the efficiency of Raman mapping and lead to greater utilisation of Raman mapping for analyses of pharmaceutical systems.

Investigating the relationship between drug distribution in solid lipid matrices and dissolution behaviour using Raman spectroscopy and mapping.

In this study, in situ and mapping Raman spectroscopic measurements were used to investigate the physical structure of solid lipid extrudates and relate the structure to dissolution behaviour. Theophylline anhydrate was extruded with tripalmitin, with and without the water-soluble polymer, polyethylene glycol 10000. Raman mapping of the extrudate cores revealed that drug particles of diverse size were dispersed in a continuous lipid phase with or without polyethylene glycol. At the surface, there was evidence of more mixing between the components. Previous characterisation by other methods suggested that the extrudate surface is covered predominantly by lipid, and the Raman mapping suggested that such a layer is in general less than a few micrometres thick. Nevertheless, the lipid layer dramatically reduced the drug dissolution rate. The extrudate cores were also mapped after a period of dissolution testing, and there was no evidence of a uniformly receding drug boundary in the extrudates during drug release. In situ Raman spectroscopy analysis during dissolution testing revealed that the drug distribution in the extrudate affected the formation of theophylline monohydrate. However, the drug release rate was primarily determined directly by drug distribution, with the solid-state behaviour of the drug having a smaller influence.

Quantification of Process Induced Disorder in Milled Samples Using Different Analytical Techniques.

The aim of this study was to compare three different analytical methods to detect and quantify the amount of crystalline disorder/ amorphousness in two milled model drugs. X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC) and Raman spectroscopy were used as analytical methods and indomethacin and simvastatin were chosen as the model compounds. These compounds partly converted from crystalline to disordered forms by milling. Partial least squares regression (PLS) was used to create calibration models for the XRPD and Raman data, which were subsequently used to quantify the milling-induced crystalline disorder/ amorphousness under different process conditions. In the DSC measurements the change in heat capacity at the glass transition was used for quantification. Differently prepared amorphous indomethacin standards (prepared by either melt quench cooling or cryo milling) were compared by principal component analysis (PCA) to account for the fact that the choice of standard ultimately influences the quantification outcome. Finally, the calibration models were built using binary mixtures of crystalline and quench cooled amorphous drug materials. The results imply that the outcome with respect to crystalline disorder for milled drugs depends on the analytical method used and the calibration standard chosen as well as on the drug itself. From the data presented here, it appears that XRPD tends to give a higher percentage of crystalline disorder than Raman spectroscopy and DSC for the same samples. For the samples milled under the harshest milling conditions applied (60 min, sixty 4 mm balls, 25 Hz) a crystalline disorder/ amorphous content of 44.0% (XRPD), 10.8% (Raman spectroscopy) and 17.8% (DSC) were detected for indomethacin. For simvastatin 18.3% (XRPD), 15.5% (Raman spectroscopy) and 0% (DSC, no glass transition) crystalline disorder/ amorphousness were detected.

Understanding the solid-state forms of fenofibrate--a spectroscopic and computational study.

The aim of this study was to investigate the structure of different solid-state forms of fenofibrate, a drug that lacks strong intermolecular interactions such as hydrogen bonding. In addition to a structural analysis of crystalline and amorphous fenofibrate using infrared and Raman spectroscopy combined with density functional theory calculations [B3LYP 6-31G(d)], solid-state changes that occur upon recrystallization of amorphous fenofibrate were monitored and described using in situ Raman spectroscopy. A comparison of the calculated vibrational spectra of a fenofibrate monomer and two dimer structures with the experimental vibrational spectra of crystalline and amorphous fenofibrate revealed conformational differences in the orientation of the two benzyl rings in the fenofibrate molecule and structural differences between the different solid-state forms in aliphatic parts of the drug molecule. The spectroscopic analysis suggests that non-hydrogen-bonded drug molecules are likely to exhibit more random molecular orientations and conformations in the amorphous phase since the weak intermolecular interactions that occur between such molecules can easily be disrupted. In situ Raman spectroscopy and multivariate analysis revealed multiple solid-state forms of fenofibrate, including the metastable crystalline form II, which were structurally analyzed with reference to the quantum chemical calculations. Overall, the study showed that vibrational spectroscopy, multivariate analysis, and quantum chemical modeling are well suited to investigate and characterize the structure of drug substances that exhibit only small structural differences between different solid-state forms.