FTIR spectroscopy in combination with principal component analysis or cluster analysis as a tool to distinguish beech (Fagus sylvatica L.) trees grown at different sites

Holzforschung ◽  
2008 ◽  
Vol 62 (5) ◽  
Author(s):  
Rumana Rana ◽  
Günter Müller ◽  
Annette Naumann ◽  
Andrea Polle

Abstract FTIR spectroscopy was used to distinguish between beech (Fagus sylvatica L.) trees grown at five different sites; one in middle Germany close to Göttingen (forest district Reinhausen), three located in the southwest (two in Rhineland-Palatinate: forest districts Saarburg and Hochwald, and one in Luxembourg), and one in North-Rhine Westfalia. Detailed investigation of the spectra in the fingerprint region (1800–600 cm-1) revealed 16 distinct peaks and shoulders, most of which were assignable to wavenumbers previously shown to represent wood compounds. Differences in peak heights and peak ratios indicated differences in wood composition of beech trees from different sites. To determine if the wood of individual trees could be distinguished, principal component analysis (PCA) and cluster analysis were performed using FTIR spectra as input data. With both PCA and cluster analysis, trees from four of the five different sites were separated. It was not possible to distinguish between trees from Saarburg and Hochwald, where similar edaphic and climatic conditions exist, while wood spectra from trees from all other areas clearly segregated. Wood collected at different positions in the stem (bottom, crown, center and outer year rings) of trees grown at the same site was not distinguishable. Therefore, FTIR spectral analysis in combination with multivariate statistical methods can be used to distinguish wood of trees from different growth habitats. Extension of this method to other species may be of great interest for wood certification, as it may be possible to distinguish wood, of a given species, originating from different regions.

2018 ◽  
Vol 37 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Safia Khelif ◽  
Abderrahmane Boudoukha

AbstractThis study is a contribution to the knowledge of hydrochemical properties of the groundwater in Fesdis Plain, Algeria, using multivariate statistical techniques including principal component analysis (PCA) and cluster analysis. 28 samples were taken during February and July 2015 (14 samples for each month). The principal component analysis (PCA) applied to the data sets has resulted in four significant factors which explain 75.19%, of the total variance. PCA method has enabled to highlight two big phenomena in acquisition of the mineralization of waters. The main phenomenon of production of ions in water is the contact water-rock. The second phenomenon reflects the signatures of the anthropogenic activities. The hierarchical cluster analysis (CA) in R mode grouped the 10 variables into four clusters and in Q mode, 14 sampling points are grouped into three clusters of similar water quality characteristics.


2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
V. Gianotti ◽  
S. Panseri ◽  
E. Robotti ◽  
M. Benzi ◽  
E. Mazzucco ◽  
...  

This study is focused on the characterisation of typical salami produced in Alessandria province (North West of Italy). Seventeen small or medium salami producers from this area were involved in the study and provided the samples investigated. The aim is double and consists in obtaining a screening of the characteristics of different products and following their evolution along ripening. The study involved five types of typical salami that were characterised for aroma components and nutritional features. This approach could provide a basis for a possible PDO or PGI label request. Principal Component Analysis and cluster analysis were used as multivariate statistical tools for data treatment. The overall results obtained point out that the products investigated do not deviate from analogous European products and show the possibility of characterising by specific parameters three main groups of samples:Salamini di Mandrogne,Muletta, andNobile Giarolo; moreover some considerations can also be drawn with respect to the nutritional characterization considering the biogenic amines profile.


2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alejandra Carreon-Alvarez ◽  
Amaury Suárez-Gómez ◽  
Florentina Zurita ◽  
Sergio Gómez-Salazar ◽  
J. Felix Armando Soltero ◽  
...  

Several physicochemical properties were measured in commercial tequila brands: conductivity, density, pH, sound velocity, viscosity, and refractive index. Physicochemical data were analyzed by Principal Component Analysis (PCA), cluster analysis, and the one-way analysis of variance to identify the quality and authenticity of tequila brands. According to the Principal Component Analysis, the existence of 3 main components was identified, explaining the 87.76% of the total variability of physicochemical measurements. In general, all tequila brands appeared together in the plane of the first two principal components. In the cluster analysis, four groups showing similar characteristics were identified. In particular, one of the clusters contains some tequila brands that are not identified by the Regulatory Council of Tequila and do not meet the quality requirements established in the Mexican Official Standard 006. These tequila brands are characterized by having higher conductivity and density and lower viscosity and refractive index, determined by one-way analysis of variance. Therefore, these economical measurements, PCA, and cluster analysis can be used to determinate the authenticity of a tequila brand.


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