A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits
Abstract
:1. Introduction
2. Materials and Methods
2.1. Reagents
2.2. Fruit Sampling
2.3. Raman Spectroscopy (RS)
2.4. Chemometric Analysis
3. Results and Discussion
3.1. Characterization of the Raman Spectra of Fruits
3.2. Multivariate Analysis
3.2.1. Principal Component Analysis
3.2.2. Partial Least Square Discriminant Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Assignment 1 | Literature | Fructose | Glucose | Sucrose | Cherimoya | Soursop | Pineapple |
---|---|---|---|---|---|---|---|
δ (CH2) | 1460 [37] | 1473.93 | 1471.74 | 1472.81 | 1464.22 | 1467.44 | 1464.22 |
ρ (CH2) | 1346 [37] | 1356.80 | 1359.73 | 1355.73 | 1355.73 | 1352.50 | 1353.58 |
ν (CC), ν (CO), β (COH) | 1142 [37] | 1133.37 | 1133.37 | 1133.37 | 1131.22 | 1134.44 | 1133.37 |
ν (CC), ν (CO) | 1074 [37] | 1080.73 | 1081.81 | 1080.73 | 1078.58 | 1085.03 | 1085.03 |
CH, COH bending | 924 [38] | 932.49 | 929.27 | 933.57 | 928.20 | 928.20 | 918.53 |
ν (CH) | 867 [38] | 859.45 | 853.00 | 860.52 | 855.15 | 854.08 | 851.93 |
β (CCO) | 596 [39] | 594.12 | 595.20 | 601.64 | 595.12 | ||
β (CCO), β (CCC) | 527 [39] | 536.12 | 537.19 | 536.12 | 535.04 | 535.04 | |
β (CCC) | 465 [39] | 456.63 | 455.55 | 457.70 | 453.40 | 455.55 | 455.55 |
β (CCC),β (CCO),β (OCO) | 424 [38] | 416.88 | 415.81 | 416.88 | 417.95 |
Pre-Processing Method | PC | RMSECV | % Variance Captured Total | Q Residual | Hotelling’s T2 |
---|---|---|---|---|---|
Mean centering | 3 | 254.18 | 76.05 | 23.95 | 76.05 |
First derivative of Savitzky–Golay | 4 | 44.06 | 92.59 | 7.41 | 92.59 |
Second derivative of Savitzky–Golay | 5 | 25.86 | 91.10 | 8.90 | 91.10 |
Third derivative of Savitzky–Golay | 5 | 39.32 | 81.06 | 18.94 | 81.06 |
Pre-Processing Methods | RMSEC | RMSECV | Principal Component Number | Eigenvalue of Cov (X) | % Variance Captured This PC | % Variance Captured Total |
---|---|---|---|---|---|---|
Mean centering | 180.47 | 254.18 | 1 | 1.29 × 107 | 39.29 | 39.29 |
2 | 6.36× 106 | 19.34 | 58.64 | |||
3 | 5.72 × 106 | 17.42 | 76.05 | |||
First derivative of Savitzky–Golay | 31.30 | 44.06 | 1 | 2.17 × 106 | 68.52 | 68.52 |
2 | 3.81 × 105 | 12.02 | 80.54 | |||
3 | 2.75 × 105 | 8.66 | 89.20 | |||
4 | 1.08 × 105 | 3.39 | 92.59 | |||
Secondderivative ofSavitzky–Golay | 19.20 | 25.86 | 1 | 5.47 × 105 | 54.96 | 54.96 |
2 | 1.90 × 105 | 19.06 | 74.01 | |||
3 | 8.61 × 104 | 8.65 | 82.67 | |||
4 | 4.62 × 104 | 4.65 | 87.31 | |||
5 | 3.77 × 104 | 3.79 | 91.10 | |||
Thirdderivative ofSavitzky–Golay | 29.15 | 39.31 | 1 | 5.12 × 105 | 47.57 | 47.57 |
2 | 1.72 × 105 | 15.96 | 63.53 | |||
3 | 8.88 × 104 | 8.24 | 71.77 | |||
4 | 6.25 × 104 | 5.80 | 77.57 | |||
5 | 3.75 × 104 | 3.48 | 81.06 |
Pre-Processing Method | Sweet Fruit | Training Data Set | Prediction Data Set | ||||
---|---|---|---|---|---|---|---|
P 1 (%) | SE 2 (%) | SP 3 (%) | P (%) | SE (%) | SP (%) | ||
Mean centering | Cherimoya | 93.06 | 93.87 | 97.98 | 100.00 | 100.00 | 100.00 |
Soursop | 97.02 | 97.95 | 96.97 | 100.00 | 100.00 | 100.00 | |
Pineapple | 100.00 | 98.00 | 100.00 | 100.00 | 100.00 | 100.00 | |
First derivative of Savitzky–Golay | Cherimoya | 100.00 | 97.43 | 100.00 | 100.00 | 100.00 | 100.00 |
Soursop | 98.68 | 100.00 | 98.66 | 100.00 | 100.00 | 100.00 | |
Pineapple | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | |
Second derivative of Savitzky–Golay | Cherimoya | 96.68 | 54.83 | 98.11 | 100.00 | 12.50 | 100.00 |
Soursop | 84.44 | 92.00 | 83.05 | 80.00 | 100.00 | 75.00 | |
Pineapple | 100.00 | 89.28 | 100.00 | 100.00 | 100.00 | 100.00 | |
Third derivative of Savitzky–Golay | Cherimoya | 98.13 | 71.79 | 98.61 | 100.00 | 20.00 | 100.00 |
Soursop | 87.94 | 97.22 | 86.67 | 80.59 | 69.23 | 83.33 | |
Pineapple | 98.68 | 100.00 | 98.67 | 100.00 | 100.00 | 100.00 |
Pre-Processing Method | Modeled Class | Cherimoya | Soursop | Pineapple |
---|---|---|---|---|
Mean centering | Sensitivity (Cal) | 0.959 | 1.000 | 1.000 |
Specificity (Cal) | 0.929 | 0.949 | 1.000 | |
Sensitivity (CV) | 0.939 | 1.000 | 1.000 | |
Specificity (CV) | 0.909 | 0.949 | 1.000 | |
Class. Err (Cal) | 0.0557617 | 0.0252525 | 0 | |
Class. Err (CV) | 0.0760668 | 0.0252525 | 0 | |
RMSEC | 0.31426 | 0.273395 | 0.217022 | |
RMSECV | 0.333692 | 0.289513 | 0.229203 | |
Bias | 1.66533× 10−16 | 1.66533× 10−16 | 1.11022 × 10−16 | |
CV Bias | 0.00127205 | −0.00184987 | 0.000577824 | |
R2 Cal | 0.554066 | 0.662499 | 0.78946 | |
R2 CV | 0.501205 | 0.622938 | 0.766066 | |
First derivative of Savitzky–Golay | Sensitivity (Cal) | 0.974 | 1.000 | 1.000 |
Specificity (Cal) | 0.972 | 0.987 | 1.000 | |
Sensitivity (CV) | 0.897 | 0.972 | 1.000 | |
Specificity (CV) | 0.958 | 0.987 | 1.000 | |
Class. Err (Cal) | 0.0267094 | 0.00666667 | 0 | |
Class. Err (CV) | 0.0721154 | 0.0205556 | 0 | |
RMSEC | 0.26207 | 0.197014 | 0.198749 | |
RMSECV | 0.311281 | 0.252941 | 0.209272 | |
Bias | −0.00915475 | 0.00408662 | 0.00506812 | |
CV Bias | 0.00775709 | −0.0145767 | 0.00681962 | |
R2 Cal | 0.699008 | 0.822952 | 0.819861 | |
R2 CV | 0.595265 | 0.717618 | 0.800737 | |
Secondderivative ofSavitzky–Golay | Sensitivity (Cal) | 0.548 | 1.000 | 0.964 |
Specificity (Cal) | 0.906 | 0.831 | 1.000 | |
Sensitivity (CV) | 0.548 | 0.960 | 0.929 | |
Specificity (CV) | 0.887 | 0.831 | 1.000 | |
Class. Err (Cal) | 0.272976 | 0.0847458 | 0.0178571 | |
Class. Err (CV) | 0.28241 | 0.104746 | 0.0357143 | |
RMSEC | 0.397685 | 0.343363 | 0.242586 | |
RMSECV | 0.400828 | 0.351006 | 0.244279 | |
Bias | 0.0964188 | −0.131557 | 0.0351384 | |
CV Bias | 0.0958097 | −0.13477 | 0.0389599 | |
R2 Cal | 0.366397 | 0.538207 | 0.740784 | |
R2 CV | 0.356427 | 0.518102 | 0.740114 | |
Thirdderivative ofSavitzky–Golay | Sensitivity (Cal) | 0.821 | 0.944 | 1.000 |
Specificity (Cal) | 0.889 | 0.813 | 0.987 | |
Sensitivity (CV) | 0.821 | 0.917 | 1.000 | |
Specificity (CV) | 0.875 | 0.813 | 0.973 | |
Class. Err (Cal) | 0.145299 | 0.121111 | 0.00666667 | |
Class. Err (CV) | 0.152244 | 0.135 | 0.0133333 | |
RMSEC | 0.348922 | 0.32159 | 0.208805 | |
RMSECV | 0.361874 | 0.336387 | 0.234444 | |
Bias | −0.00584702 | 0.000421702 | 0.00542532 | |
CV Bias | −0.00528552 | −0.00200115 | 0.00728667 | |
R2 Cal | 0.465947 | 0.528059 | 0.801175 | |
R2 CV | 0.425925 | 0.486149 | 0.749947 |
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Balcázar-Zumaeta, C.R.; Maicelo-Quintana, J.L.; Salón-Llanos, G.; Barrena, M.; Muñoz-Astecker, L.D.; Cayo-Colca, I.S.; Torrejón-Valqui, L.; Castro-Alayo, E.M. A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits. Appl. Sci. 2024, 14, 8476. https://doi.org/10.3390/app14188476
Balcázar-Zumaeta CR, Maicelo-Quintana JL, Salón-Llanos G, Barrena M, Muñoz-Astecker LD, Cayo-Colca IS, Torrejón-Valqui L, Castro-Alayo EM. A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits. Applied Sciences. 2024; 14(18):8476. https://doi.org/10.3390/app14188476
Chicago/Turabian StyleBalcázar-Zumaeta, César R., Jorge L. Maicelo-Quintana, Geidy Salón-Llanos, Miguel Barrena, Lucas D. Muñoz-Astecker, Ilse S. Cayo-Colca, Llisela Torrejón-Valqui, and Efraín M. Castro-Alayo. 2024. "A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits" Applied Sciences 14, no. 18: 8476. https://doi.org/10.3390/app14188476
APA StyleBalcázar-Zumaeta, C. R., Maicelo-Quintana, J. L., Salón-Llanos, G., Barrena, M., Muñoz-Astecker, L. D., Cayo-Colca, I. S., Torrejón-Valqui, L., & Castro-Alayo, E. M. (2024). A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits. Applied Sciences, 14(18), 8476. https://doi.org/10.3390/app14188476