Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control methods usually test a limited number of variability sources affecting the final product. This study examines the potential of NIR-HSI (in the spectral range of 935.61-1720.2 nm) as an advanced and high-throughput detector to identify different manufacturing factors and their fluctuations that impact tablet properties. These are, for instance, particle sizes of powdered excipients, their mixing, compression force used to form a tablet, origin of ingredients, storage conditions, and concentration of API. During the study, the novel expert system approach was developed to support NIR-HSI, enabling the detection of subtle, diverse substandard anomalies in tablets. The system combines (i) hyperspectrograms, which characterize and simplify tablet spatial heterogeneity through principal component analysis scores distribution, and (ii) a one-class classifier (OCC), trained exclusively on target class samples, without the need for substandard tablets. The system was trained to recognize known sources of variation and validated using tablets with cellulose, magnesium stearate, and ascorbic acid as API. It outperformed the alternative approach based on averaged spectra, achieving 100.00 % sensitivity and 98.77 % specificity.

This study illustrates at-line application of hyperspectral imaging in the visible range for quality control of large-scale offset printing. In particular, the measurement stability of a competitive device is assessed and compared to traditional handheld and desktop spectrophotometers. The performance of the commercially available instruments was assessed based on collected spectra and their corresponding L*, a*, and b* values. The printing process was described by hyperspectral images (in visible range) of selected regions from template color fields acquired at 17 sampling occasions. Spectra constituting hyperspectral images were visualized and evaluated in the space of significant principal components obtained from the principal component analysis. Furthermore, confidence ellipses were constructed for each set of spectra characterizing a specific moment of the printing process. Comparing their mutual locations, shapes, orientations, and sizes enabled effective visualization of process variability and was more comprehensive regarding the classic approach based on information provided by desktop and handheld spectrometers.

Evaporation is the phase transition process that plays a significant role in many spheres of life and science. Volatilization of hazardous materials, pesticides, petroleum spills, etc., impacts the environment and biosphere. Predicting evaporation fluxes under specific environmental conditions is challenging from theoretical and empirical points of view. A new practical method for estimating fluxes is proposed based on our experimental results and previously published data. It is demonstrated that some parameters in theoretical equations for near-equilibrium evaporation can be estimated from experiments, and these formulas can be exploited to predict steady-state evaporation fluxes in the air in a range of 8 orders of magnitude based on a single experiment carried out for nontoxic volatile compounds.

In recent years, due to the significant development of instrumental techniques, clinical research has acquired many interesting tools and analytical platforms that can support the development of advanced diagnostic methods. Nevertheless, along with these attractive instrument possibilities and the pressure to implement the complementary descriptions of biological systems, there is a need for rational research planning and the effective analysis of complex experimental data, which aims to assess research hypotheses, create logical rules and expert systems that support the diagnostic process, and respects the environment by developing greener analysis methods. These aspects are addressed when appropriate chemometric methods are applied in the research process. This article aims to show when and how this can be done.

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky–Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

The study aimed to develop a cost-effective and rapid approach for monitoring eleven crucial parameters that describe the health of cultivated Haplic Luvisol soils. To achieve this, the diffuse reflectance spectra in the NIR spectral region, between 1100 and 2500 nm, were registered, and partial least-squares (PLS) models to predict the concentrations of the critical parameters, including organic carbon, total nitrogen, the available soil nutrients (phosphorus, potassium and magnesium), the exchangeable cations (calcium, magnesium and potassium), the pH levels (determined in deionized water or KCl solution and hydrolytic acidity), were built. The validation results, which are expressed as RMSE and R2val that were obtained for validation the samples, demonstrate that the proposed approach is a reliable and efficient way to monitor the health of Haplic Luvisol soils. Specifically, excellent predictions were obtained for the total nitrogen, organic carbon and pH, which was determined in KCl solution with R2val values equal to 0.9696, 0.9573 and 0.9176, respectively. Additionally, very good PLS models were constructed to estimate the pH levels determined in deionized water, exchangeable magnesium, and po-tassium, which achieved R2val values of 0.8650, 0.8383, and 0.8329, respectively. For the remaining parameters, the corresponding R2val values were above 0.7294.

This study illustrates the successful application of near-infrared reflectance spectroscopy extended with chemometric modeling to profile Cd, Cu, Pb, Ni, Cr, Zn, Mn, and Fe in cultivated and fertilized Haplic Luvisol soils. The partial least-squares regression (PLSR) models were built to predict the elements present in the soil samples at very low contents. A total of 234 soil samples were investigated, and their reflectance spectra were recorded in the spectral range of 1100-2500 nm. The optimal spectral preprocessing was selected among 56 different scenarios considering the root mean squared error of prediction (RMSEP). The partial robust M-regression method (PRM) was used to handle the outlying samples. The most promising models were obtained for estimating the amount of Cu (using PRM) and Pb (using the classic PLS), leading to RMSEP expressed as a percentage of the response range, equal to 9.63% and 11.5%, respectively. The respective coefficients of determination for validation samples were equal to 0.86 and 0.58, respectively. Assuming similar variability of model residuals for the model and test set samples, coefficients of determination for validation samples were 0.94 and 0.89, respectively. Moreover, the favorable PLS models were also built for Zn, Mn, and Fe with coefficients of determinations equal to 0.87, 0.87, and 0.79.

We do not know whether recycling will save us from environmental disaster. We believe, however, that segregating trash makes sense. From an early age, our children learn to link the colors of containers with various types of waste. Plastic bags, plastic bottles, metal caps, aluminum cans and milk cartons end up in yellow containers and bags every day. These few examples already show the diversity of these materials. According to the Resin Identification Code (RIC) system introduced in 1988, there are as many as seven codes to designate plastics alone. The waste we separate must therefore undergo another sorting process so that we can speak of proper recycling. How to do it? It turns out that an adequately designed camera is sufficient.

This study examines the potential of near-infrared hyperspectral imaging for assessing the size of polymer particles in model fractions based on the scattering phenomena. Different fractions of ground polymers, either polymethyl methacrylate or polypropylene, were characterized by near-infrared spectra collected between 900 and 1700 nm. The possibility to estimate the size of polymer particles using hyperspectral images was confronted with a basic single spot near-infrared measurement. Hyperspectral imaging, in addition to the standard spectral data dimension, provides information about the spatial distribution of sample components and reveals changes in physical properties. Therefore, one can gain a better insight into the scattering phenomena and study the physical inhomogeneity of a sample in terms of particle size distribution. The partial least-squares models constructed to estimate particle size of polymers that were characterized by hyperspectral images (a pixel-based approach) outperforms models built for mean spectra regardless of the considered powdered polymer.

In this article, we present a new type of inexpensive multi-illumination source chamber. The innovation makes use of a smartphone camera which possesses the ability of capturing multiple images. Its performance was compared to a commercially available densitometer. Similar devices and suitable strategies for data analysis will help to solve diverse classification and/or regression problems, which will be far beyond a TLC characterization of ink samples. The multi-illumination chamber was used in an exemplary forensic application. The differences in the chemical composition of various brands of fountain pen inks were revealed on images of high-performance thin-layer chromatographic plates. Reducing image data simplified the visualization and facilitated a multivariate exploratory of the ink samples. Compared to the samples that were characterized by single wavelength densitograms, the multi-wavelength characterization using the illumination chamber with a smartphone camera or densitometer improved the clustering tendency of studied samples and enhanced their interpretation. The constructed chamber for multi-wavelength imaging is an inexpensive alternative (ca. 20 Euros) to the commercially available densitometers. The discussed approaches for image acquisition and chemometric data processing support a more reliable and objective analysis of TLC multi-wavelength data.

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal's baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed.

In this study, we examine the chemical stability of two disazo dyes, namely Solvent Red 19 and 23 (SR 19 and SR 23), under simulated conditions. Both dyes are considered to be chemically stable under normal exploitation conditions and therefore, are used extensively as excise duty components that enable a rapid visual verification of the tax levels that were imposed on fuel products as well as identifying fuel usage. However, the results from this study confirmed that the colour of the samples that had been fortified with either SR 19 or SR 23 fades under the influence of external conditions such as UV-A irradiation and temperature over time. The UV-A irradiation was the dominant factor that was responsible for the colour of the samples to fade in two designed experiments that were carried out independently for two model systems. The analysis of the UV/Vis and fluorescence spectra as well as the interpretation of the changes that were observed in the chromatographic profiles provided substantial evidence that the colour fading was caused by the photodegradation of the disazo dyes, which also occurs in non-polar media including fuel products. SR 19 is more stable than SR 23.

The objective of our study was to evaluate the advantages of the proposed validation framework for a rigorous one-class classifier that operates on the large sets of multivariate pixels that are obtained using the hyperspectral imaging technique that are considered to be individual samples. The performance of the validation strategy was evaluated experimentally using the hyperspectral images of post-consumer waste polymers (high-density polyethylene and polypropylene), which have very similar physico-chemical properties. Rigorous classification models using the partial least squares approach were constructed for the training samples from modeled groups of polymer items. Their aim was to support the process of sorting polymer waste items and to potentially provide a framework to construct an intelligent laboratory system. The results that were obtained in this study provide evidence that the pixel-based approach improves classification in terms of sensitivity and specificity. The models that described high-density polyethylene polymer items and that were built to represent individual pixels were characterized by a sensitivity of more than 98.6% and a specificity of more than 99.5%, whereas the models that were constructed for the polypropylene polymer items had a sensitivity of more than 93.4% and a specificity of more than 99.9%.

In this study, differences in the chemical compositions of rebated excise duty diesel oil samples that were caused by fuel laundering were investigated. Two possible laundering pathways were simulated using either reduction or adsorption agents in model samples that were spiked with Solvent Yellow 124 and Solvent Red 19. The samples were characterized by their chromatographic fingerprints, which were recorded using gas chromatography coupled with a nitrogen chemiluminescence detector. The collections of fingerprints were further analyzed by discriminant partial least squares and the models with the optimal complexities presented the correct discrimination rates in the range of 69.1%-99.6%, respectively. The most informative fingerprint sections that were associated with the investigated differences were identified using the variable importance in projection, selectivity ratio and uninformative variable elimination methods. The reduced multivariate discriminant models presented a relatively high performance with the correct classification rates in the range of 74.9%-99.8%, respectively. O-toluidine and 2,5-diaminotoluene were identified as potential markers of diesel oil counterfeiting by laundering through a reduction agent.

Chemometric methods permit the construction of classifiers that effectively assist in monitoring safety, quality and authenticity of meat based on the near-infrared (NIR) spectral fingerprints. Discriminant techniques are often considered in multivariate quality control. However, when the authenticity of meat products is the primary concern, they often lead to an incorrect recognition of new samples. The performances of two class modeling techniques (CMT) in order to recognize meat sample species based on their NIR spectra was compared - a one-class classifier variant of the partial least squares method (OCPLS) and the soft independent modeling of class analogy (SIMCA). Based on obtained sensitivity and specificity values, OCPLS and SIMCA can be considered as an effective CMT for the classification of complex natural samples such as studied meat samples (with a relatively large variability). Moreover, particular attention was paid to the optimization and validation of a one-class classification model.