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Classification or screening analysis of natural unprocessed teas using simple digital images and a variable selection algorithm is described. The proposed methodology uses color histograms generated on free downloadable software ImageJ 1.44p as a source of analytical information. Two chemometric methods were compared for classification of the resulting images, namely Soft Independent Modeling of Class Analogy (SIMCA), and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors found in a sample set separate from the modeling process. The choice of more informative photometric color attributes (red-greenblue (RGB), hue (H), saturation (S), brightness (B), and grayscale) for screening the tea samples was made during the color modeling because SIMCA failed to give good results. Therefore the data treatment used SPA-LDA, which correctly classified all samples according to their geographical regions, whether from Brazilian, Argentinian or foreign soils.

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A new simple, rapid and inexpensive analytical method was developed to determine the biodiesel percentage in biodiesel/ diesel blends through simple digital images of samples obtained by scanning with a commercial scanner. Soybean biodiesel and petroleum diesel samples were acquired from refineries currently in operation. There were prepared several mixtures within the range 1.5 to 12.0% of biodiesel in diesel oil, using the same procedure as is done in industry. The analytical signals were images recorded with a scanner. This data was decomposed with different color systems: RGB, HSV, HLS, CMYK and Grayscale. Chemometrics models based on color signals obtained from different mixtures of biodiesel/diesel were built. The quantification by using partial least squares (PLS) resulted in a RMSEP value for biodiesel of 0.9% (w/w); this load approximately 10-times smaller than the corresponding calibration range, with a correlation of 0.96 between predicted and reference values.

This paper proposes a NIR spectrometric method for screening analysis of liquefied petroleum gas (LPG) samples. The proposed method is aimed at discriminating samples with low and high propane content, which can be useful for the adjustment of burn settings in industrial applications. A gas flow system was developed to introduce the LPG sample into a NIR flow cell at constant pressure. In addition, a gas chromatographer was employed to determine the propane content of the sample for reference purposes. The results of a principal component analysis, as well as a classification study using SIMCA (soft independent modeling of class analogies), revealed that the samples can be successfully discriminated with respect to propane content by using the NIR spectrum in the range 8100-8800 cm(-1). In addition, by using SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm), it was found that perfect discrimination can also be achieved by using only two wavenumbers (8215 and 8324 cm(-1)). This finding may be of value for the design of a dedicated, low-cost instrument for routine analyses.

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