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Scott test is a simple, rapid, and low-cost preliminary test used extensively to suggest the presence of cocaine in drug seizures due to the development of a blue color. However, the presence of cutting agents can compromise the test result and may suggest the presence of cocaine when the drug is absent. This study evaluated the frequency of these results and the spectral behavior and color development of false positive substances. Furthermore, this study proposes the application of the partial least squares discriminant analysis (PLS-DA) method associated with photographic images obtained by a smartphone camera to increase the selectivity of the Scott test. For the first time, a study considered a diverse set of 173 samples, 126 of them from police drug seizures. The multivariate model presented a 100% hit rate for both the set of training samples and the test set. Thus, zero false positive (classified as positive in the absence of cocaine) and false negative (negative in the presence of cocaine) rates were achieved. Therefore, the proposed methodological alternative is promising, simple, low-cost, portable, and considerably increases the assertiveness of the preliminary test for researching cocaine.

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The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

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In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control.

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In this work, high-performance thin-layer chromatography (HPTLC) coupled with multivariate image analysis (MIA) is proposed as a fast and reliable tool for authentication and adulteration detection of Iranian saffron samples based on their HPTLC fingerprints. At first, the secondary metabolites of saffron were extracted using ultrasonic-assisted solvent extraction (UASE) which was optimized using central composite design (CCD). Next, the RGB coordinates of HPTLC images were used for estimation of saffron origin based on principal component analysis (PCA). The PCA scores plot showed that saffron samples were clustered into two clear-cut groups which was 92% matched with the geographical origins of the samples. In the next step, common plant-derived adulterants of saffron including safflower, saffron style, calendula, and rubia were investigated with MIA analysis of HPTLC images using partial least squares-discriminant analysis (PLS-DA) at 5-35% (w/w) levels. The PLS-DA results showed proper classification of saffron and adulterants with sensitivity 99.14%, specificity 96.94%, error rate 1.96% and accuracy 98.04. Also, the effect of HPTLC injection volume on the performance of the proposed strategy was evaluated. The ability of the proposed method was then investigated by analyzing two additional sample sets including mixed samples of four plant-derived adulterants and adulterated commercial samples. Sensitivity and specificity of this model were 100% which confirmed its validity.

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BACKGROUND: This study reports the use of multivariate time and image analysis of avalanche videographic data for quantitative visual modelling of mixability. Its usefulness, in mechanistically modelling a powder's rheological behavior in relation to mixing, was evaluated. METHODS: Particle size distribution (PSD) of a pharmaceutical grade lactose powder was modified to reflect commercially encountered variability. The PSD variants were rheologically distinct and had different mixability. Avalanche testing was performed on the modified lactose powders. Avalanche rheological properties (ARP) profiles and videos were collected for numerical and quantitative visual modelling, respectively. In quantitative visual modelling, videos captured were transformed into serial projected images. Important features of the projected images were extracted as eigen-images, to derive the avalanche rheological visual metric (ARVM). Mixability was modelled as a function of ARP or ARVM and the rotation speed. RESULTS: Relative to the ARP model, the ARVM models were highly interpretable. As a univariate expression of ARP, ARVM also possessed construct validity (r2 greater than 0.99, slope ≥ 0.96). Important rheological features of the lactose powders were holistically visualized within a single eigen-image which enabled the generation of simpler models (5 versus 34 variables for ARP model). The ARVM models predicted mixability of lactose powders with greater accuracy than the ARP model (relative root mean square error of external validation ≤ 3.30% versus 4.96%). CONCLUSIONS: Quantitative visual modelling is a viable alternative to purely numerical approaches. Most significantly, the model's interpretability and concreteness enable manufacturers to readily understand the risk posed by PSD variability on manufacturing processes and swiftly take pre-emptive actions, without being mired in multivariate data complexity. In addition, the use of quantitative visual approach in time series imaging, for studying and monitoring industrial processes, could also be explored.

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From a circular economy perspective, feeding livestock with food leftovers or former foodstuff products (FFPs) could be an effective option aimed at exploiting food leftover resources and reducing food losses. FFPs are valuable energy sources, characterised by a beneficial starch/sugar content, and also fats. However, besides these nutritional aspects, safety is a key concern given that FFPs are generally derived from packaged food. Packaging materials, such as plastics and paper, are not accepted as a feed ingredient which means that residues should be rigorously avoided. A sensitive and objective detection method is thus essential for an accurate risk evaluation throughout the former food production chain. To this end, former food samples were collected in processing plants of two different European countries and subjected to multivariate analysis of red, green, and blue (RGB) microscopic images, in order to evaluate the possible application of this non-destructive technique for the rapid detection of residual particles from packaging materials. Multivariate Image Analysis (MIA) was performed on single images at the pixel level, which essentially consisted in an exploratory analysis of the image data by means of Principal Component Analysis, which highlighted the differences between packaging and foodstuff particles, based on their colour. The whole dataset of images was then analysed by means of a multivariate data dimensionality reduction method known as the colourgrams approach, which identified clusters of images sharing similar features and also highlighted outlier images due to the presence of packaging particles. The results obtained in this feasibility study demonstrated that MIA is a promising tool for a rapid automated method for detecting particles of packaging materials in FFPs.

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The utility of modulating rotation speed in tumble mixing and its mechanistic interplay with particle size distribution (PSD) variability in excipients remain underexplored. They were investigated in this study. For the present purpose, PSD of a commercial grade lactose was modified to reflect commercially relevant variations; and mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender, at various rotation speeds. Model of mixing was constructed using avalanche rheological properties and was also rendered as quantifiable visual models using avalanche rheological visual metric (ARVM), to uncover mechanistic relationships. ARVM was derived through multivariate image analysis of avalanche flow. It was observed that increasing rotation speed reduced the number of rotations needed to achieve blend homogeneity by 30-33% for PSD variants with 16-20% fines, while increasing the number of rotations by 134% in PSD variants with less than 15% fines (p ≈ 0.00). ARVM successfully modelled (root mean square error of external validation = 2.46%) and revealed the mechanistic interplay. With increased proportion of fines, lactose exhibited quasi-parabolic motion with disaggregation of soft agglomerates and improved mixing. With decreased proportion of fines, lactose flowed as coherent wave-like masses with imperceptible dispersive tendency and increased dilation, which impeded mixing. In conclusion, this study contributes to process understanding and ideas for designing robust mixing operations. It showcases the usefulness of a quantitative visual approach, exemplified by the ARVM, to evaluate material variability and uncover its mechanistic impact on processing.

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Two separate artificial sensors, an electronic eye (EE) and an electronic tongue (ET), were recently developed to monitor grape ripening based on the analysis of must. The aim of this research is to exploit the complementary information obtained by means of EE and ET sensing systems using different data fusion strategies, in order to develop an integrated device able to quickly and easily quantify the physico-chemical parameters that are used to assess phenolic ripeness. To this purpose, both low-level and mid-level data fusion approaches were investigated. Partial Least Squares (PLS) regression was applied to the fused data, with the aim of relating the information brought by the two sensors with twelve physico-chemical parameters measured on the must samples by standard analytical methods. The results achieved with mid-level data fusion outperformed those obtained using EE and ET separately, and highlighted that both the artificial sensors have made a significant contribution to the prediction of each one of the considered physico-chemical parameters.

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Quantitative structure-activity relationship (QSAR) analysis has been carried out with a series of 107 anti-HIV HEPT compounds with antiviral activity, which was performed by chemometrics methods. Bi-dimensional images were used to calculate some pixels and multivariate image analysis was applied to QSAR modelling of the anti-HIV potential of HEPT analogues by means of multivariate calibration, such as principal component regression (PCR) and partial least squares (PLS). In this paper, we investigated the effect of pixel selection by application of genetic algorithms (GAs) for the PLS model. GAs is very useful in the variable selection in modelling and calibration because of the strong effect of the relationship between presence/absence of variables in a calibration model and the prediction ability of the model itself. The subset of pixels, which resulted in the low prediction error, was selected by genetic algorithms. The resulted GA-PLS model had a high statistical quality (RMSEP = 0.0423 and R2 = 0.9412) in comparison with PCR (RMSEP = 0.4559, R2 = 0.7929) and PLS (RMSEP = 0.3275 and R2 = 0.0.8427) for predicting the activity of the compounds. Because of high correlation between values of predicted and experimental activities, MIA-QSAR proved to be a highly predictive approach.

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Thirty-one Cephalosporin compounds were modeled using the multivariate image analysis and applied to the quantitative structure activity relationship (MIA-QSAR) approach. The acid dissociation constants (pKa) of cephalosporins play a fundamental role in the mechanism of activity of cephalosporins. The antimicrobial activity of cephalosporins was related to their first pKa by different models. Bidimensional molecular structures (images) were used to calculate some pixel descriptors. The selection of pixels by successive projections algorithm (SPA) was done to achieve simple MIA-QSAR models; based on a small subset of pixels. In the present study, the performance of pixel selection technique using SPA for partial least squares (PLS) model was evaluated. The obtained model showed nice prediction ability with root mean square error of prediction (RMSEP) values of 0.402, 0.315, and 0.160 for principal component regression (PCR), PLS and SPA-PLS models respectively. Among the three methods, SPA-PLS was potentially useful in predicting the pKa of cephalosporins. The study showed the maximum structural efficacy is on pKa in a, b and c regions.

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Senile plaques formed by aggregated amyloid ß peptides are one of the major pathological hallmarks of Alzheimer's disease (AD) which have been suggested to be the primary influence triggering the AD pathogenesis and the rest of the disease process. However, neurotoxic Aß aggregation and progression are associated with a wide range of enigmatic biochemical, biophysical and genetic processes. MALDI imaging mass spectrometry (IMS) is a label-free method to elucidate the spatial distribution patterns of intact molecules in biological tissue sections. In this communication, we utilized multimodal MALDI-IMS analysis on 18 month old transgenic AD mice (tgArcSwe) brain tissue sections to enhance molecular information correlated to individual amyloid aggregates on the very same tissue section. Dual polarity MALDI-IMS analysis of lipids on the same pixel points revealed high throughput lipid molecular information including sphingolipids, phospholipids, and lysophospholipids which can be correlated to the ion images of individual amyloid ß peptide isoforms at high spatial resolutions (10 µm). Further, multivariate image analysis was applied in order to probe the multimodal MALDI-IMS data in an unbiased way which verified the correlative accumulations of lipid species with dual polarity and Aß peptides. This was followed by the lipid fragmentation obtained directly on plaque aggregates at higher laser pulse energies which provided tandem MS information useful for structural elucidation of several lipid species. Majority of the amyloid plaque-associated alterations of lipid species are for the first time reported here. The significance of this technique is that it allows correlating the biological discussion of all detected plaque-associated molecules to the very same individual amyloid plaques which can give novel insights into the molecular pathology of even a single amyloid plaque microenvironment in a specific brain region. Therefore, this allowed us to interpret the possible roles of lipids and amyloid peptides in amyloid plaque-associated pathological events such as focal demyelination, autophagic/lysosomal dysfunction, astrogliosis, inflammation, oxidative stress, and cell death.

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We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

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In the present study the applicability of multispectral UV imaging in combination with multivariate image analysis for surface evaluation of MUPS tablets was investigated with respect to the differentiation of the API pellets from the excipients matrix, estimation of the drug content as well as pellet distribution, and influence of the coating material and tablet thickness on the predictive model. Different formulations consisting of coated drug pellets with two coating polymers (Aquacoat® ECD and Eudragit® NE 30 D) at three coating levels each were compressed to MUPS tablets with various amounts of coated pellets and different tablet thicknesses. The coated drug pellets were clearly distinguishable from the excipients matrix using a partial least squares approach regardless of the coating layer thickness and coating material used. Furthermore, the number of the detected drug pellets on the tablet surface allowed an estimation of the true drug content in the respective MUPS tablet. In addition, the pellet distribution in the MUPS formulations could be estimated by UV image analysis of the tablet surface. In conclusion, this study revealed that UV imaging in combination with multivariate image analysis is a promising approach for the automatic quality control of MUPS tablets during the manufacturing process.

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Chemical imaging techniques are beneficial for control of tablet coating layer quality as they provide spectral and spatial information and allow characterization of various types of coating defects. The purpose of this study was to assess the applicability of multispectral UV imaging for assessment of the coating layer quality of tablets. UV images were used to detect, characterize, and localize coating layer defects such as chipped parts, inhomogeneities, and cracks, as well as to evaluate the coating surface texture. Acetylsalicylic acid tablets were prepared on a rotary tablet press and coated with a polyvinyl alcohol-polyethylene glycol graft copolymer using a pan coater. It was demonstrated that the coating intactness can be assessed accurately and fast by UV imaging. The different types of coating defects could be differentiated and localized based on multivariate image analysis and Soft Independent Modeling by Class Analogy applied to the UV images. Tablets with inhomogeneous texture of the coating could be identified and distinguished from those with a homogeneous surface texture. Consequently, UV imaging was shown to be well-suited for monitoring of the tablet coating layer quality. UV imaging is a promising technique for fast quality control of the tablet coating because of the high data acquisition speed and its nondestructive analytical nature.

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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.

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Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed.

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For a decade, the multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) approach has been successfully used in the modeling of several chemical and biological properties of chemical compounds. However, the key pitfall of this method has been its exclusive applicability to congeneric datasets due to the prerequisite of aligning the chemical images with respect to the basic molecular scaffold. The present report aims to explore the use of the 2D-discrete Fourier transform (2D-DFT) as a means of opening way to the modeling, for the first time, of structurally diverse noncongruent chemical images. The usability of the 2D-DFT in QSAR modeling of noncongruent chemical compounds is assessed using a structurally diverse dataset of 100 compounds, with reported inhibitory activity against MCF-7 human breast cancer cell line. An analysis of the statistical parameters of the built regression models validates their robustness and high predictive power. Additionally, a comparison of the results obtained with the 2D-DFT MIA-QSAR approach with those of the DRAGON molecular descriptors is performed, revealing superior performance for the former. This result represents a milestone in the MIA-QSAR context, as it opens way for the possibility of screening for new molecular entities with the desired chemical or therapeutic utility.

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The distribution and chemical patterns of lignocellulosic components at microscopic scale and their effect on the simultaneous saccharification and fermentation process (SSF) in the production of bioethanol from Pinus radiata pulps were analyzed by the application of diverse microscopical techniques, including scanning electronic microscopy (SEM), confocal laser scanning microscopy (CLSM) and attenuated total reflectance (ATR) - Fourier transform infrared microspectroscopy. This last technique was accompanied with multivariate methods, including principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS) to evaluate the distribution patterns and to generate pure spectra of the lignocellulosic components of fibers. The results indicate that the information obtained by the techniques is complementary (ultrastructure, confocality and chemical characterization) and that the distribution of components affects the SSF yield, identifying lignin coalescence droplets as a characteristic factor to increase the SSF yield. Therefore, multivariate analysis of the infrared spectra enabled the in situ identification of the cellulose, lignin and lignin-carbohydrates arrangements. These techniques could be used to investigate the lignocellulosic components distribution and consequently their recalcitrance in many applications where minimal sample manipulation and microscale chemical information is required.

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During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection.

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Fast non-destructive multi-wavelength UV imaging together with multivariate image analysis was utilized to visualize distribution of chemical components and their solid state form at compact surfaces. Amorphous and crystalline solid forms of the antidiabetic compound glibenclamide, and microcrystalline cellulose together with magnesium stearate as excipients were used as model materials in the compacts. The UV imaging based drug and excipient distribution was in good agreement with hyperspectral NIR imaging. The UV wavelength region can be utilized in distinguishing between glibenclamide and excipients in a non-invasive way, as well as mapping the glibenclamide solid state form. An exploratory data analysis supported the critical evaluation of the mapping results and the selection of model parameters for the chemical mapping. The present study demonstrated that the multi-wavelength UV imaging is a fast process analytical technique with the potential for real-time monitoring of critical quality attributes.

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