A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Non-Destructive Assessment of Chicken Egg Fertility

Total hatching egg set (for both egg production chicks and broilers) in the Agriculture and Agri-Food Canada report 2017 was over 1.0 billion. With the fertility rate for this year observed to be around 82%, there were about 180 million unhatched eggs (worth over 300 million Canadian dollars) incubated in Canada for the year 2017 alone. These non-hatching (non-fertile) eggs can find useful applications as commercial table eggs or low-grade food stock if they can be detected early and isolated accordingly preferably prior to incubation. The conventional method of chicken egg fertility assessment termed candling, is subjective, cumbersome, slow, and eventually inefficient, leading to huge economic losses. Hence, there is a need for a non-destructive, fast and online prediction technology to assist with early chicken egg fertility identification problem. This paper reviewed existing non-destructive approaches including ultrasound and dielectric measurements, thermal imaging, machine vision, spectroscopy, and hyperspectral imaging. Hyperspectral imaging was extensively discussed, being an emerging new technology with great potential. Suggestions were finally proffered towards building futuristic robust model(s) for early detection of chicken egg fertility.

Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging

Hyperspectral imaging (HSI) in the spectral range of 400-1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC₁ and PC₂) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (R_p²) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had R_p² of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

A Rational Analysis of Uniformity Risk for Agglomerated Drug Substance Using NIR Chemical Imaging

Early risk detection and quick diagnosis of manufacturing challenges are necessary to support the accelerated development pace of drug product in the current competitive environment. Analytical tools, such as near-infrared (NIR) chemical imaging (CI), can be employed for alerting drug substance uniformity risks in intermediates and the final product of solid dosage forms. In this particular study, the ability to characterize the behavior of agglomerated drug substance throughout process development was enabled by NIR CI to identify uniformity risks with small sample sizes and short turnaround time. Using NIR chemical imaging, the drug substance distribution and cluster size in all intermediates were visualized throughout the drug product process. NIR CI enabled rapid identification of the key unit operations that produced the greatest reduction in cluster size for enhanced distribution of the drug substance. The comil acted as a high shear mixing step to disperse soft lumps prior to roller compaction. Shear forces or pressure during roller compaction was sufficient to break down and disperse the agglomerates further. Ultimately, the process was robust against a range of drug substance input properties such that the uniformity of the final blend was consistently achieved and the agglomerated drug substance had no risks to the drug product process.

NIR hyperspectral imaging to evaluate degradation in captopril commercial tablets

Pharmaceutical quality control is important for improving the effectiveness, purity and safety of drugs, as well as for the prevention or control of drug degradation. In the present work, near infrared hyperspectral images (HSI-NIR) of tablets with different expiration dates were employed to evaluate the degradation of captopril into captopril disulfide in different layers, on the top and on the bottom surfaces of the tablets. Multivariate curve resolution (MCR) models were used to extract the concentration distribution maps from the hyperspectral images. Afterward, multivariate image techniques were applied to the concentration distribution maps (CDMs), to extract features and build models relating the main characteristics of the images to their corresponding manufacturing dates. Resolution methods followed by extracting features were able to estimate the tablet manufacture date with a prediction error of 120days. The model developed could be useful to evaluate whether a sample shows a degradation pattern consistent with the date of manufacturing or to detect abnormal behaviors in the natural degradation process of the sample. The information provided by the HIS-NIR is important for the development of the process (QbD), looking inside the formulation, revealing the behavior of the active pharmaceutical ingredient (API) during the product's shelf life.

Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes

Monitoring of tablet quality attributes in direct vicinity of the production process requires analytical techniques that allow fast, non-destructive, and accurate tablet characterization. The overall objective of this study was to investigate the applicability of multispectral UV imaging as a reliable, rapid technique for estimation of the tablet API content and tablet hardness, as well as determination of tablet intactness and the tablet surface density profile. One of the aims was to establish an image analysis approach based on multivariate image analysis and pattern recognition to evaluate the potential of UV imaging for automatized quality control of tablets with respect to their intactness and surface density profile. Various tablets of different composition and different quality regarding their API content, radial tensile strength, intactness, and surface density profile were prepared using an eccentric as well as a rotary tablet press at compression pressures from 20MPa up to 410MPa. It was found, that UV imaging can provide both, relevant information on chemical and physical tablet attributes. The tablet API content and radial tensile strength could be estimated by UV imaging combined with partial least squares analysis. Furthermore, an image analysis routine was developed and successfully applied to the UV images that provided qualitative information on physical tablet surface properties such as intactness and surface density profiles, as well as quantitative information on variations in the surface density. In conclusion, this study demonstrates that UV imaging combined with image analysis is an effective and non-destructive method to determine chemical and physical quality attributes of tablets and is a promising approach for (near) real-time monitoring of the tablet compaction process and formulation optimization purposes.

Hyperspectral image analysis. A tutorial

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case.

PAT: From Western solid dosage forms to Chinese materia medica preparations using NIR-CI

Near-infrared chemical imaging (NIR-CI) is an emerging technology that combines traditional near-infrared spectroscopy with chemical imaging. Therefore, NIR-CI can extract spectral information from pharmaceutical products and simultaneously visualize the spatial distribution of chemical components. The rapid and non-destructive features of NIR-CI make it an attractive process analytical technology (PAT) for identifying and monitoring critical control parameters during the pharmaceutical manufacturing process. This review mainly focuses on the pharmaceutical applications of NIR-CI in each unit operation during the manufacturing processes, from the Western solid dosage forms to the Chinese materia medica preparations. Finally, future applications of chemical imaging in the pharmaceutical industry are discussed.

Raman spectroscopy: in vivo quick response code of skin physiological status

Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R2=0.9. This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.

Characterization of semi-solid Self-Emulsifying Drug Delivery Systems (SEDDS) of atorvastatin calcium by Raman image spectroscopy and chemometrics

A methodology based on Raman image spectroscopy and chemometrics for homogeneity evaluation of formulations containing atorvastatin calcium in Gelucire(®) 44/14 is presented. In the first part of the work, formulations with high amounts of Gelucire(®) 44/14 (80%) and solvents of different polarities (diethylene glycol monoethyl ether, propyleneglycol, propylene glycol monocaprylate and glyceryl mono/dicaprylate/caprate) were prepared for miscibility screening evaluation by classical least squares (CLS). It was observed that Gelucire(®) 44/14 presented higher affinity for the lipophilic solvents glyceryl mono/dicaprylate/caprate and propylene glycol monocaprylate, whose samples were observed to be homogeneous, and lower affinity for the hydrophilic solvents diethylene glycol monoethyl ether and propyleneglycol, whose samples were heterogeneous. In the second part of the work, the ratio of glyceryl mono/dicaprylate/caprate and Gelucire(®) 44/14 was determined based on studies in water and allowed the selection of the proportions of these two excipients in the preconcentrate that provided supersaturation of atorvastatin upon dilution. The preconcentrate was then evaluated for homogeneity by partial least squares (PLS) and an excellent miscibility was observed in this proportion as well. Therefore, it was possible to select a formulation that presented simultaneously homogeneous preconcentrate and solubility enhancement in water by Raman image spectroscopy and chemometrics.