A systematic approach to the recovery and identification of starches from carbonised deposits on ceramic vessels

Journal of Archaeological Science 39 (2012) 3483e3492

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A systematic approach to the recovery and identification of starches from carbonised deposits on ceramic vessels Hayley Saul a, *, Julie Wilson b, Carl P. Heron c, Aikaterini Glykou d, Sönke Hartz e, Oliver E. Craig a a

BioArch, University of York, S-Block, Heslington, York, YO10 5DD, UK Departments of Mathematics and Chemistry, University of York, Heslington, York, YO10 5DD, UK c Archaeological, Geographical and Environmental Sciences, University of Bradford, Bradford, BD7 1DP, UK d Graduate School ‘Human Development in Landscapes’, Institute of Prehistoric and Protohistoric Archaeology, University of Kiel, Germany e Stiftung Schleswig-Holsteinische Landesmuseen, Schlob Gottorf, D-24837 Schleswig, Germany b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 January 2011 Received in revised form 24 April 2012 Accepted 28 May 2012

Starch granules are being successfully recovered from an increasing range of artefacts. Here we present the recovery of starches from carbonised ceramic ‘foodcrusts’ from late Mesolithiceearly Neolithic residues at the site of Neustadt in northern Germany. A method for investigating background loading of residues with contaminant starches is proposed by comparing interior ‘foodcrusts’ versus exterior ‘sooting’, for the purposes of eliminating samples with insignificant quantities of grains from subsequent identification procedures. The classification of starches to plant taxon is traditionally achieved by manual observations and measurement of nominal and ratio morphological variables. Here, we present a method for the automated classification of granules, using software developed in-house. The results show that when multiple granules are considered, the species selected as modern reference examples can be classified to high levels of specificity. When applied to the archaeological samples we show that wild plant resources persist in importance across the transition to agriculture, with high proportions of granule forms consistent with acorn (Quercus sp.) occurring in all samples. Hazelnut (Corylus avellana) types are less well-represented suggesting it was not an important food in the context of pottery, and may have been over-represented in the repertoire of hunteregatherer resources. Cereals are not represented in any of the samples, supporting the notion that their adoption may have been a slow process, occurring more gradually than for other domesticated foods, or that they were not initially processed in ceramic vessels. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Starch Automated classification Transition to agriculture Wild plant foods Domestication Foodcrust Ceramic residues

1. Introduction Our understanding of the uses and values of plants in the past is limited in comparison to other foodstuffs, and this is especially true in prehistory. This is in large part because of the poor preservation of macroscopic plant remains, the limited scope for bimolecular techniques to detect all but oily and waxy plant products (Charters et al., 1997; Evershed et al., 1991; Evershed, 2008), and also our inability to securely tie archaeobotanical remains in sediments with specific anthropogenic causes. Plant microfossils, such as pollen, starches and phytoliths, often preserve much better than other plant tissues and offer the best target for analysis. Recent methodological advances have led to * Corresponding author. University of York, BioArch, S-Block, PO Box 373, Heslington, York YO10 5DD, UK. Tel.: þ44 1904 328806. E-mail addresses: [email protected], [email protected] (H. Saul). 0305-4403/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jas.2012.05.033

the increased recovery and identification of plant microfossils from challenging substrates, such as dental calculus (Hardy et al., 2009; Henry and Piperno, 2008; Lalueza Fox and Péz-Pérez, 1994; Lalueza Fox et al., 1996), stone tool residues (Briuer, 1976; Kealhofer et al., 1999; Perry, 2004; Piperno and Holst, 1998; Piperno et al., 2000; 2004; Yang et al., 2009), coprolites (Horrocks, 2004, Horrocks et al., 2004, Reinhard and Danielson, 2005) and food residues associated with ceramic vessels (Boyd et al., 2008; Crowther, 2005; Staller and Thompson, 2002). Furthermore, there are now well established techniques for microfossil extraction from sediments that have been applied successfully (e.g. Balme and Beck, 2002; Horrocks, 2005, Horrocks and Nunn, 2007; Pearsall, 2002). The high temperatures to which carbonised deposits have been exposed can accelerate tissue degradation in plants making them more prone to subsequent organic degradation and enzymatic attack in soils (Haslam, 2004). However, the durability of many

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plant microfossils is attested by their recovery from heavily carbonised deposits that have also undergone mechanical processing in some instances. For example, pollen has been recovered from Late Neolithic British vessels leading to the suggestion that they were used for the ritual preparation of henbane (Hyoscyamus niger) (Barclay and Russel-White, 1993) or as mead containers due to the presence of meadowsweet peaks (Filipendula ulmaria) (Barclay, 1983). Criticisms of pollen as a useful food indicator have stressed the very small proportions being considered as evidence, and the high mobility of the microfossil (Long et al., 1999, 2000). Phytoliths, with their much greater thermal durability, are having an increasingly consistent role in the analysis of charred plant deposits (Lusteck and Thompson, 2007). For example, at Kebara Cave in Israel concentrations of phytoliths from wood and bark fuel could be used to confirm the locations of prehistoric hearths (Albert et al., 2000). A current limitation of phytolith research is its application outside arid and tropical regions, where the rate of evapotranspiration is high (Madella et al., 2009). Thus, its usefulness in a temperate European context is only recently being systematically explored (Saul et al., forthcoming), and the taxonomic resolution at which it is possible to operate using phytoliths is unknown. In this paper we aim to test a range of carbonised ceramic surface deposits formed experimentally and from prehistoric submerged contexts in Northern Germany (dating from 4600 cal BC to 3700 cal BC) to see if starches preserve and can be extracted. Carbonised deposits on the inside of cooking pots (Fig. 1) are found in a wide range of archaeological contexts (e.g. Boudin et al., 2009; Craig, 2004, Craig et al., 2007; Crombé et al., 2002; Andersen and Malmros, 1984) and may provide a good micro-environment for starch preservation if degradation rates are retarded when they are in association with artefacts, as has been suggested by Haslam (2004). Crucially, plant microfossils identified within the matrix of carbonised surface residues are likely to derive from vessel use (Crowther, 2005). In addition to testing whether starch granules survive in carbonised food residues on ceramics, here we trial automated classification of extracted granules from these contexts using image analysis and pattern recognition of both wild and domesticated plant starch morphologies (Wilson et al., 2010). The study aims to answer the following three questions: i) Do starches preserve in carbonised deposits from prehistoric temperate waterlogged sites? ii) Are these starches representative of the use

Fig. 1. A carbonised ‘foodcrust’ deposit on the interior rim of a funnel beaker vessel.

of the vessel? iii) Can the starches be identified to a plant food source? 1.1. Ancient starch analysis Starch granules are semi-crystalline, water insoluble grains (Miles et al., 1985) that can account for 16e24% of the total weight of a storage root, tuber crop or other underground storage organ (Hoover, 2001). Starches are carbohydrate polymers deployed by plants into energy storage organs. Within the field of plant microfossil analysis, starches have long been known for their potential as an indicator of diet, as they display morphological variation that is taxonomically significant (Torrence and Barton, 2006). Starches have a huge range of molecular and granular structures that bear on the physico-chemical properties of granules, and their susceptibility to taphonomic processes (Leach, 1965; Sivak and Preiss, 1998). A temperature range of 50e85  C is often cited as the point at which gelatinisation of the grains begins (Hoover, 2001; Tang et al., 2001; Torrence and Barton, 2006), and whilst this is true in general, it simplifies the potential range of responses to processes like cooking (Gaillard and Bowler, 1987; Henry et al., 2009). Grain size is related to starch hydration (Torrence and Barton, 2006), with water molecules inhabiting the crystalline regions of the amylopectin molecules (Ratnayake and Jackson, 2006). Small granules generally have less swelling power, or water-binding capacity, meaning gelatinisation occurs relatively quicker than for larger granules. However, a higher amylose content also lowers swelling capacity, buffering the granules from gelatinisation (Frederiksson et al., 1998; Tester and Morrison, 1990), so in theory gelatinisation in granules with a high amylose:amylopectin ratio could be more effectively offset in larger granules than smaller ones. Starch degradation and morphological alteration is a complex issue, and current knowledge advocates as much potential for reorder as for catastrophic disorder of granule micro-structure as a consequence of cooking (Ratnayake and Jackson, 2006; Zarillo et al., 2008). This is especially true considering we know so little about these features in most wild northern European species. Annealing (low temperature heating with low water content), as well as heatemoisture treatment (low water content with high heat) are known cooking methods that increase gelatinisation temperatures, and in the former case also reduce leaching and granular swelling (Tester and Debon, 2000). A survival potential is also posed by resistant starch which is resilient against hydrolysis, because starch is tightly packed in granules in a radial pattern and is relatively dehydrated (Sajilata et al., 2006). Experiments with cooked tubers showed that in samples with gelatinised starch a proportion of the grains were resistant grain varieties, and resisted all thermal alterations to morphology (Gott et al., 2006). Microscopy of archaeological starch granules in Egyptian bread was being conducted as early as 1905 (Samuel, 1996). The use of starches as dietary indicators from carbonised pottery foodcrusts is increasing in microfossil research although applications to archaeological problems have been limited, with New World maize (Zea mays) agriculture and Australasian taro (Colocasia esculenta) domestication being the main areas of research. Pottery residue analyses of starch first showed that maize cultivation was practiced widely on the North American Great Plains well before European contact (Boyd et al., 2006). A follow-up study of foodcrust starch extended the impact of maize with much earlier evidence from Late Woodland sites on the eastern Canadian Plains around 700 AD (Boyd et al., 2008). In New Ireland Lapita pottery from Papua New Guinea, the co-occurrence of raphides with starches consistent with the Aroid family has suggested the processing of taro (C. esculenta) c. 3300 BP (Crowther, 2005). Additionally, ground and

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charred archaeological cereal starches were detected from prehistoric Greek and Bulgarian sites in recognisable forms (Valamoti et al., 2008) and almost completely carbonised cell structures of wheat (Triticum sp.) were recovered from Viking Age bread remains from Våstergården, Denmark (Hansson and Isaksson, 1994). Archaeological starches have contributed to debates about the importance of plant foods from contexts as early as the Polish Palaeolithic at c. 11,380  95 BP. In this case starches were discovered in preserved parenchyma tissue (Kubiak-Martens, 1996). Remarkably, starches have been reported to endure since the Pleistocene c. 180,000 years ago at Sai Island in northern Sudan, in association with pounding implements (Van Peer et al., 2003). Documentation of granules such as those in cooked Egyptian breads, in unaltered forms (Samuel, 1996) shows that the complex physico-chemical properties of such diverse grain morphologies means gelatinisation does not necessarily follow from cooking. The preservation of starch granules in carbonised deposits from such diverse contexts and climates as the arid and semi-arid climes of Australia (Atchinson and Fullager, 1998; Parr and Carter, 2003), tropical regions like Papua New Guinea (Barton and White, 1993; Therin et al., 1999), and temperate locations like northern Europe (Hardy, 2004, 2007) is therefore of great interest. 2. Materials and methods 2.1. Reference plant materials Twelve modern reference species were selected mainly on the basis of their occurrence as macrofossils on later prehistoric sites in northern Europe, their availability, and their potential value as a starchy food in prehistory. Domesticated cereals are represented by einkorn (Triticum monococcum), whilst hazelnuts (Corylus avellana) and acorns (Quercus sp.) were chosen due to their implied importance as wild staples (Holst, 2010; Kubiak-Martens, 1999; Mason, 2000). Other species included are edible sedge and reed types (Cyperus longus, Typha latifolia, Sparganium erectum), lordsand-ladies tuber (Arum maculatum), bracken fiddlehead (Pteridium sp.), root of wild horseradish (Armoracia rusticana), meadowsweet (F. ulmaria), and beechnut (Fagus sp.). Whilst clearly not exhaustive of the plants available during the period, these species are commonly represented on archaeological sites, and in many cases derive from plants that are high producers of edible components. Acorus calamus is not indigenous to northern Europe, and was only naturalised in the sixteenth-century (Vojtisková et al., 2004) and therefore should not be present in the prehistoric ceramic vessels. However, including this species as a control allows the accuracy of the classification to be assessed, within the parameters of the dataset with which training was carried out. The fact that granules were classified as A. calamus shows that they are most morphologically similar to this species than to any other in the training set and highlights the limitations of the reference collection size. 2.2. Experimental foodcrusts In order to study the effect of cooking on starch granules, whole einkorn (T. monococcum) and acorn (Quercus sp.) were boiled separately in water for 3 h in replica ceramic vessels. Each cooking experiment was repeated three times in the same vessel. Boiling was chosen as it is a cooking process associated with the use of ceramic vessels, attested archaeologically by residues that frequently appear as a horizontal band on the rim of vessels. During the experiment spot temperatures were recorded with a thermocouple at regular intervals both in the liquid and on the surface of

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the pot. Foodcrust rapidly accumulated on the inside of the vessels, to a thickness of up to 5 mm. The pots were air dried and sealed for further analysis. 2.3. Archaeological ceramic vessels Samples were obtained from the site of Neustadt (LA 156, Kreis Ortholstein), northern Germany (54 070 0500 N/10 560 1600 E). The site is a substantial coastal dump of cultural debris that spans the transition to agriculture, which occurred in the centuries around 4100 cal BC in this region (Hartz et al., 2002). Based on the large numbers of terrestrial and marine mammals found, the site was likely to have been a hunting and fishing station. The ceramics were dated based on their distinctive typologies as belonging to the late Mesolithic Ertebølle (EBK) pointed-based style or Early Neolithic flat-based Funnel Beaker (TRB) form. Associated dates on faunal remains were in agreement with the typological dating (Glykou, 2011) and indicate that the site was used from ca. 4300 cal BC to 3800 cal BC. Several authors (e.g. Fischer, 2002) suggest that a shift from wild to domesticated plant foods, such as cereals, occurred during this period, providing an additional incentive for selecting this site. All finds come from a peat context ranging from c. 0.2e0.6 m in thickness sealed under a stratified thick mud layer beneath a sand layer. The excavators consider the find layer to be compacted after deposition as a result of intensive production activities in this area, such as carcass processing, fishing and flint knapping. Although it was not possible to separate Ertebølle from Funnel Beaker ceramics by visible stratigraphy, Ertebølle vessels were predominant at the base of the vertical distribution, and Funnel Beakers in the upper portion (Glykou, 2011). The residues are extremely well preserved (Fig. 1), in some cases to a thickness of up to 3 mm. Twenty-six surface deposits were analysed from 18 EBK and 8 TRB vessels. Four soot deposits were available from the vessels’ exteriors. These were sampled to provide negative controls as they are far less likely to derive from the vessels’ contents than interior deposits. However, their position on the vessel, such as the rim or body, was recorded to allow the possibility of over-spill to be assessed. Soil samples were acquired from the stratum associated with the ceramics, to provide further controls for contamination. Five separate sub-samples of between 250 mg and 300 mg (average weight 276 mg) were tested. Faster granule degradation rates have been proposed for starch granules in soil than for those associated with artefacts, due to factors such as increased enzyme accessibility in the absence of the protective micro-environment of the ceramic (Haslam, 2004). Therefore interior versus exterior residue counts (mg1) were used as the primary indicator of contamination, with the soil samples serving as a supporting measure. 2.4. Starch extraction Carbonised residues (w1 mg) from experimental and archaeological vessels were removed with a scalpel, accurately weighed into sterile plastic tubes and treated with hydrogen peroxide (H2O2; 10%, 10 ml; 15e30 min), whilst gently disaggregating the carbonised matrix with a spatula. The tubes were centrifuged (2665 rcf; 3 min) and the supernatant removed. After washing three times with UltraPure water, the remaining residue was made up to a 1 ml suspension with UltraPure water. The supernatant, containing liberated starch granules, was added to glass microscope slides that were then left to dry at room temperature. This approach leads to quantities of fine carbonised detritus being included in the mounts, but eliminates the potential issue of differential settling rates of plant microfossils, identified with phytoliths (Stromberg, 2007).

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Scientific, UK), together with two aliquots of starch powder (1 ml; 150 mg ml1; Zea mays, commercially available) to provide a positive control. Thermally-stable a-amylase (0.25 ml; undiluted, Sigma, UK) was added to one set of the aliquots and UltraPure water added to the other. The plates were incubated for 24 h at 25  C and the contents of each well was then mounted in a single plane in glycerol and left at room temperature for a further 24 h before viewing.

Table 1 The species included as modern references, showing the number of images, the number of granules in total for each species and the number used for training and testing the classification algorithm. Class Species # 2 3 4 5 6 7 8 9 10 11 12

Common name

Acorus calamus Arum maculatum Cyperus longus Armoracia rusticana Triticum monococcum Pteridium sp. Filipendula ulmaria Quercus sp. Typha latifolia Fagus sp. Sparganium erectum Corylus avellana hazelnut

Sweet flag Lords-and-Ladies Galingale Horseradish Einkorn Bracken Meadowsweet Oak (acorn) Reedmace Beech (beechnut) Bur-reed Hazel (hazelnut)

Total

# # # # Images Granules Training Test 6 6 6 6 6 5 6 8 6 6

611 265 89 63 149 255 191 40 344 222

100 100 50 50 100 100 100 30 100 100

511 165 39 13 49 155 91 10 244 122

6 5

71 653

50 100

21 553

72

2953

980

1973

2.6. Microscopy and image acquisition Polarising microscopy observations of the starch extracts were obtained with an Olympus IX71 inverted microscope (Olympus, UK) fitted with a ColorView III microscope camera (Olympus, UK) linked to the Digital Image Solutions program CellD, version 2.6 (Build 1200) (Olympus, UK). A grid (200 mm) was imposed over the slide and all the starches on the slide were counted. The concentration of granules in exterior soot deposits was used to determine the level considered due only to contamination. Since counts of up to 100/ mg1 were found in exterior samples, any interior samples with less than 100 starch granules mg1 were excluded from subsequent analysis. Images of the starch granules were obtained at a resolution of 2576  1932  24 bit bitmap (BMP). The magnification was set at 600. Images were taken in pairs, consisting of one photograph taken in polarised brightfield and a corresponding photograph in cross-polarised brightfield. The number of images (Table 1) obtained differs between species due to differences in the ease of extraction and slide preparation for fresh plant specimens, with some species having much more extraneous organic material than others.

Dried samples were mounted in a single plane in glycerol and left at room temperature for at least 24 h before viewing. Hydrogen peroxide treatment was tested on modern C. avellana reference specimens with no noticeable effect. For extraction of starch from soil an optimised multi-stage method was preferred. Ten millilitres of 6% H2O2 was added to each sample which were manually disaggregated during soaking for 1 h. Samples were sieved using a piece of 200 mm mesh and the retained H2O2 fraction was centrifuged for 5 min at 2665 rcf. After washing four times with 5% Calgon, the deflocculated samples were washed four times with UltraPure water. The UltraPure water was then siphoned off and the samples left to dry overnight at room temperature before 3 ml of sodium polytungstate solution (1.7 specific gravity) was added and the samples centrifuged at 347 rcf for 3 min. The top layer containing starches was then removed to a separate test tube, and the sodium polytungstate washed from this fraction by diluting the sample with 3 ml of UltraPure water, centrifuging (347 rcf, 3 min), before disposing of the upper portion of the supernatant. These washes were repeated 4 times then the sample was dried and mounted as for the ceramic residue samples.

2.7. Image analysis and starch classification In order to extract features for classification, the boundaries of individual granules were first identified in the images. Where possible this was achieved automatically using edge detection methods. Sudden changes in pixel intensity at the edges of objects allow their identification by analysis of the gradient, or rate of change of image intensity. Pixels with a gradient magnitude above a threshold based on the image’s intensity statistics were used to define the boundaries of the objects. Any objects in contact with the edge of the image (e.g. Object A, Fig. 2) were eliminated as possibly incomplete granules. Wilson et al., (2010) used shape descriptors, including a measure of concavity, to recognise and delete damaged granules. This was not possible here, as some perfectly good granules have

2.5. Amylase degradation To confirm the presence of starch, two 1 ml aliquots, extracted as above from five of the archaeological samples (Table 5) were placed in separate wells in a polystyrene tissue culture plate (Fisher

Table 2 Confusion matrix showing the classification of the training data with the twelve species numbered as in Table 1. Row numbers show the actual species and column numbers show the species assigned by the classifier. Thus, the percentage of granules of species j classified as species k is shown in the kth column of row j so that each row total is 100. The percentage of the training data that was correctly classified is therefore indicated in bold. The specificity of the classifier for each species is also shown.

1 2 3 4 5 6 7 8 9 10 11 12 specificity

1

2

3

4

5

6

7

8

9

10

11

12

92 1 0 0 0 1 2 0 1 0 0 0 0.95

0 72 10 2 8 2 0 0 2 0 8 0 0.77

0 1 66 0 1 2 0 0 0 0 0 0 0.89

0 0 0 92 0 0 0 0 2 0 0 0 0.96

0 9 10 0 74 1 3 0 5 2 4 4 0.70

0 6 2 0 3 84 2 0 2 0 0 2 0.84

8 1 2 0 1 3 90 0 1 1 0 0 0.85

0 0 0 0 0 0 0 100 0 0 0 0 1.00

0 4 2 2 7 2 0 0 74 3 2 2 0.78

0 0 2 0 2 1 0 0 5 89 4 5 0.85

0 1 0 0 1 3 1 0 0 2 80 0 0.83

0 5 6 4 3 1 2 0 8 3 2 87 0.76

# Wrong (class)

Real class position

1 2 3 4 5 6 7 8 9 10 11

Acorus calamus Arum maculatum Cyperus longus Armoracia rusticana Triticum monococcum Pteridium sp. Filipendula ulmaria Quercus sp. Typha latifolia Fagus sp. Sparganium erectum

6 6 5 6 6 5 6 4 6 6 6

5 6 5 5 6 5 5 4 6 5 3

1 0 0 1 0 0 1 0 0 1 3

12

Corylus avellana

5

5

0

2nd e e 3rd e e 2nd e e 3rd 3rd, 3rd, 2nd e

(7)

(1)

(1)

(12) (5, 9,10)

Most likely species

Quercus sp. Cyperus longus Quercus sp. Acorus calamus Corylus avellana Quercus sp. Quercus sp. A. calamus Quercus sp. Quercus sp. Quercus sp. A. calamus Quercus sp. Pteridium sp. Quercus sp. Quercus sp. Quercus sp. 0 0 0 2 42 0 0 2 0 0 0 0 0 0 0 0 0 4 0 0 0 0 2 7 0 0 3 7 17 26 0 0 13 5 0 0 0 0 5 0 1 0 0 0 0 0 4 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67 9 100 2 5 79 51 27 40 75 76 0 61 29 85 50 68 29 9 0 0 16 5 34 30 0 2 0 13 4 14 11 3 0 0 4 0 4 0 12 3 0 0 12 14 8 4 43 4 27 11 0 9 0 2 0 0 0 0 20 2 0 0 0 7 0 0 0 0 9 0 0 0 0 0 9 0 0 0 25 0 0 0 0 0 0 30 0 15 0 0 0 0 20 2 0 0 0 0 0 0 11 0 9 0 0 0 2 0 0 20 5 3 0 0 7 0 3 5

Indicates a sample that was selected for a-amylase degradation.

# Correct

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a

# Images

0 22 0 74 32 0 3 32 0 0 0 38 0 0 0 0 0

Species

45 23 1 46 19 43 70 66 5 60 29 24 23 14 27 30 19

Class

EBK EBK EBK TRB EBK TRB TRB EBK EBK EBK EBK TRB EBK EBK TRB EBK EBK

Table 4 The results of the classification of images, obtained by combining the individual classifications of all test set granules within an image. The 5th column shows the number of misclassified images with the class number(s) that they were assigned to in brackets. The 6th column shows the rank of the correct class; in all cases the correct class of the image appeared in the top 3 highest scores. Species are numbered as in Table 1.

N1317 N1456 N1919 N217a N2285 N2631a N2635 N2648 N2756 N2860a N3148 N3233 N3304 N3305 N3309 N3377a N629a

a concave boundary. As shape descriptors could not be used to automatically identify the objects to be excluded from analysis, it was not always possible to distinguish between starches and some of the other organic matter seen in these images (e.g. Object B, Fig. 2). It was therefore sometimes necessary to visually identify and delete objects that were not starch granules. The extent of manual intervention necessary was dependent upon the amount of background debris. For some images, automated boundary recognition was followed up by manual separation of granules forming clumps with shared boundaries (Fig. 3). All manual intervention was performed using the software GraphicConverter (Lemkesoft GmbH). Where there was excessive background noise, rough circles were drawn around the granules to prevent other organic matter from forming objects (Fig. 4) and automated boundary recognition was then deployed only within these circles. In the worst cases, where granules overlapped background detritus, boundaries were defined manually using GraphicConverter. More problems were encountered with some species than others. A lack of debris allowed granules of Acorus calamus, Arum maculatum, Cyperus longus, and A. rusticana to be isolated and recognised automatically. Quercus sp., Fagus sp., and Pteridium sp., however required circles to be drawn around the granules within which automated identification was applied. Most C. avellana granules formed clusters that needed separation along shared boundaries, as did w30e40% of T. monococcum granules. F. ulmaria and S. erectum had lots of background debris and required manual definition of boundaries. Table 1 shows the number of images together with the number of

Corylus avellana

1 11 3 8 4 4 3 0 7 12 0 66

Sparganium erectum

12

1 1 0 0 2 5 0 0 2 2 45 3

Fagus sp.

11

1 1 0 0 0 3 0 0 5 69 5 8

Typha latifolia

10

0 6 3 0 18 5 1 0 67 3 5 3

Quercus sp.

9

0 0 0 0 0 0 0 100 0 0 0 0

Filipendula ulmaria

8

9 2 3 17 2 1 66 0 0 6 5 3

Pteridium sp.

7

7 4 5 0 0 61 6 0 4 3 10 3

Triticum monococcum

6

0 20 18 8 65 8 2 0 9 3 20 6

Armoracia rusticana

5

1 4 5 58 0 2 4 0 1 0 0 1

Cyperus longus

4

0 2 53 8 2 1 2 0 0 0 0 0

Arum maculatum

3

0 50 11 0 6 8 2 0 5 0 5 5

Acorus calamus

2

81 0 0 0 0 2 12 0 1 3 5 1

Number of granules

1

Context

1 2 3 4 5 6 7 8 9 10 11 12

Sample

Table 3 The results of using the trained classifier to classify the granules of known species in the test set. Species are numbered as in Table 1. The percentage of granules correctly classified is given in bold.

Table 5 The results of classifying archaeological starches, showing those samples used for amylase degradation. The most likely species given is the species in the reference collection that most granules within a sample are assigned to and therefore only shows the reference species that the granules are morphologically most similar to. Many of the samples contain high proportions of starches morphologically similar to Quercus sp.

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Fig. 2. A masked image of Arum maculatum granules with (A) an unusable starch granule overlapping the edge of the image and (B) an unwanted piece of cellulose. Magnification 600.

granules extracted for each of the modern reference species. For most species, well over 100 granules were extracted. For each granule, a total of 26 feature variables were recorded for classification. Variables were calculated based on size and shape of the granule, its boundary curvature and concavity and the shape of its polarisation cross. More detail on these variables can be found in Wilson et al., (2010). In addition, statistical measures were calculated from pixel intensities within the granule for both the polarised and cross-polarised images as well as the gradient magnitudes. Data for two-thirds of the granules (chosen randomly) from each modern species were used to train supervised learning algorithms and the remaining data used as an independent test set. Four different classifiers were trained; a self-organising map (SOM), learning vector quantisation (LVQ) and support vector machines

Fig. 4. The manual intervention of rough black circles around the granules allows the edge detection to be restricted and only objects located within these areas considered. Magnification 600.

(SVM), both (SVM_linear) and with a radial basis function kernel (SVM_RBF). Classification of the training data was used to assess the internal consistency (both sensitivity and specificity) of the individual classifiers as well as combinations of classifiers. The best classification system was then applied to the test data from the modern reference starches not used during training, to test the extrapolation of the results to unseen data. The trained classifier was then applied to experimentally cooked examples of T. monococcum and acorn (Quercus sp.) to test classification after thermal alteration. Finally the classifier was applied to archaeological starches from carbonised surface deposits from Neustadt. 3. Results and discussion 3.1. Starch survival in experimentally carbonised deposits Starch granules in various states of heat-alteration are preserved in carbonised deposits. This emphasises the still incomplete understanding of the processes and conditions of morphogenesis. The experimental acorn and einkorn cooking pots yielded 608 granules mg1 and 184 granules mg1 respectively. These were exposed to temperatures of at least 100  C for more than 3 h and reached higher temperatures (>120  C) as the water content of the pots decreased and the residues dehydrated on the ceramic surface. The granules survived repeated cooking in the vessels, confirming that thermal degradation does not occur uniformly at a threshold temperature (Torrence and Barton, 2006). 3.2. Starch survival in archaeological carbonised deposits

Fig. 3. Without manual intervention, the clumps of hazelnut (Corylus avellana) granules lead to composite objects as shown in (a). After boundaries between granules are added manually, separate granules can be identified and treated individually in subsequent morphological analysis. Magnification 600.

No granules were found in any of the archaeological samples that were subjected to a-amylase, whereas counts of between 252 and 284 mg1 were obtained for the five controls without amylase, suggesting conclusively that these are starch. The positive Z. mays control showed no starch, whilst the negative Z. mays starches (>500 mg1) remained unaltered. Therefore it would seem that, at least in this burial environment, archaeological carbonised deposits of at least 6000 years old do contain starch. The minor discrepancy between counts mg1 of samples put forward for automated classification and counts mg1 of the same samples used for a-amylase degradation may result in part from the non-uniform nature of starch content in the residues.

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3.3. Do granules represent vessel use? Fig. 5 shows the number of starch granules per mg for archaeological samples. A two-tailed t-test showed a statistically significant difference (p < 2.25E-07) between the interior and exterior deposits (interior n ¼ 22, exterior n ¼ 4). The greater number of granules inside the pots is consistent with a deliberate packing of the ceramics with starchy foodstuffs. A small number of the interior samples (indicated by empty bars in Fig. 5) showed low counts (0e70 granules mg1) and were removed from further analysis as such levels could be due simply to contamination rather than food preparation. Soil samples showed very low starch levels, with granule counts of just 0.01 to 0.02 granules mg1. This is much lower than counts from most sooty exteriors, and may be due, at least in part, to faster degradation rates for granules easily accessed by enzymes, but sooty deposits with significantly higher counts could indicate over-spill during use. The interior deposits with significantly higher granule counts may therefore be considered representative of deliberate vessel use.

3.4. Automated starch classification 3.4.1. Classification of the training set The best results on the training set were obtained using the single SVM_RBF classifier. Each cell in Table 2 shows the percentage of grains of the species corresponding to the column label that were classified as the species corresponding to the row label. For example 92% of Class 1 (A. calamus) grains in the training set were correctly classified, but 8% were assigned to Class 7 (F. ulmaria). Thus the cells on the diagonal show the percentages of correctly classified granules for each species. As well as the 92% of Class 1 grains correctly classified as A. calamus, 1% of each of Class 2 (A. maculatum), Class 6 (Pteridium sp.) and Class 9 (Typha latifolia), and 2% of Class 7 (F. ulmaria) were also assigned to Class 1. The proportion of granules classified as a particular species that really are that species gives a measure of the specificity of the classifier that can then be used to improve the classification of further samples. For example, although the percentage of correctly classified granules is high for both

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A. calamus at 92% and C. avellana at 87%, few granules are incorrectly classed as Acorus calamus, whereas more granules from other species are also classed as Corylus avellana. The specificity of the classifier for each species is given in the final row of Table 2. It can be seen that the specificity, as well as the sensitivity, is high for A. calamus and Quercus sp. Only Fagus sp. (beechnut) and C. avellana (hazelnut) have substantial numbers of similar sized granules, but granules of both these species are almost perfectly round. 3.4.2. Classification of the test set The trained SVM_RBF classifier was used to classify granules in the independent test set (those granules not used during training) in order to assess its predictive ability. The results are shown in Table 3. Although there are very few acorn granules in total, all those in the test set are classified correctly due to their distinctive shapes. A. calamus granules also classify well, with 81% of the 511 granules in the test classified correctly, although 12% of all F. ulmaria (meadowsweet) granules are also classed as A. calamus. F. ulmaria do appear to be similar in shape, but are generally larger than A. calamus granules. The classification of individual grains for other species is less successful compared to the training set data. It may be that the classification of A. rusticana and S. erectum could be improved by increasing the number of granules in the training set. The trained classifier was then used to classify multiple granules from a particular species. This was achieved by combining the classification of the individual test set granules within an image. The number of granules classified to a particular species in each image was multiplied by the specificity for that species as determined on the training data (final row Table 2) to give a score for that species. If all classifiers were used, this allowed for a slight improvement in the results with a more accurate combined score. These results are shown in Table 4. The scores were ranked and the species with the highest score was then taken as the classification for that image. Table 4 also shows the number of misclassified images with the class number(s) that they were assigned to in brackets. The final column shows the rank of the correct class. Of the 67 images containing test set granules, 60 were classified correctly and, in all cases the correct class of the image appeared in the top 3 highest scores. Whilst individual granules from most species do not classify particularly well, classification of a collection of granules is much more successful. This can be explained by the fact that most species have some round grains of a similar size with no particular discriminatory features and it would not be possible to classify these granules individually using any method. However, most of the species in this study do also have granules with distinctive features that allow the images to be classified correctly. 3.4.3. Experimentally cooked residues When applied to the modern cooked einkorn (T. monococcum) and acorn (Quercus sp.) the trained SVM_RBF classifier displayed mixed success. Although 87% of the granules extracted for cooked acorn were classified correctly, none of the cooked einkorn granules could be correctly identified. Although many of the acorn granules survived the cooking process with little morphological change, the experimentally cooked einkorn starches were very degraded, being rendered ‘bagel-shaped’ (Fig. 6).

Fig. 5. Granule counts mg1 for interior (F) and exterior (S) deposits from Neustadt. Counts considered insignificant are indicated only outlined bars.

3.4.4. Archaeological samples The classification outcomes for each image were combined to give a sample result. Table 5 shows the percentage of granules identified as each species, with a final column showing the most likely class for that sample, were a single species assumed present in the deposit. Thirteen have considerable proportions of granules that are morphologically similar to Quercus sp. (acorn), and 11 of those have been classified primarily as Quercus sp. morphologies.

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Fig. 6. The degradation of experimentally cooked einkorn (Triticum monococcum) shows a distinctive ‘bagel’-shape, resulting in poor identification in automated analysis. Magnification 600.

These findings correspond strongly with manual observations of the granules, suggesting that a primary role for these vessels may have been the processing of plants with acorn-like starch morphologies. These results are supported by the large quantities of acorn casings recovered from deposits at Neustadt (recorded as “N” in the macrofloral report). Other samples show equally high proportions of other species. For example, five samples suggest edible Pteridium sp., type starch in combination with C. avellana (n1), F. ulmaria, and S. erectum (n1) type starch. Little evidence for the use of cereals was found. A moderate percentage (20%) of granules classified as T. monococcum (einkorn) type was recorded in an Ertebølle vessel (N2756), albeit with a low granule count. Furthermore, none of the archaeological samples had starches that resembled the degraded T. monococcum granules observed after cooking. It could be that cereals, if present at all, were not processed in these pots or perhaps, as suggested by the cooking experiments, that cereal starches do not survive in this particular archaeological context. One reason for the poor preservation could be the low proportion (25e27%) of amylose in cereal starch (Frederiksson et al., 1998), which may not be sufficient to offset gelatinisation during formation of the foodcrusts. In five samples, a high proportion of granules are classified as A. calamus although this species is not indigenous to Europe and was not introduced until the 16th Century. This species therefore could not have been cooked in Ertebølle or Early Neolithic vessels. The fact that granules were classified as A. calamus shows that they are more similar morphologically to this species than to any other species in the training set, and highlights the limitations of using such a small reference set. For this reason, all the identifications proposed above should be considered to be tentative and preliminary, pending expansion of the reference dataset. 3.4.5. Wider significance of the findings Granule forms similar to hazelnut are only represented in one vessel as the most likely species, suggesting that macroscopic findings of hazelnut (Kubiak-Martens, 1999; Holst, 2010; Harild and Karg, 2009; Mason, 2004) may have overemphasised their importance archaeologically, in relation to other important staples. Alternatively hazelnuts were not processed in ceramic vessels as frequently as other plants. Conversely, starches with morphologies similar to acorn seem to have been intensively processed in the vessels at Neustadt, occurring in all of the samples analysed. Although this cannot be taken as definite proof of acorn processing, comparable practices are known ethnographically from Californian

Native Americans (Abrams and Nowacki, 2008; Keeley, 2002). Acorns are stripped of their outer casings, crushed roughly and heated repetitively in several changes of water to remove toxic tannins. In purely calorific terms, the intensive collection and processing of acorns for food produces similar suggested return rates to hazelnut (Rowley-Conwy, 1984, 303). However, unlike hazelnuts, Quercus sp., is a masting plant meaning it produces a substantial crop bi-, tri-, or quadrennially, rather than annually (Abrams and Nowacki, 2008). In order for acorns to make up an annual contribution either a collection area spanning multiple staggered masting regions must be available, or storage of acorn must be employed. The results may be evidencing the second option. Acorns are arguably best stored as a processed and dried flour. Up to 31% of their content can be lipid (Bainbridge, 1985), compared to only 1% for wheat (Wagnon, 1946), making them susceptible to rancidity in their fresh form. In addition, rapid destruction of a fresh store of acorns can occur with infestation by acorn weevil (e.g. Curculio nucum and Cuculio glandinium), an insect commonly found burrowed discretely into the achene. Considering calories exclusively, a duel acorn and hazelnut plant staple easily matches the return rates for land mammals (RowleyConwy, 1984), and even outdoes them, especially if the suggested kill rate is too high as has been speculated (RowleyConwy, 1984). Considering the importance animal products have played in interpretations of the transition to agriculture, these results would suggest plant resources have a higher profile role than has previously been allowed for. 4. Conclusions Here we have shown that starches can be extracted and identified from carbonised residues adhering to ceramics. Comparisons of counts mg1 from interior foodcrusts and exterior sooty deposits, as well as associated sediments show that these starches are representative of plant processing activities across the transition to agriculture at Neustadt, northern Germany. Based on these current findings, there is no support for the interpretation of a synchronous adoption of domesticated cereals with a change to Funnel Beakers, as is traditionally supposed (Fischer, 2002). However, further work is required to establish whether this absence of evidence is due to compromised starch preservation following cooking or taphonomy. Our results show that acorn (Quercus sp.) type starch and grain forms similar to some species of edible sedge and reed (Sparganium erectum, Cyperus longus) may have been important plant resources in both the Mesolithic and Early Neolithic. Classification algorithms can only assign one of the classes for which training data is available. The more representative the modern training data is, the more reliable the classification will be. The most systematic survey of starch variability including northern European species is to date represented by Reichert (1913). Though not directly measuring variability, the micrographs and accompanying descriptions suggest considerable variation in granule histology, pointing to the potential for increased identification accuracy with expansion of the reference collection. In addition to the inclusion of further species, future improvements should include training with experimentally cooked starch populations to account for heat alteration more accurately. Although the methods presented here are aimed at classifying the plant starch part of the residue which might only be a small fraction of the foods that were cooked, such systematic and extensive techniques may finally begin to rectify stunted debates about the importance of

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plants across the transition to agriculture in northern Europe (Zvelebil, 1994). Acknowledgements The authors would like to gratefully acknowledge the financial support of the Arts and Humanities Research Council (AH/ E008232/1). Some of the modern reference material was kindly given by The Royal Botanical Gardens at Kew, London. This work could not have been achieved without the support of Nicky Milner, and Richard Allen. References Abrams, M.D., Nowacki, G., 2008. Native Americans as active and passive promoters of mast and fruit trees in eastern USA. Holocene 18 (7), 1123e1137. Albert, R.M., Weiner, S., Bar-Yosef, O., Meignen, L., 2000. Phytoliths in the Middle Palaeolithic deposits of Kebara Cave, Mt. Carmel, Israel: study of plant materials used for fuel and other purposes. J. Archaeol. Sci. 27 (10), 931e947. Andersen, S., Malmros, C., 1984. “Madskorpe” på Ertebøllekar fra Tybrind Vig. Aarbøger for Nordisk Oldkyndighed of Historie, 78e95. Atchinson, J.L., Fullager, R., 1998. 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