کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6307700 1618837 2015 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم محیط زیست شیمی زیست محیطی
پیش نمایش صفحه اول مقاله
NIR spectroscopy as a tool for discriminating between lichens exposed to air pollution
چکیده انگلیسی


- Lichens as biomonitors of air pollution.
- NIR spectroscopy for analysing lichen samples.
- PCA as a multivariate display method to visualise the NIR data.
- LDA to discriminate between lichens according to their exposure to pollutants.

Lichens are used as biomonitors of air pollution because they are extremely sensitive to the presence of substances that alter atmospheric composition. Fifty-one thalli of two different varieties of Pseudevernia furfuracea (var. furfuracea and var. ceratea) were collected far from local sources of air pollution. Twenty-six of these thalli were then exposed to the air for one month in the industrial port of Genoa, which has high levels of environmental pollution.The possibility of using Near-infrared spectroscopy (NIRS) for generating a 'fingerprint' of lichens was investigated. Chemometric methods were successfully applied to discriminate between samples from polluted and non-polluted areas. In particular, Principal Component Analysis (PCA) was applied as a multivariate display method on the NIR spectra to visualise the data structure. This showed that the difference between samples of different varieties was not significant in comparison to the difference between samples exposed to different levels of environmental pollution.Then Linear Discriminant Analysis (LDA) was carried out to discriminate between lichens based on their exposure to pollutants. The distinction between control samples (not exposed) and samples exposed to the air in the industrial port of Genoa was evaluated. On average, 95.2% of samples were correctly classified, 93.0% of total internal prediction (5 cross-validation groups) and 100.0% of external prediction (on the test set) was achieved.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemosphere - Volume 134, September 2015, Pages 355-360
نویسندگان
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