Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles
Abstract
:1. Introduction
2. Results
2.1. Descriptive Statistics
2.2. Fuzzy Divisive Hierarchical Associative-Clustering
3. Materials and Methods
3.1. Sample Collection
3.2. ICP-MS Analysis
3.3. Fuzzy Clustering Methods
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Element | Mean | Median | Minimum | Maximum | Range | Std.Dev. |
---|---|---|---|---|---|---|
Li | 11.8 | 8.8 | 0.0 | 75.1 | 75.1 | 12.9 |
Be | 0.6 | 0.0 | 0.0 | 5.9 | 5.9 | 1.0 |
B | 4896.3 | 5015.2 | 1654.2 | 9120.4 | 7466.2 | 1703.1 |
Na | 7352.3 | 5904.8 | 2429.6 | 34053.5 | 31623.9 | 5345.5 |
Mg | 54434.8 | 53676.1 | 37390.4 | 92725.6 | 55335.2 | 10216.3 |
Al | 188.2 | 49.1 | 0.0 | 1857.9 | 1857.9 | 337.8 |
P | 78044.8 | 72164.4 | 9573.5 | 197524.9 | 187951.4 | 37460.6 |
K | 238898.5 | 214348.2 | 123688.4 | 719534.1 | 595845.8 | 101572.9 |
Ca | 39343.1 | 39865.4 | 20474.6 | 62776.9 | 42302.3 | 10256.6 |
Sc | 5.2 | 5.0 | 1.7 | 11.1 | 9.3 | 1.9 |
Mn | 676.8 | 612.1 | 281.6 | 1569.4 | 1287.8 | 297.7 |
Co | 1.2 | 0.2 | 0.0 | 7.6 | 7.6 | 1.9 |
Cu | 81.5 | 29.5 | 0.0 | 1020.0 | 1020.0 | 157.3 |
Zn | 285.7 | 219.6 | 0.0 | 1116.9 | 1116.9 | 247.6 |
Ga | 0.5 | 0.4 | 0.0 | 1.6 | 1.6 | 0.4 |
As | 1.0 | 0.7 | 0.0 | 4.0 | 4.0 | 1.0 |
Rb | 1237.1 | 1228.2 | 310.7 | 3025.5 | 2714.8 | 534.0 |
Sr | 302.7 | 249.5 | 118.6 | 1035.9 | 917.3 | 184.8 |
Pd | 4.1 | 3.6 | 0.0 | 14.4 | 14.4 | 3.5 |
Ag | 0.3 | 0.2 | 0.0 | 1.8 | 1.8 | 0.4 |
In | 0.2 | 0.1 | 0.0 | 1.2 | 1.2 | 0.2 |
Sb | 0.2 | 0.2 | 0.0 | 0.5 | 0.5 | 0.1 |
Cs | 4.2 | 3.8 | 0.7 | 10.2 | 9.5 | 2.1 |
Ba | 84.5 | 76.2 | 20.5 | 220.2 | 199.7 | 40.4 |
Ce | 4.6 | 0.9 | 0.0 | 94.0 | 94.0 | 13.0 |
Au | 0.3 | 0.3 | 0.0 | 1.0 | 1.0 | 0.2 |
Tl | 0.4 | 0.3 | 0.0 | 1.5 | 1.5 | 0.3 |
Pb | 12.4 | 6.7 | 1.6 | 91.4 | 89.8 | 15.2 |
Bi | 1.4 | 1.0 | 0.0 | 4.6 | 4.6 | 1.1 |
U | 0.4 | 0.3 | 0.0 | 2.2 | 2.2 | 0.4 |
Fuzzy Partition Level | Divisive Fuzzy Partition History | Wines | DOMsRange of Wines | Associated Variables (Elements) | DOMs Range of Variable (Elements) |
---|---|---|---|---|---|
0 | A | 1, …, 65 | 1, …, 30 | ||
1 | A1 | M2s, O2s, O4r, M6s, M3s, Mu2s, M5s | 0.9836–0.7428 | K | 0.9990 |
A2 | T6c, T6r, T5r, T2c, Mu4s, T6p, O3r, O6c, M2s, O5s, M2p, Mu3r, Mu6s, T6s, O6c, Mu5s, T5p, M4r, O3c, O3s, Mu3s, M4r, O6s, O2r, Mu2r, O5c, T5c, T2p, T3p, T4c, M5p, T2s, M2r, O6r, T3r, M4p, T2r, T3s, M4s, T4r, T3c, O5r, M2r, T4s, T4p, M3s, M4c, M6r, M5r, M3r, O2c, M3p, M2c, O4s, Mu4r, O4c, M4s, T5s | 0.9998–0.6449 | B, Na, Rb, Mn, Sr, Zn, Al, Cu, Ba, Pb, Li, Sc, Ce, Pd, Cs, Bi, Co, As, Be, Ga, U, Tl, Ag, Au, Sb, In, Ca, Mg, P | 1.0000–0.8069 | |
2 | A21 | O2r, M4r, O6c, T5p, O6r, Mu2r, M2r, M2p, M4s, M2s, M4p, O3r, O2c, M3p, M3s, M4c, M6r, M3r, T5r, M5r, Mu4r, M4s, O4c, T6c, T5s, T6r, O4s, M4r, M2c, M5p | 0.9867–0.5665 | Mg, Ca, P | 0.9090–0.7406 |
A22 | O3s, Mu5s, T6s, Mu6s, Mu5s, T3p, T2r, T4c, O5r, O3c, T3c, T5c, T3r, T2p, T6p, O5s, O6s, T2c, T2s, Mu3r, T4p, T3s, T4r, T4s, Mu4s, M2r, O5c, O6c | 0.9884–0.6115 | Mn, Rb, Sr, Zn, Al, Cu, Ba, Pb, Li, Sc, Ce, Pd, Cs, Bi, Co, As, Be, Ga, U, Tl, Ag, Au, Sb, In, B, Na | 0.9996–0.9817 | |
3 | A211 | O6r, M4s, M3p, M3s, M4p, M4r, M2s, T5p, M4c, Mu4r, M4s, T5s, O4c, O3r, M6r, M2c, T6r | 0.9214–0.3494 | P | 0.7405 |
A212 | M2p, M2r, Mu2r, O2r, T5r, M3r, O6c, T6c, O4s, M5r, M4r, O2c, M5p | 0.8803–0.3904 | Mg, Ca | 0.8604; 0.7837 | |
4 | A2121 | M2p, Mu2r | 0.8726–0.7183 | Mg | 0.8604 |
A2122 | T5r, O6c, M2r, O2r, T6c, O4s, M4r, O2c, M5r, M3r, M5p | 0.7537–0.3608 | Ca | 0.7837 | |
5 | A221 | T4c, T2r, O5r, T5c, T3c, T3p, T2p, T3r, T2s, T4p, T3s, T4r, O3s, T4s, O3c, O6s | 0.9471–0.4844 | Al, Cu, Ba, Pb, Sr, Li, Ce, Sc, Pd, Cs, Bi, As, Co, Be, U, Ga, Ag, Au, Tl, Sb, In, Zn, Mn, Rb | 0.9994–0.9442 |
A222 | T6s, Mu3s, Mu3r, T6p, O5c, Mu6s, O5s, Mu4s, Mu5s, O6c, M2r, T2c | 0.9830–0.4454 | Na, B | 0.9598; 0.9097 | |
6 | A2211 | T5c, T2s, T3p, T2r, T2p | 0.8888–0.4492 | Rb | 0.9422 |
A2212 | O5r, T4c, T4p, T3s, T4r, T3r, O3c O3s, T4s, T3c, O6s | 0.8945–0.2854 | Al, Ba, Pb, Ce, Li, Sc, Pd, Cs, Bi, As, Be, Ag, Co, Au, U, Ga, Tl, In, Sb, Cu, Sr, Zn, Mn | 0.9989–0.5792 | |
7 | A22121 | O5r, T4c, T3r, O3s, O3c, T3c, O6s | 0.7895–0.2543 | Al, Ce, Li, Sc, Pd, Cs, Bi, As, Be, Au, Ag, U, Ga, Tl, Co, Sb, In, Al, Ba, Cu | 0.9981–0.7604 |
A22122 | T4p, T3s, T4r, T4s | 0.6889–0.5368 | Zn Sr Mn | 0.9364–0.4689 | |
8 | A221211 | T4c, T3r, O3s, O3c, O6s | 0.7881–0.2496 | Li, Pd, Cs, Sc, Bi, As, Be, Au, Ag, U, Ga, Tl, Co, Sb, In, Pb, Ce | 0.9965–0.9734 |
A221212 | O5r, T3c | 0.7766; 0.3596 | Al, Ba, Cu | 0.8573–0.7597 | |
9 | A2221 | Mu3s, T6s, Mu6s, O5c, O5s, Mu4s, Mu5s, M2r, T2c | 0.9204–0.4239 | B | 0.9093 |
A2222 | Mu3r, T6p, O6c | 0.7292–0.5125 | Na | 0.9591 |
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Feher, I.; Magdas, D.A.; Voica, C.; Cristea, G.; Sârbu, C. Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles. Molecules 2020, 25, 4955. https://doi.org/10.3390/molecules25214955
Feher I, Magdas DA, Voica C, Cristea G, Sârbu C. Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles. Molecules. 2020; 25(21):4955. https://doi.org/10.3390/molecules25214955
Chicago/Turabian StyleFeher, Ioana, Dana Alina Magdas, Cezara Voica, Gabriela Cristea, and Costel Sârbu. 2020. "Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles" Molecules 25, no. 21: 4955. https://doi.org/10.3390/molecules25214955
APA StyleFeher, I., Magdas, D. A., Voica, C., Cristea, G., & Sârbu, C. (2020). Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles. Molecules, 25(21), 4955. https://doi.org/10.3390/molecules25214955