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Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels. [ABSTRACT FROM AUTHOR]

In order to characterize iron–chromium oxides generated by laser irradiation on the surface of stainless steel plates, an ultraviolet–visible (UV-Vis) near-infrared (NIR) multiwavelength excitation Raman analysis has been performed using both austenitic SS304 and ferritic SS430 stainless steel samples. Raman spectra were obtained using five different excitation wavelengths from blue (455 nm) to NIR (830 nm). These measurements have allowed us to observe and identify four Raman bands, among which two have not been previously observed for iron–chromium oxides, and characterize the existence of different resonant excitation conditions for the different excitation wavelengths. For example, when using 455 nm as excitation wavelength the band at 485 cm−1 did not show up, although that when using 830 nm as excitation wavelength is a clear characteristic band for iron–chromium oxide. In addition, the dependence of the spectra profile with the excitation wavelength for films and microspheres features was observed. This experimental Raman analysis shows the importance of the excitation wavelength for the characterization of metallic oxides with different features. [ABSTRACT FROM AUTHOR]

Fuji apples from two production areas were separated into six batches by different experimenters. After applying light (500-1010 nm) on the surface of intact ones for their visible and near-infrared (NIR) spectra, destructive samples of three apple components were taken to determine the soluble solids content (SSC). Correlation and regression coefficients between the second Savitzky-Golay derivative of the spectra and SSC were analyzed to reveal that SSC values derived from the different apple components showed significantly different responses in the visible region. However, similar responses, particularly in the NIR section (730-932 nm), remained, including two sugar bands at 890 and 906 nm. On the basis of applying above characteristic bands to remove the interference signals, partial least square (PLS) and multiple linear regression (MLR) showed similar effective performances. According to the analysis of variance (ANOVA) method, sampling methods had significant effect on quantitative accuracy, and the model, using SSC values detected from the outer flesh cuboid (2.5 × 2.5 × 1.5 cm³), provided the best performance with lower root mean square error of prediction and higher correlation coefficient. [ABSTRACT FROM AUTHOR]

Two state-of-the-art methods for non-invasive visualization of subsurface (or overpainted) pictorial layers present in painted works of art are employed to study Le portrait, painted by Belgian artist René Magritte in 1935. X-ray radiography, a commonly used method for the nondestructive inspection of paintings, had revealed the presence of an underlying figurative composition, part of an earlier Magritte painting entitled La pose enchantée (1927) which originally depicted two full length nude female figures with exaggerated facial features. On the one hand, macroscopic X-ray fluorescence analysis (MA-XRF), a method capable of providing information on the distribution of the key chemical elements present in many artists' pigments, was employed. The ability of the X-rays to penetrate the upper layer of paint enabled the imaging of the facial features of the female figure and provided information on Magritte's palette for both surface and hidden composition. On the other hand, visible and near infrared hyperspectral imaging spectroscopies in transmission mode were also used, especially in the area of the table cloth in order to look through the upper representation and reveal the pictorial layer(s) below. MA-XRF provided elemental information on the pigment distributions in both the final painting and the prior whereas the transmission mode provided information related to preparatory sketches as well as revealing differences between the paints used in both compositions. These results illustrate very well the manner in which the two imaging methods complement each other, both in the sense of providing different types of information on the nature and presence of paint components/pigments and in the sense of being optimally suited to easily penetrate through different types of overpaint. [ABSTRACT FROM AUTHOR]

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible-near-infrared (Vis-NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B 1 (AFB 1 ). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB 1 -contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB 1 contamination of peanut kernels.