Spectroscopy For Discrimination Research Articles

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions.

Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part I: Design and Theoretical Performance of Multivariate Optical Elements

Phytoplankton are single-celled, photosynthetic algae and cyanobacteria found in all aquatic environments. Differential pigmentation between phytoplankton taxa allows use of fluorescence excitation spectroscopy for discrimination and classification. For this work, we applied multivariate optical computing (MOC) to emulate linear discriminant vectors of phytoplankton fluorescence excitation spectra by using a simple filter-fluorometer arrangement. We grew nutrient-replete cultures of three differently pigmented species: the coccolithophore Emiliania huxleyi, the diatom Thalassiosira pseudonana, and the cyanobacterium Synechococcus sp. Linear discriminant analysis (LDA) was used to determine a suitable set of linear discriminant functions for classification of these species over an optimal wavelength range. Multivariate optical elements (MOEs) were then designed to predict the linear discriminant scores for the same calibration spectra. The theoretical performance specifications of these MOEs are described.

Evaluation of flaA Short Variable Region Sequencing, Multilocus Sequence Typing, and Fourier Transform Infrared Spectroscopy for Discrimination between Campylobacter jejuni Strains

Discriminatory and robust typing methods are needed to improve the understanding of the dynamics of food-borne Campylobacter infections and epidemiology in primary animal production. To evaluate the strain discriminatory potential of typing methods, flaA short variable region (SVR) sequencing and Fourier transform infrared (FTIR) spectroscopy were applied on a collection of 102 epidemiologically related and unrelated Campylobacter jejuni strains. Previous application of FTIR spectroscopy for subtyping of Campylobacter has been limited. A subset of isolates, initially discriminated by flaA SVR sequencing, were further subjected to multilocus sequence typing (MLST). It was found that flaA SVR sequencing had a slightly higher discriminatory power than FTIR spectroscopy, based on the Simpson diversity index. The clustering of strains indicated that FTIR spectroscopy is indeed a suitable method for discrimination of Campylobacter. The isolates were assigned to six clusters based on flaA SVR sequences and nine clusters based on the FTIR spectroscopy profiles. Furthermore, the cluster analysis of flaA SVR sequences, MLST, and FTIR spectroscopy profiles showed a high degree of congruence, assigning the isolates to similar cluster structures. In conclusion, FTIR spectroscopy can be applied for subtyping of Campylobacter, and the high discriminatory potential of both flaA SVR sequencing and FTIR spectroscopy render them suitable screening methods for large numbers of strains.

CLASSIFICATION OF CHINESE RICE WINE WITH DIFFERENT MARKED AGES BASED ON NEAR INFRARED SPECTROSCOPY

ABSTRACT The feasibility of near infrared (NIR) spectroscopy for discrimination between Chinese rice wine with different marked ages (1, 3 and 5 years) was presented in this research. NIR spectra were collected in transmission mode in the wavelength range of 800–2500 nm. Discriminant models were developed based on discriminant analysis (DA) together with raw and second derivative spectra. The calibration result for raw spectra was better than that for second derivative spectra. The percentage of samples correctly classified for raw and second derivative spectra was 100 and 78.4%, respectively. In validation analysis, the percentage of samples correctly classified was 94.4%. For 1‐, 3‐ and 5‐year‐old sample groups, the percentage of samples correctly classified was 88.3, 100 and 100%, respectively. The results demonstrated that NIR spectroscopy could be used as a rapid and reliable method for classification of Chinese rice wine with different marked ages.

Discriminant analysis of edible oils and fats by FTIR, FT-NIR and FT-Raman spectroscopy

The goals of this study were to use Fourier transform mid-infrared (FTIR), near-infrared (FT-NIR) and Raman (FT-Raman) spectroscopy for discrimination among 10 different edible oils and fats, and to compare the performance of these spectroscopic methods for edible oil/fat study. The spectral features of edible oils and fats were studied and the unsaturation bond (C C) in IR and Raman spectra was identified and used for discriminant analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for the discrimination and classification of different edible oils and fats based on spectral data. FTIR spectroscopy was found to be the most efficient in classification of oils and fats when used with CVA and yielded about 98% classification accuracy, followed by FT-Raman (94%) and FT-NIR (93%) methods; however, the number of factors were much higher for FT-Raman and FT-NIR methods. Overall, results demonstrated that FTIR, FT-NIR and FT-Raman techniques can be used to rapidly and simply determine the authenticity of edible oils and fats with chemometric analysis.

Multivariate strategies for classification based on NIR-spectra—with application to mayonnaise

The goal of the presented study is two-fold. First, we want to emphasize the power of Near Infrared Reflectance (NIR) spectroscopy for discrimination between mayonnaise samples containing different vegetable oils. Secondly, we want to use our data to compare the performances of different classification procedures. The NIR spectra with 351 variables correspond to equally spaced wavelengths in the 1100–2500 nm area. Feature extraction both by automatic wavelength-selection and by projection onto principal components (PCs) is discussed. The discriminant methods considered are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and regression with categorical {0,1}-responses. A dataset containing 162 spectra of mayonnaise samples based on six different vegetable oils is analyzed. By LDA with authentic cross-validation (PC-models re-estimated for each cross-validation segment), only one sample was misclassified. Classification by allocating a sample according to the largest fitted value of a linear regression (Discriminant-Partial least squares (DPLS) or Discriminant-Principal components regression (DPCR)) is demonstrated sub-optimal compared to LDA of the corresponding PLS- or PCR-scores. QDA significantly outperforms LDA for projections of the data onto subspaces of moderate size (scores of 7–9 PCs). Two automatic variable-selection procedures choose 16 and 26 wavelengths (variables), respectively from the spectra. Based on the selected wavelengths, LDA gives considerably better classification than the regression approach. By reporting the performances of several feature extraction techniques in tandem with three of the most common classification methods, we hope that the reader will notice two relevant aspects: (1) By using the DPLS and DPCR (classification by `dummy' regressions) one is exposed to a significant risk of obtaining sub-optimal classification results; (2) The automatic wavelength selections may give valuable information about what is actually causing a successful discrimination. Such knowledge can, for instance, be used to select the most suited filters for online applications of NIR. Besides, from demonstrating different classification strategies, our study clearly shows that classification methods with NIR spectra can be used to discriminate between mayonnaise samples of different oil types and fatty acid composition.