The Use of UV-Visible Reflectance Spectroscopy as an Objective Tool to Evaluate Pearl Quality

Abstract

Assessing the quality of pearls involves the use of various tools and methods, which are mainly visual and often quite subjective. Pearls are normally classified by origin and are then graded by luster, nacre thickness, surface quality, size, color and shape. The aim of this study was to investigate the capacity of Artificial Neural Networks (ANNs) to classify and estimate the quality of 27 different pearls from their UV-Visible spectra. Due to the opaque nature of pearls, spectroscopy measurements were performed using the Diffuse Reflectance UV-Visible spectroscopy technique. The spectra were acquired at two different locations on each pearl sample in order to assess surface homogeneity. The spectral data (inputs) were smoothed to reduce the noise, fed into ANNs and correlated to the pearl’s quality/grading criteria (outputs). The developed ANNs were successful in predicting pearl type, mollusk growing species, possible luster and color enhancing, donor condition/type, recipient/host color, donor color, pearl luster, pearl color, origin. The results of this study shows that the developed UV-Vis spectroscopy-ANN method could be used as a more objective method of assessing pearl quality (grading) and may become a valuable tool for the pearl grading industry.

The Use of Probabilistic Neural Network and UV Reflectance Spectroscopy as an Objective Cultured Pearl Quality Grading Method

“…Diffuse reflectance UV spectroscopy combined with PNN data modeling, successfully classified and graded 28 different pearls, resulting in more accurate prediction of selected pearl quality parameters (mollusk species, pearl and color donor, luster, and surface complexity) when compared with the previous model [3]. 25 out of 28 mollusk species were correctly classified (90%) and similarly 90% of pearl color was correctly predicted with developed models.…”

“…The UV diffuse reflectance spectrum is a unique property of a pearl and different pearls will generally have different spectra due to differences in nacre composition (Figures 1-3) [3].…”

“…Given the good agreement of the previous study that employed the multilayer perceptron (MLP) artificial neural network (ANN) [3] (one of the most commonly used networks), to correlate diffuse reflectance UV-Visible spectra with pearl quality parameters, we wanted to investigate if reducing the number and type of spectral inputs (to UV only) and using the more specific probabilistic neural network (PNN) for correlation we could further improve model predictability. It is well established that the presence of redundant inputs that are not highly related to the outputs, increases the size of a neural network and may result in the network taking longer to train and may provide misleading results.…”

Lombok Pearl Quality Classification Using a Combination of Feature Extraction and Artificial Neural Networks Based on Shape

“…This damage cannot be seen with the naked eye, making it difficult for cultivators and buyers to distinguish the existing damages on Pearl. This damage can be detected and classified using the help of a computer system and visual tools and methods (Agatonovic-Kustrin & Morton, 2012). This damage can affect the quality and price of the Pearl itself.…”

“…Research on pearls using computer processing systems is still relatively low (Agatonovic-Kustrin & Morton, 2012). This study investigated the capacity of artificial neural networks to classify the quality of 27 different pearls from the UV-Visible spectrum.…”