The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR
Abstract
:1. Introduction
2. Materials and Methods
2.1. Conductance, Concentration and pH
2.2. Color Measurement
2.3. Viscosity Measurement
2.4. Machine Learning
2.5. Statistical Analysis
3. Results and Discussion
3.1. Viscosity Results
3.2. L*a*b* Color Results
3.3. Results of Machine Learning
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Machine Learning Algorithm Type | Name | Hyperparameters |
---|---|---|
LinearRegression | LR | default |
DecisionTreeRegressor | DT | max_depth = 9 |
RandomForestRegressor | RF | n_estimators = 120 |
SVR | SVR | kernel = ’rbf’ |
BayesianRidge | BayesianRidge | n_iter = 300 |
Elastic Net | Elastic Net | max_iter = 1000 |
HuberRegressor | HR | max_iter = 50 |
pH | A ± ΔA | B ± ΔB | R2 |
---|---|---|---|
3 | 0.155 ± 0.016 | 0.648 ± 0.013 | 0.999 |
4 | 0.194 ± 0.031 | 0.578 ± 0.020 | 0.999 |
5 | 0.303 ± 0.070 | 0.444 ± 0.033 | 0.968 |
6 | 0.231 ± 0.062 | 0.461 ± 0.038 | 0.972 |
7 | 0.117 ± 0.020 | 0.554 ± 0.024 | 0.989 |
8 | 0.116 ± 0.026 | 0.542 ± 0.033 | 0.977 |
C | pH | |||||
---|---|---|---|---|---|---|
[%] | 3 | 4 | 5 | 6 | 7 | 8 |
1 | 0.596 ± 0.159 Ga | 0.699 ± 0.147 Fa | 0.831 ± 0.090 Ea | 0.887 ± 0.194 Ea | 0.812 ± 0.132 Ea | 0.665 ± 0.104 Ea |
2 | 0.763 ± 0.840 Ga | 0.858 ± 0.183 Fa | 0.915 ± 0.049 Ea | 1.025 ± 0.498 Ea | 0.626 ± 0.188 Ea | 0.585 ± 0.109 Ea |
3 | 1.512 ± 0.436 Fa | 1.220 ± 0.197 Fab | 1.153 ± 0.080 Eab | 1.004 ± 0.191 Eab | 0.635 ± 0.129 Eb | 0.641 ± 0.037 Eb |
4 | 2.694 ± 0.399 Ea | 2.095 ± 0.284 Ea | 2.682 ± 0.296 Da | 1.442 ± 0.201 Db | 0.733 ± 0.208 Ec | 0.746 ± 0.138 Ec |
5 | 4.044 ± 0.127 Da | 3.401 ± 0.189 Db | 2.292 ± 0.218 Dc | 1.961 ± 0.106 EDcd | 1.862 ± 0.075 Dcd | 1.498 ± 0.071 Dd |
6 | 7.676 ± 0.149 Ca | 5.301 ± 0.530 Cb | 3.656 ± 0.125 Cc | 3.337 ± 0.079 Ccd | 3.250 ± 0.092 Ccd | 2.934 ± 0.207 Cd |
7 | 13.928 ± 0.159 Ba | 9.866 ± 0.766 Bb | 6.047 ± 0.252 Bc | 6.164 ± 0.063 Bc | 5.537 ± 0.161 Bcd | 5.351 ± 0.104 Bd |
8 | 27.758 ± 0.132 Aa | 19.894 ± 0.105 Ab | 11.291 ± 0.191 Ac | 8.681 ± 0.127 Ae | 9.945 ± 0.177 Ad | 8.409 ± 0.317 Ae |
Parameter | pH\Concentration | 1% | 2% | 3% | 4% | 5% | 6% | 7% | 8% |
---|---|---|---|---|---|---|---|---|---|
3 | 31.63 ± 0.05 c | 26.68 ± 0.01 b | 28.12 ± 0.01 b | 28.70 ± 0.02 d | 32.04 ± 0.14 b | 27.76 ± 0.03 a | 27.45 ± 0.06 a | 26.17 ± 0.01 b | |
4 | 33.11 ± 0.03 d | 26.58 ± 0.01 a | 27.83 ± 0.18 ab | 26.11 ± 0.01 a | 27.93 ± 0.68 a | 27.18 ± 0.25 a | 31.56 ± 0.01 d | 28.21 ± 0.13 a | |
L* | 5 | 33.41 ± 0.02 e | 35.67 ± 0.03 f | 34.70 ± 0.02 d | 33.51 ± 0.02 e | 32.48 ± 0.02 b | 33.44 ± 0.45 b | 36.17 ± 0.01 f | 30.15 ± 0.01 d |
6 | 34.13 ± 0.01 f | 34.09 ± 0.02 e | 34.14 ± 0.02 c | 34.54 ± 0.02 f | 34.65 ± 0.03 c | 36.42 ± 0.01 c | 34.68 ± 0.03 e | 34.17 ± 0.07 e | |
7 | 28.53 ± 0.01 b | 28.27 ± 0.01 d | 27.62 ± 0.01 a | 28.02 ± 0.01 c | 27.71 ± 0.01 a | 27.72 ± 0.06 a | 27.95 ± 0.01 c | 27.63 ± 0.08 c | |
8 | 27.67 ± 0.01 a | 26.88 ± 0.01 c | 27.61 ± 0.01 a | 27.62 ± 0.01 b | 27.57 ± 0.01 a | 27.94 ± 0.01 a | 27.83 ± 0.01 b | 28.18 ± 0.01 a | |
3 | 3.15 ± 0.02 a | 1.88 ± 0.01 e | 2.54 ± 0.01 a | 2.06 ± 0.02 e | 1.48 ± 0.06 d | 1.97 ± 0.01 e | 1.82 ± 0.02 b | 1.03 ± 0.01 d | |
4 | 3.22 ± 0.02 a | 0.75 ± 0.02 d | 2.49 ± 0.02 a | 0.90 ± 0.02 d | 1.74 ± 0.08 e | 0.38 ± 0.19 d | 1.37 ± 0.04 b | 1.63 ± 0.02 b | |
a* | 5 | 2.82 ± 0.04 d | 2.38 ± 0.03 a | 0.77 ± 0.03 d | 0.75 ± 0.06 c | 0.73 ± 0.04 c | −0.85 ± 0.07 c | −2.33 ± 0.07 c | 1.73 ± 0.07 b |
6 | 2.95 ± 0.03 e | 2.36 ± 0.05 a | 1.11 ± 0.09 e | 0.54 ± 0.04 b | −1.29 ± 0.08 b | −4.91 ± 0.06 b | −0.98 ± 0.32 d | −0.71 ± 0.23 c | |
7 | 0.05 ± 0.02 b | 0.12 ± 0.02 b | 0.23 ± 0.01 c | −0.04 ± 0.01 a | −0.07 ± 0.02 a | −0.18 ± 0.02 a | −0.19 ± 0.02 a | −0.25 ± 0.02 a | |
8 | 0.05 ± 0.01 c | 0.12 ± 0.01 c | 0.23 ± 0.01 b | −0.04 ± 0.02 a | −0.07 ± 0.01 a | −0.18 ± 0.02 a | −0.19 ± 0.01 a | −0.25 ± 0.01 a | |
3 | 1.41 ± 0.01 c | 0.91 ± 0.01 a | 0.77 ± 0.01 c | 0.50 ± 0.02 d | 0.94 ± 0.03 b | 0.40 ± 0.01 d | 0.26 ± 0.02 c | 0.54 ± 0.02 d | |
4 | 1.49 ± 0.01 e | 1.72 ± 0.01 b | 1.19 ± 0.01 c | 0.92 ± 0.02 c | 0.76 ± 0.04 c | 0.86 ± 0.01 b | 1.15 ± 0.01 b | 0.63 ± 0.02 c | |
5 | 1.63 ± 0.02 d | 1.44 ± 0.02 c | 1.15 ± 0.02 d | 1.18 ± 0.02 e | 0.87 ± 0.02 d | 0.98 ± 0.01 e | 0.87 ± 0.01 d | 0.86 ± 0.01 e | |
b* | 6 | 1.63 ± 0.01 f | 1.44 ± 0.02 d | 1.15 ± 0.01 d | 1.18 ± 0.01 f | 0.87 ± 0.02 b | 0.98 ± 0.01 f | 0.87 ± 0.01 b | 0.86 ± 0.01 f |
7 | 1.07 ± 0.01 a | 0.88 ± 0.02 a | 0.48 ± 0.01 b | 0.28 ± 0.02 b | 0.08 ± 0.01 a | 0.15 ± 0.01 c | 0.02 ± 0.01 a | 0.18 ± 0.01 b | |
8 | 1.18 ± 0.01 b | 0.93 ± 0.01 a | 0.37 ± 0.01 a | 0.10 ± 0.01 a | 0.10 ± 0.02 a | −0.02 ± 0.01 a | 0.04 ± 0.01 a | 0.02 ± 0.01 a |
Machine Learning Algorithm Type | MSE | RMSE | MAE | Coefficient of Determination (R2) | Pearson Correlation (r) |
---|---|---|---|---|---|
LinearRegression | 8.069 | 2.841 | 1.656 | 0.771 | 0.893 |
DecisionTreeRegressor | 0.012 | 0.108 | 0.013 | 0.999 | 0.999 |
RandomForestRegressor | 0.087 | 0.294 | 0.091 | 0.998 | 0.999 |
SVR | 10.522 | 3.244 | 1.035 | 0.702 | 0.914 |
BayesianRidge | 8.122 | 2.850 | 1.648 | 0.770 | 0.892 |
Elastic Net | 20.432 | 4.520 | 2.319 | 0.420 | 0.782 |
HuberRegressor | 11.885 | 3.447 | 1.536 | 0.663 | 0.875 |
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Siejak, P.; Przybył, K.; Masewicz, Ł.; Walkowiak, K.; Rezler, R.; Baranowska, H.M. The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR. Sustainability 2024, 16, 5877. https://doi.org/10.3390/su16145877
Siejak P, Przybył K, Masewicz Ł, Walkowiak K, Rezler R, Baranowska HM. The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR. Sustainability. 2024; 16(14):5877. https://doi.org/10.3390/su16145877
Chicago/Turabian StyleSiejak, Przemysław, Krzysztof Przybył, Łukasz Masewicz, Katarzyna Walkowiak, Ryszard Rezler, and Hanna Maria Baranowska. 2024. "The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR" Sustainability 16, no. 14: 5877. https://doi.org/10.3390/su16145877