As of 2022, what is the currently accepted verifiable scientific test of a genuine diamond?
An investigation of the provenance of Diamond using multivariate statistics
The premise that the LIBS spectra of every material include information about the origin, composition, and history of the substance is used in multivariate data analysis. Two different multivariate modelling approaches were used to simulate LIBS spectra. MATLAB was the programming language that was used for the data analysis that was done in the Quantagenetics® method (Mathworks, Natick, MA, USA). Using a three-step method, the spectra from single shots were pre-processed to reduce the variance from shot to shot as much as possible: First, normalize each spectrum to its mean, then convert to log10 form. First, set any intensities that are less than 1.0 to 1.0. The first step prevents the log10 operation from being performed on negative integers and produces a minimum normalized intensity of zero. In order to establish spectral clusters, an unsupervised cluster analysis was performed on all 3161 spectra included inside the model. These spectral clusters correlated to the known geographic and laboratory groupings of diamonds. Using a procedure known as leave-one-out, the probability distance function for each cluster was used as the tool to categorize the spectra. During this process, each spectrum was successively excluded from the calibration, and the results were used to verify the model. The probability distance functions were computed by first calculating the Euclidian distance between each sample and the cluster mean in n-dimensional space, where n is the number of channels in the LIBS spectrum. This distance was used in the subsequent calculation of the probability distance functions. The square root of the sum of the squares of the residuals for each channel is the formula for calculating the distance between a sample and the mean of the cluster. After that, the distribution of distances was used to generate the probability function, which was then used to represent the variance in spectra relative to the mean.
Calculating the Euclidian distance between an unknown spectrum and the means of each cluster allows the Quantagenetics® method to determine the probability that a spectrum from an unknown sample belongs to one of several clusters of spectra. This is done by comparing the unknown spectrum to each of the cluster means. Comparison of the distance to the probability distance function for the cluster results in the calculation of the spectrum's likelihood of belonging to the cluster in question. The cluster that had the greatest likelihood of being the source of the unknown was chosen as the one to be used. For a model to be reliable, it must be possible to forecast both the samples that originate from a certain location and those that do not originate from that location. As a result, success rates are determined by computing the proportion of genuine, positive and negative results relative to the total number of spectra. To get an understanding of the spectral characteristics that are representative of important connections within the dataset, principal component analysis (PCA) was used. The spectral properties that are substantially associated in each main component may be identified via the use of PCA loading charts (PC).
Partial least squares regression (PLSR) models
The second strategy is to train and evaluate a decision tree using a succession of partial least squares regression (PLSR) models. This was done in order to improve the accuracy of the tree's predictions. The principal component factor analysis (PLSR) is a multivariate approach that may be used to estimate the value of an independent variable based on a data set obtained via spectroscopy. After averaging the spectra of each specimen across all groups, a total of 320 spectra was obtained from the 29–30 specimens that were included in each of the 11 geographical groups (the Brazilian carbonado diamonds were not included in this model). The PLSR decision tree is made up of a number of binary models, each of which makes a prediction as to whether a spectrum belongs to the group being modelled or to the group consisting of all other samples. For every model, the value of the independent variable is set to 1 for spectra that belong to the group that is being modelled, but it is set to 0 for spectra that belong to any other group. The models were calibrated with the help of the vast majority of the spectra (83 percent), and validated with the help of the remaining 17 percent. PLSR will determine the value of the independent variable for each calibration spectrum before continuing on with the calibration process. Spectra that belong to the group that is being modelled should have calculated values that are relatively close to one, while spectra that belong to the group that contains all other spectra should have calculated values that are relatively close to zero. The Value of Apparent Distinction (VAD) is the independent variable that is used to differentiate between the two groups. Spectra that has a calculated value for an independent variable that is either greater than or equal to the VAD are placed in the group that is being modelled. Spectra that have calculated values for an independent variable that are lower than the VAD are placed in the group that contains all of the other possible spectra. The percentage of spectra in the validation set that were assigned the right classification is what is used to determine the success rate.
The first group to be modelled is the one that has a PCA score plot that reveals that its composition is the most differentiated from the other groups. After the calibration and validation processes have been completed without issue, all of the spectra belonging to that group are deleted from the succeeding models. Until all of the groups have been modelled, the process of establishing the next group, calibrating and verifying a model that classifies spectra, as belonging to that group or to the group of all other spectra, and the removal of spectra of that group from succeeding models will continue. The percentage of properly labelled spectra is used to determine success rates for each model individually as well as for the decision tree as a whole.
The "Fog Test" makes it simple to confirm if a diamond is an authentic one
Breathing on the stone while exhaling in the same manner as if you were attempting to clear your glasses or defog a window is a simple and fast way to evaluate whether or not the stone in question is in fact a genuine diamond. A stone is not a diamond if it has a cloudy appearance on its surface. Because they do not hold heat well, diamonds will not fog even when they are exposed to the warm air produced by your breath.
The "Transparency Test" may determine a diamond's brilliance
The grey and white brilliance that may be seen inside a diamond is referred to as the diamond's "brilliance." In the meanwhile, the term "fire" refers to the rainbow light that is reflected off of a diamond. If you gaze into your diamond and find a vivid glitter within the stone, it's likely a fake and not a genuine diamond. This is a crucial difference that helps differentiate a true diamond from a fake diamond.
In a similar fashion, if you have a diamond that is not set, you should lay it on top of a newspaper, directly on top of a line of text. The brightness of a real diamond should dazzle enough to prevent one from reading the print on the stone's surface through the stone itself. However, the text that is engraved under other stones, such as cubic zirconia, will be viewable through the gem itself.
Sandpaper test to check hardness of Diamond
Sandpaper must be rubbed on the diamond to polish it. Since diamonds are one of naturally occurring materials with the highest level of hardness, a stone that can be damaged by sandpaper is not likely to be a genuine diamond.
Thermal test for Diamond
After heating the diamond over a lighter for a half of a minute, place it straightaway into a glass of cold water. A genuine diamond will not be impacted by sudden changes in temperature; instead, it will continue to sink to the bottom of the glass while it is held there. However, fake diamonds will shatter as soon as they are touched.
- ↑ Antunes, Ana Rita; Buga, Cláudia Buga; Costa, Daniel Coutinho; Grilo, José; Braga, Ana Cristina; Costa, Lino A. (2021-09-13). "A Multivariate Analysis Approach to Diamonds' Pricing Using Dummy Variables in SPSS". Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part IV. Cagliari, Italy: Springer-Verlag: 609–623. doi:10.1007/978-3-030-86973-1_43. ISBN 978-3-030-86972-4. no-break space character in
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- ↑ MohitMhapuskar (2019-03-23). "Prediction of Diamond Prices Using Multivariate Regression". Cite journal requires
- ↑ Hecker, Christoph; Dilles, John H.; van der Meijde, Mark; van der Meer, Freek D. (2012-03). "Thermal infrared spectroscopy and partial least squares regression to determine mineral modes of granitoid rocks: TIR AND PLSR TO DETERMINE MINERAL MODES". Geochemistry, Geophysics, Geosystems. 13 (3): n/a–n/a. doi:10.1029/2011GC004004. Check date values in:
- ↑ 4.0 4.1 Hecker, Christoph; Dilles, John H.; van der Meijde, Mark; van der Meer, Freek D. (2012-03). "Thermal infrared spectroscopy and partial least squares regression to determine mineral modes of granitoid rocks: TIR AND PLSR TO DETERMINE MINERAL MODES". Geochemistry, Geophysics, Geosystems. 13 (3): n/a–n/a. doi:10.1029/2011GC004004. Check date values in:
- ↑ "How to Spot a Fake Diamond: What These 13 Tests Really Mean! - IGS". International Gem Society. Retrieved 2022-10-17.
- ↑ "Sandpaper Test for Diamond Authenticity | Adiamor". Adiamor Blog. 2010-10-26. Retrieved 2022-10-17.
- ↑ Gemologist, Corinne Davis, Graduate (2021-10-28). "The Diamond Tester: What Passes and What Doesn't". Do Amore. Retrieved 2022-10-17.