Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Materials
2.2. Grain Selection
2.3. FTIR Spectroscopy
2.4. UV-vis-NIR Reflectance Spectroscopy
2.5. Luminescence Spectroscopy
2.6. Data Analysis
2.6.1. Chemometric Analysis
2.6.2. Model Evaluation
3. Results and Discussion
3.1. MIR Absorption Spectroscopy
3.1.1. FTIR Spectral Trends and Features
3.1.2. PCA of FTIR Absorption Spectra
3.1.3. LDA and SVM Classification
3.2. UV-Vis-NIR Spectroscopy
3.2.1. UV-Vis-NIR Spectral Trends and Features
3.2.2. PCA of UV-vis-NIR Absorption Spectra
3.2.3. LDA and SVM Classification
3.3. Luminescence Spectroscopy
3.3.1. Luminescent Spectral Trends and Features
3.3.2. PCA of Luminescent Spectra
3.3.3. LDA and SVM Classification
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Peak Frequency, cm−1 | Assignment 1 on The Basis of [26,39,40,41,42,43] |
---|---|
893 | stretching vibrations in glyosidic bonds |
983 | v(C-O) |
1003 | v(C-O) |
1156 | vas(COC) |
1241 | v(C-O) |
1314 | δ(HCC), δ(COH) |
1365 | δ(HCC), δ(COH) |
1414 | δ(XCH) |
1453 | δ(HCH) |
1512 | v(C=C), δ(XCH) |
1598 | v(C=C) |
1629 | δ(OHO) |
1730 | v(C=O) |
2850 | vs(CH2) |
2918 | vas(CH2) |
3268 | v(OH) in water and intra- and inter-molecular hydrogen bonding |
Peak Frequency, cm−1 | Assignment 1 on The Basis of [26,39,40,41,42,43] |
---|---|
880 | stretching vibrations in glyosidic bonds |
1027 | v(CO) |
1151 | vas(COC) |
1233 | amide III |
1310 | δ(HCC), δ(COH) |
1378 | δ(HCC), δ(COH) |
1447 | δ(HCH) |
1534 | amide II |
1633 | amide I, δ(OHO) |
1738 | v(C=O) |
2850 | vs(CH2) |
2920 | vas(CH2) |
3056 | v(=C-H) |
3181 | v(NH) |
3266 | v(OH) in water and intra- and inter-molecular hydrogen bonding |
Model | Scores Data | Data Set | Accuracy | Sensitivity | Specificity | Positive Predictive Value | Negative Predictive Value |
---|---|---|---|---|---|---|---|
PCA-LDA | PC1, PC2 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC1, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC2, PC3 | Training Set | 0.60 | 0.65 | 0.54 | 0.59 | 0.61 | |
Prediction Set | 0.73 | 0.62 | 0.85 | 0.80 | 0.69 | ||
Total Set | 0.62 | 0.62 | 0.64 | 0.62 | 0.64 | ||
PC1, PC2, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PCA-SVM | PC1, PC2 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC1, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC2, PC3 | Training Set | 0.83 | 0.77 | 0.88 | 0.87 | 0.79 | |
Prediction Set | 0.92 | 0.85 | 1.00 | 1.00 | 0.87 | ||
Total Set | 0.86 | 0.79 | 0.92 | 0.91 | 0.82 | ||
PC1, PC2, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Model | Score Data | Data Set | Accuracy | Sensitivity | Specificity | Positive Predicted Value | Negative Predicted Value |
---|---|---|---|---|---|---|---|
PCA-LDA | PC1, PC2 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC1, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC2, PC3 | Training Set | 0.54 | 0.43 | 0.64 | 0.55 | 0.53 | |
Prediction Set | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | ||
Total Set | 0.50 | 0.43 | 0.57 | 0.50 | 0.50 | ||
PC1, PC2, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PCA-SVM | PC1, PC2 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC1, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PC2, PC3 | Training Set | 0.96 | 1.00 | 0.93 | 0.93 | 1.00 | |
Prediction Set | 0.93 | 1.00 | 0.86 | 0.88 | 1.00 | ||
Total Set | 0.95 | 1.00 | 0.90 | 0.91 | 1.00 | ||
PC1, PC2, PC3 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Model | Scores Data | Data Set | Accuracy | Sensitivity | Specificity | Positive Predicted Value | Negative Predicted Value |
---|---|---|---|---|---|---|---|
PCA-LDA | PC1, PC2 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
PCA-SVM | PC1, PC2 | Training Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Prediction Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ||
Total Set | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
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Pankin, D.; Povolotckaia, A.; Borisov, E.; Povolotskiy, A.; Borzenko, S.; Gulyaev, A.; Gerasimenko, S.; Dorochov, A.; Khamuev, V.; Moskovskiy, M. Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains. Foods 2023, 12, 3426. https://doi.org/10.3390/foods12183426
Pankin D, Povolotckaia A, Borisov E, Povolotskiy A, Borzenko S, Gulyaev A, Gerasimenko S, Dorochov A, Khamuev V, Moskovskiy M. Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains. Foods. 2023; 12(18):3426. https://doi.org/10.3390/foods12183426
Chicago/Turabian StylePankin, Dmitrii, Anastasia Povolotckaia, Eugene Borisov, Alexey Povolotskiy, Sergey Borzenko, Anatoly Gulyaev, Stanislav Gerasimenko, Alexey Dorochov, Viktor Khamuev, and Maksim Moskovskiy. 2023. "Investigation of Spectroscopic Peculiarities of Ergot-Infected Winter Wheat Grains" Foods 12, no. 18: 3426. https://doi.org/10.3390/foods12183426