Département de médecine moléculaire, Faculté de Médecine, Université Laval, Québec, Canada.
Géopole du Pacifique Sud, Université de Polynésie Française, Faa'a, Tahiti, French Polynesia.
Sci Rep. 2023 Aug 12;13(1):13122. doi: 10.1038/s41598-023-40325-z.
Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry.
大溪地珍珠，由黑唇珍珠贝 Pinctada margaritifera 人工养殖而成，以其独特的颜色和较大的尺寸而闻名，使珍珠产业成为法属波利尼西亚经济的重要组成部分。了解珍珠形成的机制对于实现质量和可持续生产至关重要。在本文中，我们通过研究珍珠的旋转来探索珍珠形成的过程。在这里，我们使用深度卷积神经网络展示了珍珠在形成过程中在牡蛎中的旋转与其最终形状之间的直接联系。我们提出了一种新的非侵入性珍珠监测方法，并提出了一种基于旋转数据预测珍珠最终形状的模型，其准确性为 81.9%。这些新颖的资源为研究和增强我们对珍珠形成整体机制的理解提供了新的视角，为提高珍珠生产和行业质量控制具有潜在的长期应用价值。
