Practical issues of hyperspectral imaging analysis of solid dosage forms

Multi-level color classification of post-consumer plastic packaging flakes by hyperspectral imaging for optimizing the recycling process

In a circular economy perspective, the development of fast and efficient sensor-based recognition strategies of plastic waste, not only by polymer but also by color, plays a crucial role for the production of high quality secondary raw materials in recycling plants. In this work, mixed colored flakes of high-density polyethylene (HDPE) from packaging waste were simultaneously classified by hyperspectral imaging working in the visible range (400-750 nm), combined with machine learning. Two classification models were built and compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE macro-color classes identification (i.e., white, blue, green, red, orange and yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the different HDPE color tones, providing as output 14 color classes. The obtained classification results were excellent for both models, with values of Recall, Specificity, Accuracy, and F-score in prediction close to 1. The proposed methodological approach can be utilized as sensor-based sorting logic in plastic recycling plants, tuning the output based on the required needs of the recycling plant, allowing to obtain a high-quality recycled HDPE of different colors, optimizing the plastic recycling process, in agreement with the principles of circular economy.

Near-infrared hyperspectral image analysis for monitoring the cheese-ripening process

Ripening is the most crucial process step in cheese manufacturing and constitutes multiple biochemical alterations that describe the final cheese quality and its perceived sensory attributes. The assessment of the cheese-ripening process is challenging and requires the effective analysis of a multitude of biochemical changes occurring during the process. This study monitored the biochemical and sensory attribute changes of paraffin wax-covered long-ripening hard cheeses (n = 79) during ripening by collecting samples at different stages of ripening. Near-infrared hyperspectral (NIR-HS) imaging, together with free amino acid, chemical composition, and sensory attributes, was studied to monitor the biochemical changes during the ripening process. Orthogonal projection-based multivariate calibration methods were used to characterize ripening-related and orthogonal components as well as the distribution map of chemical components. The results approve the NIR-HS imaging as a rapid tool for monitoring cheese maturity during ripening. Moreover, the pixelwise evaluation of images shows the homogeneity of cheese maturation at different stages of ripening. Among the chemical compositions, fat content and moisture are the most important variables correlating to NIR-HS images during the ripening process.

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Exploring the effect of raw material properties on continuous twin-screw wet granulation manufacturability

Twin-screw wet granulation (TSWG) stands out as a promising continuous alternative to conventional batch fluid bed- and high shear wet granulation techniques. Despite its potential, the impact of raw material properties on TSWG processability remains inadequately explored. Furthermore, the absence of supportive models for TSWG process development with new active pharmaceutical ingredients (APIs) adds to the challenge. This study tackles these gaps by introducing four partial least squares (PLS) models that approximate both the applicable liquid-to-solid (L/S) ratio range and resulting granule attributes (i.e., granule size and friability) based on initial material properties. The first two PLS models link the lowest and highest applicable L/S ratio for TSWG, respectively, with the formulation blend properties. The third and fourth PLS models predict the granule size and friability, respectively, from the starting API properties and applied L/S ratio for twin-screw wet granulation. By analysing the developed PLS models, water-related material properties (e.g., solubility, wettability, dissolution rate), as well as density and flow-related properties (e.g., flow function coefficient), were found to be impacting the TSWG processability. In addition, the applicability of the developed PLS models was evaluated by using them to propose suitable L/S ratio ranges (i.e., resulting in granules with the desired properties) for three new APIs and related formulations followed by an experimental validation thereof. Overall, this study helped to better understand the effect of raw material properties upon TSWG processability. Moreover, the developed PLS models can be used to propose suitable TSWG process settings for new APIs and hence reduce the experimental effort during process development.

Qualitative and Quantitative Analysis of Azithromycin as Solid Dosage by Raman Spectroscopy

Active pharmaceutical ingredients (APIs) and excipients are main drug constituents that ought to be identified qualitatively and quantitatively. Raman spectroscopy is aimed to be an efficient technique for pharmaceutical analysis in solid dosage forms. This technique can successfully be used in terms of qualitative and quantitative analysis of pharmaceutical drugs, their APIs, and excipients. In the proposed research, Raman spectroscopy has been employed to quantify Azithromycin based on its distinctive Raman spectral features by using commercially prepared formulations with altered API concentrations and excipients as well. Along with Raman spectroscopy, principal component analysis and partial least squares regression (PLSR), two multivariate data analysis techniques have been used for the identification and quantification of the API. For PLSR, goodness of fit of the model (R2) was found to be 0.99, whereas root mean square error of calibration was 0.46 and root mean square error of prediction was 2.42, which represent the performance of the model. This study highlights the efficiency of Raman spectroscopy in the field of pharmaceutics by preparing pharmaceutical formulations of any drug to quantify their API and excipients to compensate for the commercially prepared concentrations.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

High-speed scanless entire bandwidth mid-infrared chemical imaging

Mid-infrared spectroscopy probes molecular vibrations to identify chemical species and functional groups. Therefore, mid-infrared hyperspectral imaging is one of the most powerful and promising candidates for chemical imaging using optical methods. Yet high-speed and entire bandwidth mid-infrared hyperspectral imaging has not been realized. Here we report a mid-infrared hyperspectral chemical imaging technique that uses chirped pulse upconversion of sub-cycle pulses at the image plane. This technique offers a lateral resolution of 15 µm, and the field of view is adjustable between 800 µm × 600 µm to 12 mm × 9 mm. The hyperspectral imaging produces a 640 × 480 pixel image in 8 s, which covers a spectral range of 640-3015 cm^-1, comprising 1069 wavelength points and offering a wavenumber resolution of 2.6-3.7 cm^-1. For discrete frequency mid-infrared imaging, the measurement speed reaches a frame rate of 5 kHz, the repetition rate of the laser. As a demonstration, we effectively identified and mapped different components in a microfluidic device, plant cell, and mouse embryo section. The great capacity and latent force of this technique in chemical imaging promise to be applied to many fields such as chemical analysis, biology, and medicine.

Characterization of Pharmaceutical Tablets by X-ray Tomography

Solid dosage forms such as tablets are extensively used in drug administration for their simplicity and large-scale manufacturing capabilities. High-resolution X-ray tomography is one of the most valuable non-destructive techniques to investigate the internal structure of the tablets for drug product development as well as for a cost effective production process. In this work, we review the recent developments in high-resolution X-ray microtomography and its application towards different tablet characterizations. The increased availability of powerful laboratory instrumentation, as well as the advent of high brilliance and coherent 3rd generation synchrotron light sources, combined with advanced data processing techniques, are driving the application of X-ray microtomography forward as an indispensable tool in the pharmaceutical industry.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Monitorization of Varietal Aroma Composition Dynamics during Ripening in Intact Vitis vinifera L. Tempranillo Blanco Berries by Hyperspectral Imaging

The measurement of aromatic maturity during grape ripening provides very important information for determining the harvest date, particularly in white cultivars. However, there are currently no tools that allow this measurement to be carried out in a noninvasive and rapid way. For this reason, in the present work, we have studied the use of hyperspectral imaging (HSI)) to estimate the aromatic composition of Vitis vinifera L. Tempranillo Blanco berries during ripening. A total of 236 spectra in the VIS+short wave near-infrared (VIS+SW-NIR) range (400-1000 nm) of intact berries were acquired contactless under laboratory conditions. As gold standard values, a total of 20 volatile compounds were quantified by gas chromatography-mass spectrometry (GC-MS), and the concentration of total soluble solids (TSS) was measured by refractometry. Calibration, cross-validation, and prediction models were built using partial least squares (PLS). Values of R_CV² ≥ 0.70 were obtained for α-terpineol, p-cymene, β-damascenone, β-ionone, benzaldehyde, benzyl alcohol, hexanal, citral, linalool, 2-phenylethanol, octanoic acid, nonanoic acid, 2-hexenal, 2-hexen-1-ol, (Z)-3-hexen-1-ol, total C₁₃ norisoprenoids, total C6 compounds, total positive compounds (i.e., the sum of all families except C6 compounds), total benzenoids, and total soluble solids (TSS). Therefore, it can be affirmed that HSI in the VIS + SW-NIR range could be a good tool to estimate the aromatic composition of Tempranillo Blanco grape berries in a contactless, fast, and nondestructive way.

Research progress on the application of spectral imaging technology in pharmaceutical tablet analysis

Tablet as a traditional dosage form in pharmacy has the advantages of accurate dosage, ideal dissolution and bioavailability, convenient to carry and transport. The most concerned tablet quality attributes include active pharmaceutical ingredient (API) contents and polymorphic forms, components distribution, hardness, density, coating state, dissolution behavior, etc., which greatly affect the bioavailability and consistency of tablet final products. In the pharmaceutical industry, there are usually industry standard methods to analyze the tablet quality attributes. However, these methods are generally time-consuming and laborious, and lack a comprehensive understanding of the properties of tablets, such as spatial information. In recent years, spectral imaging technology makes up for the shortcomings of traditional tablet analysis methods because it provides non-contact and rich information in time and space. As a promising technology to replace the traditional tablet analysis methods, it has attracted more and more attention. The present paper briefly describes a series of spectral imaging techniques and their applications in tablet analysis. Finally, the possible application prospect of this technology and the deficiencies that need to be improved were also prospected.

Novel aspects of Raman spectroscopy in skin research

The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.

Detection of chicken and fat adulteration in minced lamb meat by VIS/NIR spectroscopy and chemometrics methods

Meat fraud has been changed to an important challenge to both industry and governments because of the public health issue. The main purpose of this research was to inspect the possibility of using VIS/NIR spectroscopy, combined with chemometric techniques to detect the adulteration of chicken meat and fat in minced lamb meat. 180 samples of pure lamb, chicken and fat and adulterated samples at different levels: 5, 10, 15 and 20% (w/w) were prepared and analyzed after pre-processing techniques. In order to remove additive and multiplicative effects in spectral data, derivatives and scatter-correction preprocessing methods were applied. Principle Component Analysis (PCA) as unsupervised method was applied to compress data. Moreover, Support Vector Machine (SVM) and Soft Independent Modeling Class Analogies (SIMCA) as supervised methods was applied to estimate the discrimination power of these models for nine and three class datasets. The best classification results were 56.15 and 80.70% for classification of nine class and three class datasets respectively with SVM model. This study shows the applicability of VIS/NIR combined with chemometrics to detect the type of fraud in minced lamb meat.

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview

Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.

Evaluation of the Miscibility of Novel Cocoa Butter Equivalents by Raman Mapping and Multivariate Curve Resolution-Alternating Least Squares

Cocoa butter (CB) is an ingredient traditionally used in the manufacturing of chocolates, but its availability is decreasing due to its scarcity and high cost. For this reason, other vegetable oils, known as cocoa butter equivalents (CBE), are used to replace CB partially or wholly. In the present work, two Peruvian vegetable oils, coconut oil (CNO) and sacha inchi oil (SIO), are proposed as novel CBEs. Confocal Raman microscopy (CRM) was used for the chemical differentiation and polymorphism of these oils with CB based on their Raman spectra. To analyze their miscibility, two types of blends were prepared: CB with CNO, and CB with SIO. Both were prepared at 5 different concentrations (5%, 15%, 25%, 35%, and 45%). Raman mapping was used to obtain the chemical maps of the blends and analyze their miscibility through distribution maps, histograms and relative standard deviation (RSD). These values were obtained with multivariate curve resolution-alternating least squares. The results show that both vegetable oils are miscible with CB at high concentrations: 45% for CNO and 35% for SIO. At low concentrations, their miscibility decreases. This shows that it is possible to consider these vegetable oils as novel CBEs in the manufacturing of chocolates.

Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based on HSI in the near infrared range (HSI-NIR) to distinguish conventional from transgenic cotton seeds. Three different chemometric approaches, one pixel-based and two object-based, using partial least squares discriminant analysis (PLS-DA) were built and their performances were compared considering the pros and cons of each approach. Specificity and sensitivity values ranged from 0.78-0.92 and 0.62-0.93, respectively, for the different approaches.

Evaluation of Transmission Raman spectroscopy and NIR Hyperspectral Imaging for the assessment of content uniformity in solid oral dosage forms✰

PURPOSE

The objective of the present study was to explore and compare fast and non-destructive Transmission Raman Spectroscopy (TRS) and Near Infrared Hyperspectral imaging (NIR HSI) for the development of predictive quantitative methods to determine content uniformity (CU) of tablets.

METHODS

A set of single Active Pharmaceutical Ingredients (API) tablets with nine concentration levels of caffeine ranging from 12.75%w/w to 17.75%w/w and another set of double API tablets with five concentration levels of model API A* (5.25%w/w - 9.25%w/w) and caffeine (7%w/w - 13%w/w) were prepared. Chemometric prediction models were developed using partial least square (PLS 1) and later tested using a test set for both single and double API tablets.

RESULTS

Calibration PLS1 models were developed for both single and double APIs using a combination of S-G 1st derivative and SNV data pre-processing steps that offer an optimal model performance with the lowest cross-validation error and bias. The root mean square error of prediction (RMSEP) for the PLS1 model for single API caffeine tablets using TRS and NIR HSI was 0.27% and 0.36% respectively. The RMSEP for the PLS1 models built using TRS for the double API tablets was 0.29% for API A and 0.34% for caffeine. Similarly, for the NIR HIS prediction models the RMSEP was 0.43% for API A and 0.56% for caffeine.

CONCLUSION

Overall TRS presented a 25-30% more accurate prediction capability compared to NIR HSI in this specific sample sets. Nevertheless, both TRS ad NIR HSI possess the potential to be employed as rapid, nondestructive techniques to replace classical wet- chemistry methods for at- or off-line determination of tablet CU.

Lipid distribution on ethnic hairs by Fourier transform infrared synchrotron spectroscopy

BACKGROUND

A synchrotron-based Fourier transform infrared micro-spectrometer (μ-FTIR) allows the spatial determination of lipids across the different layers of ethnic hairs and differentiates between the lipid order arrangement and quantity.

MATERIALS AND METHODS

The three ethnic fibers were delipidized, the lipid extracts were characterized, and the delipidized fibers were studied by dynamic vapor sorption experiments (DVS) and FTIR-synchrotron techniques.

RESULTS

The average spectra from the different hair regions exhibited the most intense CH₂ sym peaks on the medulla, followed by those from the cuticle and cortex for all hairs of different ethnicities. Differences in the lipid fraction of the three hair types have been observed, and they can explain some barrier properties. African virgin hair was demonstrated to have more lipids mainly in the medulla, which implies an important hydrophobicity with low hysteresis between absorption and desorption water vapor processes. In addition, these lipids are highly disordered, mainly in the cuticle, which can be related to its high water vapor diffusion. Asian and Caucasian virgin hairs presented a similar lipid order in all regions, with similar diffusion coefficients. Results indicate that the higher order of the lipid bilayer hinders water permeation kinetics in some way.

CONCLUSION

The differences in the presence and organization of the lipids in the different regions of the African hair can account for its differentiation with regards to moisturization and swelling from the other types of fibers.

Introducing ATR-FTIR Spectroscopy through Analysis of Acetaminophen Drugs: Practical Lessons for Interdisciplinary and Progressive Learning for Undergraduate Students

Infrared (IR) spectroscopy is a vibrational spectroscopic technique useful in chemical, pharmaceutical, and forensic sciences. It is essential to identify chemicals for reasons spanning from scientific research and academic practices to quality control in companies. However, in some university degrees, graduate students do not get the proficiency to optimize the experimental parameters to obtain the best IR spectra; to correlate the IR spectral bands with the molecular vibrations (chemical elucidation); to have some criteria for any substance identification (especially relevant in quality control to recognize counterfeit); and to apply chemometrics for comparing, visualizing, and classifying the IR spectra. This work presents an experimental laboratory practice for an introductory teaching of the IR instrumental conditions in the identification of substances based on visual spectra comparison and statistical analysis and matching. Then, the selected IR conditions are applied to different commercial drugs, in the solid state or in solution, mostly composed of acetaminophen. Finally, the students apply chemometrics analysis to the IR data. This practice was designed for the training in a chemistry subject for undergraduate students of the chemistry, pharmacy, or forensics degrees, among others related to science, medical, food, or technological sciences.

Performance assessment of linear iterative optimization technology (IOT) for Raman chemical mapping of pharmaceutical tablets

Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (< 5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps.

Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics

The aim of this study is to explore the feasibility of detection and quantification of two cheap adulterants (maltodextrin and starch) in Chinese functional food, hawthorn fruits powder (HFP), by using near infrared (NIR) spectroscopy coupled with chemometrics methods. The partial least squares discriminant analysis (PLS-DA) models were developed to discriminate the adulterated HFP from the authentic HFP, while the partial least squares regression (PLSR) models were employed to determine the contents of adulterants. In order to yield the best results, various spectra pretreatment methods and wavelength selection methods were carefully investigated. The models' qualities were assessed by the self-consistency test, the independent test and the rigorous leave-one-out cross-validation test. The metrics for the PLS-DA discriminative model included error rate, true positive rate, true negative rate and F1 score, while the metrics for the PLSR quantitative model were determination coefficient, root mean square error and residual prediction deviation. Finally, very satisfying results were obtained, which indicate that our method is quite robust and applicable, and thus has great potential for rapid detection of adulteration in powder of many other herbal plants or functional foods.

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions.

Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis-NIR (400-1000 nm) hyperspectral imaging

Aflatoxin-B1 contamination in maize is a major food safety issue across the world. Conventional detection technique of toxins requires highly skilled technicians and is time-consuming. Application of appropriate chemometrics along with hyperspectral imaging (HSI) can identify aflatoxin-B1 infected maize kernels. Present study was undertaken to classify 240 maize kernels inoculated with six different concentrations (25, 40, 70, 200, 300 and 500 ppb) of aflatoxin-B1 by using Vis-NIR HSI. The reflectance spectral data were pre-processed (multiplicative scatter correction (MSC), standard normal variate (SNV), Savitsky-Golay smoothing and their combinations) and classified using partial least square discriminant analysis (PLS-DA) and k-nearest neighbour (k-NN). PLS model was also developed to predict the concentration of aflatoxin-B1in naturally contaminated maize kernels inoculated with Aspergillus flavus. The potential wavelength (508 nm) was selected based on principal component analysis (PCA) loadings to distinguish between sterile and infected maize kernels. PCA score plots revealed a distinct separation of low contaminated samples (25, 40 and 70 ppb) from highly contaminated samples (200, 300 and 500 ppb) without any overlapping of data. The maximum classification accuracy of 94.7% was obtained using PLS-DA with SNV pre-processed data. Across all the combinations of pre-processing and classification models, the best efficiency (98.2%) was exhibited by k-NN model with raw data. The developed PLS model depicted good prediction accuracy ( R CV 2  = 0.820, SECV = 79.425, RPDCV = 2.382) during Venetian-blinds cross-validation. The results of pixel-wise classification (k-NN) and concentration distribution maps (PLS with raw spectra) were quite close to the result obtained by reference method (HPLC analysis) of aflatoxin-B1 detection.

Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging

In pharmaceutical wet granulation, drying is a critical step in terms of energy and material consumption, whereas granule moisture content and size are important process outcomes that determine tabletting performance. The drying process is, however, very complex due to the multitude of interacting mechanisms on different scales. Building robust physical models of this process therefore requires detailed data. Current data collection methods only succeed in measuring the average moisture content of a size fraction of granules, whereas this property rather follows a distribution that, moreover, contains information on the drying patterns. Therefore, a measurement method is devised to simultaneously characterise the moisture content and size of individual pharmaceutical granules. A setup with near-infrared chemical imaging (NIR-CI) is used to capture an image of a number of granules, in which the absorbance spectra are used for deriving the moisture content of the material and the size of the granules is estimated based on the amount of pixels containing pharmaceutical material. The quantification of moisture content based on absorption spectra is performed with two different regression methods, Partial Least Squares regression (PLSR) and Elastic Net Regression (ENR). The method is validated with particle size data for size determination, loss-on-drying (LOD) data of average moisture contents of granule samples and, finally, batch fluid bed experiments in which the results are compared to the most detailed method to date. The individual granule moisture contents confirmed again that granule size is an important factor in the drying process. The measurement method can be used to gain more detailed experimental insight in different fluidisation and particulate processes, which will allow building of robust process models.

Process analytical technology tools for process control of roller compaction in solid pharmaceuticals manufacturing

This article presents an overview of using process analytical technology in monitoring the roller compaction process. In the past two decades, near-infrared spectroscopy, near-infrared spectroscopy coupled with chemical imaging, microwave resonance technology, thermal effusivity and various particle imaging techniques have been used for developing at-, off-, on- and in-line models for predicting critical quality attributes of ribbons and subsequent granules and tablets. The common goal of all these methods is improved process understanding and process control, and thus improved production of high-quality products. This article reviews the work of several researchers in this field, comparing and critically evaluating their achievements.

Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality

Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets.

Quantitative analysis of solid dosage forms of cefixime using Raman spectroscopy

Quantification of antibiotics is of significant importance because of their use in the prevention and treatment of different diseases. Cefixime (CEF) is a cephalosporin antibiotic that is used against bacterial infections. In the present study, Raman spectroscopy has been applied for the identification and quantification of Raman spectral features of cefixime with different concentrations of Active Pharmaceutical Ingredient (API) and excipients in solid dosage forms. The changes in Raman spectral features of API and excipients in the solid dosage forms of cefixime were studied and Raman peaks were assigned based on the literature. Multivariate data analysis techniques including the Principal Component Analysis (PCA) and Partial Least Squares Regression analysis (PLSR) have been performed for the qualitative and quantitative analysis of solid dosage forms of cefixime. PCA was found helpful in differentiating all the Raman spectral data associated with the different solid dosage forms of cefixime. The coefficient of determination (R²), mean absolute error (MAE), and mean relative error (MRE) for the calibration data-set were 0.99, 0.72, and 0.01 respectively and for the validation data-set were 0.99, 3.15, and 0.02 respectively, that shows the performance of the model. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.56 mg and 3.13 mg respectively.

Practical Recommendations for Hyperspectral and Thermal Proximal Disease Sensing in Potato and Leek Fields

Thermal and hyperspectral proximal disease sensing are valuable tools towards increasing pesticide use efficiency. However, some practical aspects of the implementation of these sensors remain poorly understood. We studied an optimal measurement setup combining both sensors for disease detection in leek and potato. This was achieved by optimising the signal-to-noise ratio (SNR) based on the height of measurement above the crop canopy, off-zenith camera angle and exposure time (ET) of the sensor. Our results indicated a clear increase in SNR with increasing ET for potato. Taking into account practical constraints, the suggested setup for a hyperspectral sensor in our experiment involves (for both leek and potato) an off-zenith angle of 17°, height of 30 cm above crop canopy and ET of 1 ms, which differs from the optimal setup of the same sensor for wheat. Artificial light proved important to counteract the effect of cloud cover on hyperspectral measurements. The interference of these lamps with thermal measurements was minimal for a young leek crop but increased in older leek and after long exposure. These results indicate the importance of optimising the setup before measurements, for each type of crop.

Fiber-Array-Based Raman Hyperspectral Imaging for Simultaneous, Chemically-Selective Monitoring of Particle Size and Shape of Active Ingredients in Analgesic Tablets

The particle shape, size and distribution of active pharmaceutical ingredients (API) are relevant quality indicators of pharmaceutical tablets due to their high impact on the manufacturing process. Furthermore, the bioavailability of the APIs from the dosage form depends largely on these characteristics. Routinely, particle size and shape are only analyzed in the powder form, without regard to the effect of the formulation procedure on the particle characteristics. The monitoring of these parameters improves the understanding of the process; therefore, higher quality and better control over the biopharmaceutical profile can be ensured. A new fiber-array-based Raman hyperspectral imaging technique is presented for direct simultaneous in-situ monitoring of three different active pharmaceutical ingredients- acetylsalicylic acid, acetaminophen and caffeine- in analgesic tablets. This novel method enables a chemically selective, noninvasive assessment of the distribution of the active ingredients down to 1 µm spatial resolution. The occurrence of spherical and needle-like particles, as well as agglomerations and the respective particle size ranges, were rapidly determined for two commercially available analgesic tablet types. Subtle differences were observed in comparison between these two tablets. Higher amounts of acetaminophen were visible, more needle-shaped and bigger acetylsalicylic acid particles, and a higher incidence of bigger agglomerations were found in one of the analgesic tablets.

Quantification of Inkjet-Printed Pharmaceuticals on Porous Substrates Using Raman Spectroscopy and Near-Infrared Spectroscopy

The use of inkjet printing for pharmaceutical manufacturing is gaining interest for production of personalized dosage forms tailored to specific patients. As part of the manufacturing, it is imperative to ensure that the correct dose is printed. The aim of this study was to use inkjet printing for manufacturing of personalized dosage forms combined with the use of near-infrared (NIR) and Raman spectroscopy as complementary analytical techniques for active pharmaceutical ingredient (API) quantification of the inkjet-printed dosage forms. Three APIs, propranolol (0.5-4.1 mg), montelukast (2.1-12.1 mg), and haloperidol (0.6-4.1 mg) were inkjet printed in 1 cm² areas on a porous substrate. The printed doses were non-destructively analyzed by transmission NIR and Raman spectroscopy (both transmission and backscatter). X-ray computed microtomography (μ-CT) analysis was undertaken for porosity measurements of the substrate. The API content was confirmed using high-performance liquid chromatography (HPLC), and the content in the dosage forms was modeled from the NIR and Raman spectra using partial least squares regression (PLS). HPLC analysis revealed a linear correlation of the number of layers printed to the API content. The resulting PLS models for both NIR and Raman had R² values between 0.95 and 0.99. The best predictive model was obtained using NIR, followed by Raman spectroscopy. μ-CT revealed the substrate to be highly porous and optimal for inkjet printing. In conclusion, NIR and Raman spectroscopic techniques could be used complementary as fast API quantification tools for inkjet-printed medicines.

Analytical aspects of printed oral dosage forms

Printing technologies, both 2D and 3D, have gained considerable interest during the last years for manufacturing of personalized dosage forms, tailored to each patient. Here we review the research work on 2D printing techniques, mainly inkjet printing, for manufacturing of film-based oral dosage forms. We describe the different printing techniques and give an overview of film-based oral dosage forms produced using them. The main part of the review focuses on the non-destructive analytical methods used for evaluation of qualitative aspects of printed dosage forms, e.g., solid-state properties, as well as for quantification of the active pharmaceutical ingredient (API) in the printed dosage forms, with an emphasis on spectroscopic methods. Finally, the authors share their view on the future of printed dosage forms.

Recent advances in the applications of vibrational spectroscopic imaging and mapping to pharmaceutical formulations

Vibrational spectroscopic imaging and mapping approaches have continued in their development and applications for the analysis of pharmaceutical formulations. Obtaining spatially resolved chemical information about the distribution of different components within pharmaceutical formulations is integral for improving the understanding and quality of final drug products. This review aims to summarise some key advances of these technologies over recent years, primarily since 2010. An overview of FTIR, NIR, terahertz spectroscopic imaging and Raman mapping will be presented to give a perspective of the current state-of-the-art of these techniques for studying pharmaceutical samples. This will include their application to reveal spatial information of components that reveals molecular insight of polymorphic or structural changes, behaviour of formulations during dissolution experiments, uniformity of materials and detection of counterfeit products. Furthermore, new advancements will be presented that demonstrate the continuing novel applications of spectroscopic imaging and mapping, namely in FTIR spectroscopy, for studies of microfluidic devices. Whilst much of the recently developed work has been reported by academic groups, examples of the potential impacts of utilising these imaging and mapping technologies to support industrial applications have also been reviewed.

Quantification and spatial distribution of salicylic acid in film tablets using FT-Raman mapping with multivariate curve resolution

In this study, we proposed a rapid and sensitive method for quantification and spatial distribution of salicylic acid in film tablets using FT-Raman spectroscopy with multivariate curve resolution (MCR). For this purpose, the constituents of film tablets were identified by using FT-Raman spectroscopy, and then eight different concentrations of salicylic acid tablets were visualized by Raman mapping. MCR was applied to mapping data to expose the active pharmaceutical ingredients in the presence of other excipients by monitoring distribution maps and combination of FT-Raman mapping with MCR enabled the determination of lower salicylic acid concentrations. In addition, the distribution of major excipient, lactose, was examined in the tablet form. A calibration curve was obtained by plotting the intensity of the Raman signal at 1635 cm⁻¹ versus the concentration of salicylic acid and the correlation was found to be linear within the range of 0.5%–3.9% with a correlation coefficient of 0.99. The limit of detection for the technique was determined 0.35%. The ability of the technique to quantify salicylic acid in tablet test samples was also investigated.

Development of an analytical method for crystalline content determination in amorphous solid dispersions produced by hot-melt extrusion using transmission Raman spectroscopy: A feasibility study

The development of a quantitative method determining the crystalline percentage in an amorphous solid dispersion is of great interest in the pharmaceutical field. Indeed, the crystalline Active Pharmaceutical Ingredient transformation into its amorphous state is increasingly used as it enhances the solubility and bioavailability of Biopharmaceutical Classification System class II drugs. One way to produce amorphous solid dispersions is the Hot-Melt Extrusion (HME) process. This study reported the development and the comparison of the analytical performances of two techniques, based on backscattering and transmission Raman spectroscopy, determining the crystalline remaining content in amorphous solid dispersions produced by HME. Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were performed on preprocessed data and tended towards the same conclusions: for the backscattering Raman results, the use of the DuoScan™ mode improved the PCA and PLS results, due to a larger analyzed sampling volume. For the transmission Raman results, the determination of low crystalline percentages was possible and the best regression model was obtained using this technique. Indeed, the latter acquired spectra through the whole sample volume, in contrast with the previous surface analyses performed using the backscattering mode. This study consequently highlighted the importance of the analyzed sampling volume.