Identification of starch granules using image analysis and multivariate techniques

The full potential of ancient starch analysis will not be fully realised until comprehensive identification keys have been established. To address this problem multivariate analysis of data recorded from digital images can be used to construct an automatic system of classification. As a first step we used digital image analysis to record 18 variables for 1998 starch granules representing 29 species which were mainly collected within one area of Papua New Guinea and largely used for food or craft production. High success rates for classifying these into their correct plant taxa were achieved by three different methods of discriminant analysis. The results confirm earlier studies that have shown that the morphology of many starch granules, especially those derived from storage organs, is distinctive to a specific plant taxon. We also demonstrate that multivariate analyses can play an important part in the establishment of starch granule classification keys.

[1]  R. Fullagar,et al.  Australian Museum starch reference collection , 1997 .

[2]  Carol J Lentfer,et al.  Starch Grains and Environmental Reconstruction: a Modern Test Case from West New Britain, Papua New Guinea , 2002 .

[3]  Dolores R. Piperno,et al.  The presence of starch grains on prehistoric stone tools from the humid neotropics : Indications of early tuber use and agriculture in Panama , 1998 .

[4]  Inna Y. Zayas,et al.  Distinguishing selected hard and soft red winter wheats by image analysis of starch granules , 1994 .

[5]  D. Piperno,et al.  Starch grains reveal early root crop horticulture in the Panamanian tropical forest , 2000, Nature.

[6]  Delwen Samuel,et al.  Brewing and baking , 2000 .

[7]  Paul Thomas Nicholson,et al.  Ancient Egyptian materials and technology , 2001 .

[8]  R. Fulcher,et al.  Genetic Aspects of Starch Granule Traits in Barley , 1994 .

[9]  Delwen Samuel,et al.  Investigation of Ancient Egyptian Baking and Brewing Methods by Correlative Microscopy , 1996, Science.

[10]  Huw Barton,et al.  Use of Stone and Shell Artifacts at Balof 2, New Ireland, Papua New Guinea , 1993 .

[11]  D. Ugent,et al.  Prehistoric remains of the sweet potato from the Casma valley of Peru , 1981 .

[12]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[13]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[14]  Carlos Herrero,et al.  Pattern recognition analysis applied to classification of wines from Galicia (northwestern Spain) with certified brand of origin , 1994 .

[15]  R. Fullagar,et al.  Clues to Stone Tool Function Re-examined: Comparing Starch Grain Frequencies on Used and Unused Obsidian Artefacts , 1998 .

[16]  D. Piperno.  Paleoethnobotany in the Neotropics from Microfossils: New Insights into Ancient Plant Use and Agricultural Origins in the Tropical Forest , 1998 .

[17]  E. Henty,et al.  Flora of the Bismarck Archipelago for Naturalists , 1984 .

[18]  Richard Fullagar,et al.  Integrating Phytoliths within Use-Wear/Residue Studies of Stone Tools , 1999 .

[19]  P Colonna,et al.  Starch granules: structure and biosynthesis. , 1998, International journal of biological macromolecules.

[20]  R. Shanmugam.  Multivariate Analysis: Part 2: Classification, Covariance Structures and Repeated Measurements , 1998 .

[21]  Matthew Spriggs,et al.  Direct evidence for human use of plants 28,000 years ago: starch residues on stone artefacts from the northern Solomon Islands , 1992, Antiquity.

[22]  Richard Fullagar,et al.  Starch in sediments: a new approach to the study of subsistence and land use in Papua New Guinea. , 1999 .

[23]  A. Shinn,et al.  Discrimination of the notifiable pathogen Gyrodactylus salaris from G. thymalli (Monogenea) using statistical classifiers applied to morphometric data , 2000, Parasitology.