A new approach to the teaching and use of the refractometer

Sturman, Darko B.

doi:10.15506/jog.2010.32.1-4.74

Cited by 3 publications

(3 citation statements)

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“…Unfortunately, these tests are sometimes limited in application or with the reliability of their results. The RI requires three operative conditions: the homogeneity of the sample, a polished surface (better if flat), and the use of the refractometer contact liquid [57]. The first condition is difficult to apply to rocks, which are a mix of phases.…”

Section: Classical Gemological and Mineralogical Propertiesmentioning

confidence: 99%

The Importance of Multidisciplinary Analytical Strategies to Solve Identification and Characterization Challenges in Gemology: The Example of the “Green Stones”

Musa

2022

Applied Sciences

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The present review aims to discuss the importance of a multidisciplinary approach in cultural heritage and archaeometry investigations. The analytical methods used to identify and characterize “Green Stones” are discussed as an example. In the present paper, the term Green Stones is applied but not limited to jade materials, which have considerable importance in cultural heritage studies. In fact, archaeological samples made in Green Stones have been discovered worldwide, with many dating back to the Neolithic Age. Moreover, these materials represent an interesting analytical challenge, starting with their nomenclature and, in most cases, the nature of their polycrystalline samples and their heterogeneity. Indeed, after a brief introduction about the advantages of the non-destructive analytical techniques commonly used for gemstones and cultural heritage samples analyses, the limits of the same have been discussed on the basis of Green Stones applicability. Finally, a multidisciplinary methodology for Green Stones identification and full characterization, which considers materials’ heterogeneity and information, has been proposed and based on different references.

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Section: Classical Gemological and Mineralogical Propertiesmentioning

confidence: 99%

The Importance of Multidisciplinary Analytical Strategies to Solve Identification and Characterization Challenges in Gemology: The Example of the “Green Stones”

Musa

2022

Applied Sciences

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“…This is a difficult process, as many gems share colour and characteristics, as illustrated in Figure 1, which displays a sample of 500 gems distributed among 87 different categories. Identification and classification are often accompanied by the use of gemmological tools, which include refractometers [4], polariscopes and conoscopes [5], handheld spectroscopes [6], dichroscopes [7] and ultraviolet light [8] to probe the optical properties of gemstones. Measuring physical properties such as specific gravity [9] (also known as relative density) provides additional information related to the identity of a gemstone.…”

Section: Introductionmentioning

confidence: 99%

“…With the emergence of new synthetic gemstones and treatment techniques, increasingly complex instruments with powerful spectroscopic, fluorescent or chemical analysing abilities have been introduced into gemmological laboratories [2]. Such instruments include infrared spectrometers [10], Raman and luminescence spectrometers [11][12][13], ultraviolet-visible spectrometers [14], cathodoluminescence [15], energy-dispersive X-ray fluorescence spectrometers [4,16], laser ablation-inductively coupled plasma-mass spectrometers [17] and fluorescence spectrometers [18]. Yet, the identification is still difficult and time consuming, and not all laboratories have access to these sophisticated instruments; thus, the identification through automatic techniques based solely on images is attractive.…”

Section: Introductionmentioning

confidence: 99%

Automatic Gemstone Classification Using Computer Vision

Chow

Reyes-Aldasoro

2021

Minerals

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This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas.

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