Reflected Light

Ligouis, Bertrand

doi:10.1002/9781118941065.ch44

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…PPL (plane polarised light), XPL (cross-polarised light), and OIL (oblique incident light), applied with a petrographic microscope are indispensable to identify and distinguish particles in TSs by their specific light refraction properties (e.g. FitzPatrick, 2012;Ligouis, 2017;Nesse, 2013). This procedure implies that fragmented, microscopic field-of-view observations must be recombined after completion of the TS analysis.…”

Section: Introductionmentioning

confidence: 99%

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

Zickel,

Gröbner,

Röpke

et al. 2024

E&G Quaternary Sci. J.

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Abstract. Micromorphological analysis using a petrographic microscope is one of the conventional methods to characterise microfacies in rocks (sediments) and soils. This analysis of the composition and structure observed in thin sections (TSs) yields seminal, but primarily qualitative, insights into their formation. In this context, the following question arises: how can micromorphological features be measured, classified, and particularly quantified to enable comparisons beyond the micro scale? With the Micromorphological Geographic Information System (MiGIS), we have developed a Python-based toolbox for the open-source software QGIS 3, which offers a straightforward solution to digitally analyse micromorphological features in TSs. By using a flatbed scanner and (polarisation) film, high-resolution red–green–blue (RGB) images can be captured in transmitted light (TL), cross-polarised light (XPL), and reflected light (RL) mode. Merging these images in a multi-RGB raster, feature-specific image information (e.g. light refraction properties of minerals) can be combined in one data set. This provides the basis for image classification with MiGIS. The MiGIS classification module uses the random forest algorithm and facilitates a semi-supervised (based on training areas) classification of the feature-specific colour values (multi-RGB signatures). The resulting classification map shows the spatial distribution of thin section features and enables the quantification of groundmass, pore space, minerals, or pedofeatures, such nodules being dominated by iron oxide and clay coatings. We demonstrate the advantages and limitations of the method using TSs from a loess–palaeosol sequence in Rheindahlen (Germany), which was previously studied using conventional micromorphological techniques. Given the high colour variance within the feature classes, MiGIS appears well-suited for these samples, enabling the generation of accurate TS feature maps. Nevertheless, the classification accuracy can vary due to the TS quality and the academic training level, in micromorphology and in terms of the classification process, when creating the training data. However, MiGIS offers the advantage of quantifying micromorphological features and analysing their spatial distribution for entire TSs. This facilitates reproducibility, visualisation of spatial relationships, and statistical comparisons of composition among distinct samples (e.g. related sediment layers).

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Section: Introductionmentioning

confidence: 99%

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

Zickel,

Gröbner,

Röpke

et al. 2024

E&G Quaternary Sci. J.

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“…SMT 3 corresponds to randomly oriented charred vegetal fragments and plant residues in a groundmass which included a minor amount of mineral (quartz, mica, and micrite) and anthropogenic components (ash and pottery). Considering the limit of micromorphology in distinguishing between charred and humified plant tissues, organic petrology allowed for a better characterization of SMT 3 [ 59 ]. This SMT is composed of numerous fusinite tissues (charcoals and burned tissues) showing in situ tar and detrital tar particles no longer attached to the tissues from which they were generated.…”

Section: Resultsmentioning

confidence: 99%

“…Micro XRF mapping was performed on uncovered thin sections at the Institute for Archaeological Sciences of the University of Tübingen [ 58 ]. Organic petrology was performed on one sample to differentiate between charred and humified blackish plant residues [ 59 ]. These analyses were conducted on the polished block resulting from thin section production (sample OPP77).…”

Section: Methodsmentioning

confidence: 99%

High-resolution sediment analysis reveals Middle Bronze Age byre-houses at the site of Oppeano (Verona province, NE Italy)

et al. 2022

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High-resolution sediment analysis allowed us to identify two Middle Bronze Age (MBA 1, 1650–1550 cal a BCE) byre-houses at the waterlogged site of Oppeano “4D”, south of Verona (Veneto region, NE Italy). The site lies in a low-lying valley incised by the Adige River in its LGM alluvial fan. In this fluvio-palustrine environment burial and taphonomic conditions were such that the archaeological record was extremely well preserved. The wooden elements making up basal parts of nine ‘huts’ were in fact exposed at Oppeano, and so were their internal accretion deposits. These featured finely laminated dung units deriving from the stalling of small herbivores, possibly ovicaprids, intercalated with repeated accumulations of wood ash. This was produced in large and multi-stratified hearths that were exposed within each hut. Organic petrology provided evidence of the production of wood tar inside one of the studied structures. At Oppeano 4D it was thus demonstrated that these structures were not just byres or stables, but spaces that housed humans together with animals at least during some periods of the year, hence byre-houses. The identification of byre-houses in a Middle Bronze Age settlement is key for the reconstruction of socio-economic aspects of Bronze Age economy and production systems.

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“…Taylor et al 1998 ). Individual components are referred to as ‘macerals’ and studied under oil immersion with a petrographic microscope in reflected-optical and fluorescent mode (Ligouis 2017 ). Within the micro-contextual approach, organic petrology is able to confirm whether macerals are likely to be the product of burning (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…char) or of humification (e.g. Taylor et al 1998 ; Goldberg et al 2009 ; Stahlschmidt et al 2015 ; Ligouis 2017 ; Villagran et al 2017 ). While both micro-FTIR and organic petrology provide useful information, none of these methods—to this date—have been explored to distinguish animal- from plant-derived matter in archaeological contexts.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of charred organic matter in micromorphological thin sections by means of Raman spectroscopy

Lambrecht

Vera

Jambrina‐Enríquez

et al. 2021

Archaeol Anthropol Sci

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Burned or charred organic matter in anthropogenic combustion features may provide important clues about past human activities related to fire. To interpret archaeological hearths, a correct identification of the organic source material is key. In the present work, Raman spectroscopy is applied to characterise the structural properties of char produced in laboratory heating- and open-fire experiments. This reference data set is compared to analyses of three different archaeological sites with Middle Palaeolithic combustion contexts. The results show that it is possible to determine whether a charred fragment is the product of burning animal-derived matter (e.g. meat) or plant-derived matter (e.g. wood) by plotting a few Raman spectral parameters (i.e. position of G and D bands, and intensity ratios HD/HG and HV/HG) against one another. The most effective parameters for discriminating animal- from plant-derived matter are the position of the G band and the HV/HG intensity ratio. This method can be applied on raw sample material and on uncovered micromorphological thin sections. The latter greatly compliments micromorphology by providing information about char fragments without any clear morphological characteristics. This study is the first of its kind and may provide archaeologists with a robust new method to distinguish animal- from plant-derived char in thin sections.

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Reflected Light

Cited by 12 publications

References 25 publications

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach

High-resolution sediment analysis reveals Middle Bronze Age byre-houses at the site of Oppeano (Verona province, NE Italy)

Characterisation of charred organic matter in micromorphological thin sections by means of Raman spectroscopy

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