Intense wildfires destroy everything in their path, including archaeological sites. Prehistorically, archaeological sites were regularly and intentionally burned. In what ways does burning affect those sites? With increased wildfire activity, research has begun to describe the effects of fire on archaeological materials through post-fire and experimental treatment, yet, little is known about the effects of fire on microbotanical remains, such as starch granules. Although some studies address the impact of fire on starch-rich foods, there is virtually no research on the fire effects of starch granules embedded in ground stone tools. The current study examines changes in the morphology of starch granules embedded in ground stone tools before and after exposure to flame combustion. A measurable amount of intact and identifiable starch granules was recovered from all of the treated samples. However, significantly fewer intact, identifiable granules were found as tools were exposed to higher temperatures for longer periods of time.

Starch Extraction and Identification

All samples were processed for starch following standard lab procedures (see Louderback et al., 2015). Large ground stone tools were surface sampled for ~5 min with a sonicating toothbrush and DH₂O ; smaller tools were sampled after sonicating in a DH₂O bath for 3 min. Each sample was rinsed through an Endecott mesh sieve (125µm) with DH₂O and sample liquid <125µm was retained in a 50mL test tube. Sample extract >125µm was discarded. The sample liquid (presumably containing starch) was centrifuged for 3minutes at 3,000 rpm. The supernatant was decanted and the sample pellet was transferred to a sterile 15mL tube with DH₂O , mixed with a vortex, centrifuged for 3minutes at 3,000 rpm, and decanted. Samples were resuspended with ~7mL of heavy liquid (lithium heteropolytungstate; specific gravity 2.2), vortex-mixed, and centrifuged for 15 minutes at 1,000rpm. Heavy liquid separates the lighter organic material, including starch granules, from the heavier content. The lighter organics were collected using a pipette and transferred to a new, sterile 15mL tube. Residual heavy liquid was rinsed from the organic matter with ~10mL of DH₂O , mixed with a vortex, and centrifuged for 3 minutes at 3,000rpm; twice. The samples were decanted and rinsed with ~7mL of acetone, vortex-mixed, and centrifuged for 3 minutes at 3,000rpm. After decanting one final time, processed samples were left to dry overnight before mounting on glass slides for microscopy observation.

Starch granules were measured and described based on a set of established criteria, including maximum length through the hilum (µm), hilum position, two-dimensional shape, clarity of the extinction cross, and the presence or absence of surface features such as fissures and pressure facets (Brown and Louderback 2020; Holst et al., 2007; ICSN, 2011; Joyce et al 2021; Louderback and Pavlik, 2017; Musaubach et al., 2013; Piperno et al., 2004, 2009; Reichert, 1913; Torrence and Barton, 2016).  These criteria were recorded as absent (0) or present (1) and expressed as a percentage of the occurrence.

Slides were scanned with a transmitted brightfield microscope using polarizing filters and Nomarski optics (Zeiss Axioskop Imager M1, Zeiss International, Göttingen, Germany). Observations were obtained using randomly generated X and Y coordinates on the microscope stage. All starch granules observed within each field of view were measured and described. Images and measurements at 400X were captured under polarized light (POL) with a digital camera (Zeiss AxioCam MRc5) using Zen Core 3.1 imaging and measurement software. The presence of surface features was imaged and recorded in differential interference contrast (DIC) micrographs.  

Identification and Quantification of Treated Starch

The relative proportions and arrangements of amylose and amylopectin affect both granule morphology and functionality (Vamadevan and Bertoft, 2015). They can also cause various mechanical and chemical changes, such as granule swelling (gelatinization), pasting, and loss of birefringence in response to different food processing methods (Cai et al., 2014; Crowther, 2012; Di Poala et al., 2003; Gong et al., 2011; Mason, 2009; Wang et al., 2014).

Exposing starch granules to heat can cause morphological damage such that granules may be difficult to identify. Granules exposed to high temperatures have been shown to enlarge in size (swell) and gelatinize, losing their birefringence cross, amylose layers, and surface characteristics before dispersing away entirely (Crowther, 2012; Singh et al., 2002; Vamadan and Bertoft, 2015). Congo Red dye (empirical formula C32H22N6O6S2Na2) reacts with granules when amylose layers are broken down (usually due to cooking or some other form of damage), staining them orange to vivid red. Undamaged starch granules (with intact amylose layers), however, are hydrophobic and, therefore, do not react with Congo Red (Lamb and Loy, 2005).  

To measure the percent or degree of damage to starch granules, milled and burned samples were treated with a Congo Red solution following a protocol similar to Lamb and Loy (2005). Dried residue samples were resuspended with 25µL of Congo Red and absorbed the stain for 15 minutes before diluting with 100µL of DH₂O (1:4 ratio). Slides were prepped with 25µL of the hydrated sample and a cover slip was applied but not affixed with fingernail polish (experimentation found that clear fingernail polish interfered with Congo Red stain). The liquified stained samples dried within ~45 minutes, therefore, all observations were photographed immediately. Microscope observations on slides from the milled and burned (close proximity) samples were obtained using randomly generated X and Y coordinates. Samples exposed directly to flame, however, produced fewer measurable granules, so observations were collected by scanning the entire slide.

Size distributions from the control, milled, and burned samples were statistically analyzed with a Kolmogorov-Smirnov (K-S) test to determine any significant difference in the distribution of median lengths. Boxplot-stripcharts were generated in R Statistical Software (v4.0.2; R Core Team 2020) to show the quartile summary variation

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