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Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources. [ABSTRACT FROM AUTHOR]

Saffron ( Crocus sativus ) stigmas as a flavoring commodity command a very high price in international food markets and, as a result, are a candidate for various kinds of fraud. This paper reports on the use of thin layer chromatography combined with image analysis (TLC-IA) and chemometrics techniques for validating the authenticity of saffron and rapidly identifying the type of adulterants. This method includes several pre-processing steps, such as correcting the general baseline (using the asymmetric least squares (AsLS) algorithm), converting the images to RGB chromatographic channels, and removing the shifts and concavity of spots (using a correlation optimization warping (COW) algorithm) prior to image analysis of saffron thin layer chromatography patterns. After employing the preprocessing sequence, different unsupervised multivariate data analysis (i.e. principal component analysis (PCA) and k-means) and supervised chemometric methods (i.e. partial least squares discrimination analysis (PLS-DA), variable selection (loading weight and variable importance in projection (VIP)) and linear discriminant analysis (LDA)) were applied to validate the authenticity of saffron and to classify the types of adulterants. As a result, quality control of Iranian sourced saffron in the international food market became possible. [ABSTRACT FROM AUTHOR]

Quality of saffron, a valuable food additive, could considerably affect the consumers’ health. In this work, a novel preprocessing strategy for image analysis of saffron thin layer chromatographic (TLC) patterns was introduced. This includes performing a series of image pre-processing techniques on TLC images such as compression, inversion, elimination of general baseline (using asymmetric least squares (AsLS)), removing spots shift and concavity (by correlation optimization warping (COW)), and finally conversion to RGB chromatograms. Subsequently, an unsupervised multivariate data analysis including principal component analysis (PCA) and k-means clustering was utilized to investigate the soil salinity effect, as a cultivation parameter, on saffron TLC patterns. This method was used as a rapid and simple technique to obtain the chemical fingerprints of saffron TLC images. Finally, the separated TLC spots were chemically identified using high-performance liquid chromatography-diode array detection (HPLC-DAD). Accordingly, the saffron quality from different areas of Iran was evaluated and classified. [ABSTRACT FROM AUTHOR]