Monitoring the concentrations of Cd, Cu, Pb, Ni, Cr, Zn, Mn and Fe in cultivated Haplic Luvisol soils using near-infrared reflectance spectroscopy and chemometrics.

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.

Multi-wavelength imaging of HPTLC plates using a constructed illumination chamber with a smartphone camera as the detector.

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.

Identification of ground meat species using near-infrared spectroscopy and class modeling techniques - Aspects of optimization and validation using a one-class classification model.

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.