Identification of Four Wood Species by an Electronic Nose and by LIBS

Abstract

This paper presents two complementary methods capable of identifying four wood species (Cedrela fissilis, Ocotea porosa, Hymenolobium petraeum, and Aspidosperma subincanum) both by their volatile organic compounds and by the presence of 10 chemical elements: Al, B, Ca, Mg, Zn, Cu, Mn, Fe, Na, and Si. The volatile compounds were detected by an electronic nose formed by an array of three different conductive polymer gas sensors. The elemental determination was made by laser-induced breakdown spectrometry (LIBS). The emissions measured were treated by principal component analysis (PCA). Leave-one-out analysis showed a rate of hits of 100%.

Good practices in LIBS analysis: Review and advices

“…In this case, one should consider a matrix X of predictors instead of a single value x. First of all, the X-matrix is usually described through PCA calculation [41]. Once the principal components are calculated, a second step consisting in measuring the distances between the points is necessary to conclude about the presence of classes.…”

“…Different methods of validation have been implemented, namely cross-validation methods such as i) the leave-one-out procedure (LOO) [41,49], ii) the k-fold method (or LMO) [40], iii) the leave-onesubject-out procedure (LOSO) [61] and also external validation based on the use of an independent dataset for testing the model [50,60]. It should be emphasized that in the case of LOSO, no replicate of any given sample could be found in both the learning and the validation sets.…”

“…Rate of correct classification Percentage of samples correctly classified for a given class [46,54] Rate of wrong classification Percentage of samples incorrectly classified for a given class [46,54] Rate of no classification Percentage of samples not classified in any class [46,54] Overall accuracy (%) Number of samples correctly classified (all classes) / total number of samples [55,56] Sensitivity TP/(TP+FN) for a given class [46,56] Specificity TN/(TN+FP) for a given class [56] Negative predictive value TN/(TN+FN) for a given class [56] Positive predictive value TP/(TP+FP) for a given class [56] Robustness After suppression of a given class, the rate of wrong classification of the samples in the other classes [41,46] A C C E P T E D M A N U S C R I P T…”

Dispersive liquid–liquid microextraction combined with laser-induced breakdown spectrometry and inductively coupled plasma optical emission spectrometry to elemental analysis

“…Laser Induced Breakdown Spectroscopy (LIBS), even if not yet considered a conventional instrumental technique, has also been used for elemental analysis since its introduction, providing a significant number of applications [1][2][3][4].…”

QA: Fraud Control for Foods and Other Biomaterials by Product Fingerprinting

“…Recently an original and fast approach for the authentication of wood species has been proposed. It is based on the analysis of the volatile compounds measured by a low-cost conductive polymer-based portable electronic nose formed by an array of only three gas sensors and/or the elemental fingerprint measured by laser-induced breakdown spectrometry (LIBS) which performs a multielemental and direct analysis even in solid samples [74].…”