A Raman measurement scheme for accurate compositional analysis of dark-colored sample by minimizing laser-induced sample degradation: Determination of Fe3O4 concentration in sintered ores.

An effective and rapid Raman measurement scheme to determine Fe3O4 concentration in sintered ores was explored. Because sintered ores are brownish-black materials that easily absorb laser photons, accurate quantitative analysis requires obtaining an Fe3O4 peak with a high signal-to-ratio by reducing the possibility of local sample heating and degradation. For this purpose, a wide area coverage (WAC) Raman scheme with a laser-illumination diameter of 1 mm was adopted to decrease the laser power per area (LP/A) on each sample. The sintered ore sample was also wetted with water to reduce the chance of further heating by the laser. The combination of the WAC scheme and water-wetting allowed to increase the laser power during sample measurement, and the subsequent intensity (as well as the signal-to-noise ratio) of the Fe3O4 peak was elevated compared with both that measured by a Raman microscope yielding a higher LP/A and without water-wetting of the sample. In the Raman spectra of 93 real sintered ore samples measured using the proposed scheme, the ratio of Fe3O4 and Fe2O3 peak areas correlated closely (R2 = 0.94) with Fe3O4 concentration determined by titration. The demonstrated scheme is practical when Raman spectroscopy is employed for compositional analysis of dark and highly photon-absorbing samples.

Intelligent color image analysis of sintered ores for simple and rapid determination of Fe3O4 concentration.

A simple determination of the Fe3O4 concentrations of sintered ores using color images of the samples has been explored. Sintered ore is mainly composed of Fe2O3 (red), Fe3O4 (black), and other white inorganic oxides, so the color of sintered ore could be representative of the relative abundance of the constituents. Two important challenges were addressed to achieve reliable quantitative color image analysis. First, minute dents and bumps (embosses) exist on the sample surface due to inconsistent particle sizes and particle agglomeration, thereby generating dark shadows. Second, small white spots corresponding to inorganic oxide particles were spread throughout acquired images. The white spots yield very high RGB values, which would hamper the translation of the real sample color originating from the iron oxides. Therefore, the segmentations of particle agglomeration-induced shadows and white spots in the sample images were separately executed using Otsu's method and modified fuzzy C-means (MFCM), respectively. Then, color moments and derived variables from the segmented images were employed to determine Fe3O4 concentrations (6.5-10.5 wt%) using extreme gradient boosting (XGBoost). The predicted concentrations from the color analysis correlated well with reference concentrations determined using conventional titration, with a root mean square error of prediction (RMSEP) of 0.39 wt%.

Identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry by selecting sample-descriptive components with an Incremental Association Markov Blanket.

The identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry was attempted in this study. Since soybean paste was odor-rich, 36 components were identified in the imported and domestic soybean samples. t-Test, variable importance in projection (VIP), and Incremental Association Markov Blanket (IAMB) were employed to select proper components that could effectively discriminate the two sample groups. The discrimination accuracies were below 87.3 % when all 36 components were fed for either LDA, k-NN, or SVM. When the five t-test-selected components or six VIP score-selected components were employed, the accuracies improved to 95.2-96.2 %. The IAMB selected three different components were 3-methylbutanal, 4-methylnonane, and 2,3-pentanedione, and the correlations among their peak areas were not significant. This suggests that these three components were independently relevant for the discrimination. The accuracy obtained using these three components was superior, 97.7 %, as undescriptive and/or redundant components for the discrimination were excluded.

Authentication of ST25 rice using temperature-perturbed Raman measurement with variable selection by Incremental Association Markov Blanket.

A temperature-perturbed transmission Raman measurement was demonstrated for the discrimination of ST25 and non-ST25 rice samples. ST25 rice is a premium long-grain Vietnamese rice with the aroma of pandan leaves and the scent of early sticky rice. Raman spectra of rice samples were acquired with temperature perturbation ranging from 20 to 50 °C, and the variables (intensities of peaks) with greater discrimination were selected from the spectra using Incremental Association Markov Blanket (IAMB) for authentication. The combination of four, seven, and four variables selected from the spectra at 20, 30, and 50 °C, respectively, yielded the highest accuracy of 97.9%. The accuracies in the single-temperature measurements were lower, suggesting that the combination of mutually complementary spectral features acquired at these temperatures is synergetic to recognize the compositional differences between two sample groups, such as in the amylose/amylopectin ratio and the protein constituent.

Geographical discrimination of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS).

This study presents a method for discriminating the geographical origin of dried chili peppers using femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fsLA-ICP-MS) and multivariate analysis, such as orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap analysis, and canonical discriminant analysis (CDA). Herein, 102 samples were analyzed for the content of 33 elements using optimized conditions of 200 Hz (repetition rate), 50 µm (spot size), and 90% (energy). Significant differences in count per second (cps) values of the elements were observed between domestic and imported peppers, with variations of up to 5.66 times (133Cs). The OPLS-DA model accuracy achieved an R2 of 0.811 and a Q2 of 0.733 for distinguishing dried chili peppers of different geographical origins. The variable importance in projection (VIP) and s-plot identified elements 10 and 3 as key to the OPLS-DA model, and in the heatmap, six elements were estimated to be significant in discriminating between domestic and imported samples. Furthermore, CDA showed a high accuracy of 99.02%. This method can ensure food safety for consumers, and accurately determine the geographic origin of agricultural products.

Cooperative combination of LIBS-based elemental analysis and near-infrared molecular fingerprinting for enhanced discrimination of geographical origin of soybean paste.

Laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy were combined to enhance discrimination of soybean paste samples according to geographical origin. Since element and organic component compositions of soybean pastes depend on soybean cultivation areas and fermentation conditions, utilization of two complementary spectroscopic signatures would be synergetic for the discrimination. When the areas of C (AC) and Ca (ACa) peaks in the LIBS spectra were used as the inputs for linear discriminant analysis, the accuracy was 95.4%. The accuracy became 92.1%, when the principal component (PC) scores obtained by principal component analysis of the NIR spectra were employed. To enhance NIR discrimination, two-trace two-dimensional (2T2D) correlation analysis was adopted to recognize minute spectral differences. With using the 1st/2nd PC scores of 2T2D slice spectra, accuracy increased to 95.0%. When the ratios of ACa/AC and the 2nd PC scores of the samples were combined together, the accuracy improved to 99.6%.