The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese Cyperus esculentus L.
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation and Near-Infrared Spectrum Collection
2.2. The Analyses of Crude Oil, Crude Protein, and Total Starch
2.3. Algorithms
2.4. Software and Datasets
2.5. Evaluation Metrics
3. Results
3.1. Sample Composition Content
3.2. Near-Infrared Spectroscopy and Preprocessing
3.3. Full-Spectrum Model
3.4. Variable Selection Algorithm Model
3.4.1. Variable Selection Model for CO
3.4.2. Variable Selection Model for CP
3.4.3. Variable Selection Model of TS
3.5. Comparison of Variable Selection and Full-Spectrum Modeling
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Indicators | Datasets | Numbers | Max Value | Min Value | Mean Value |
---|---|---|---|---|---|
CO | Calibration set | 50 | 26.83 | 8.45 | 17.95 |
Validation set | 25 | 26.51 | 12.75 | 18.40 | |
CP | Calibration set | 50 | 12.13 | 4.31 | 8.90 |
Validation set | 25 | 11.57 | 4.47 | 8.79 | |
TS | Calibration set | 50 | 40.40 | 20.85 | 29.82 |
Validation set | 25 | 35.90 | 21.33 | 28.60 |
Preprocessing Methods | Indicators | nLV | Rcv2 | RMSECV | Rpre2 | RMSEP |
---|---|---|---|---|---|---|
Raw spectra | CO | 10 | 0.8025 | 1.6732 | 0.8124 | 1.5696 |
CP | 10 | 0.8962 | 0.6843 | 0.3276 | 1.5950 | |
TS | 10 | 0.6408 | 2.5989 | 0.8415 | 1.6625 | |
S–G smoothing | CO | 7 | 0.8160 | 1.6148 | 0.7727 | 1.7279 |
CP | 10 | 0.8987 | 0.6760 | 0.5081 | 1.3642 | |
TS | 10 | 0.6950 | 2.3949 | 0.9179 | 1.1969 | |
SNV | CO | 10 | 0.7826 | 1.7553 | 0.8366 | 1.4649 |
CP | 7 | 0.7449 | 1.0726 | 0.4243 | 1.4758 | |
TS | 3 | 0.6873 | 2.4250 | 0.4951 | 2.9675 | |
MSC | CO | 10 | 0.8141 | 1.6230 | 0.8062 | 1.5953 |
CP | 10 | 0.8870 | 0.7137 | 0.3988 | 1.5082 | |
TS | 10 | 0.7167 | 2.3082 | 0.8132 | 1.8048 |
Preprocessing Methods | Variable Selection Algorithms | nLV | Rcv2 | RMSECV | Rpre2 | RMSEP |
---|---|---|---|---|---|---|
Raw spectra | MWPLS | 5 | 0.7716 | 1.7991 | 0.6275 | 2.2117 |
iPLS | 3 | 0.6885 | 2.1013 | 0.5088 | 2.5399 | |
UVE-SPA | 9 | 0.8700 | 1.3575 | 0.8677 | 1.3182 | |
ICO | 10 | 0.9383 | 0.9355 | 0.8682 | 1.3157 | |
S–G smoothing | MWPLS | 5 | 0.7747 | 1.7870 | 0.6220 | 2.2279 |
iPLS | 5 | 0.7160 | 2.0062 | 0.4534 | 2.6792 | |
UVE-SPA | 10 | 0.8718 | 1.3478 | 0.8946 | 1.1764 | |
ICO | 10 | 0.9321 | 0.9813 | 0.8852 | 1.2279 | |
SNV | MWPLS | 4 | 0.7778 | 1.7746 | 0.6826 | 2.0416 |
iPLS | 3 | 0.6466 | 2.2380 | 0.4223 | 2.7544 | |
UVE-SPA | 10 | 0.8716 | 1.3491 | 0.8674 | 1.3194 | |
ICO | 10 | 0.9540 | 0.8077 | 0.8177 | 1.5471 | |
MSC | MWPLS | 4 | 0.8364 | 1.5227 | 0.7174 | 1.9265 |
iPLS | 5 | 0.8066 | 1.6555 | 0.6765 | 2.0612 | |
UVE-SPA | 10 | 0.8938 | 1.2268 | 0.8218 | 1.5297 | |
ICO | 10 | 0.9594 | 0.7589 | 0.8473 | 1.4163 |
Pretreatment Methods | Variable Selection Techniques | nLV | Rcv2 | RMSECV | Rpre2 | RMSEP |
---|---|---|---|---|---|---|
Raw spectra | MWPLS | 3 | 0.7313 | 1.1008 | 0.1980 | 1.7419 |
iPLS | 4 | 0.7058 | 1.1518 | 0.0365 | 1.9092 | |
UVE-SPA | 10 | 0.9313 | 0.5566 | 0.7454 | 0.9815 | |
ICO | 10 | 0.9607 | 0.4208 | 0.6575 | 1.1383 | |
S–G smoothing | MWPLS | 3 | 0.7341 | 1.0951 | 0.2228 | 1.7148 |
iPLS | 3 | 0.7112 | 1.1412 | 0.1885 | 1.7522 | |
UVE-SPA | 9 | 0.9026 | 0.6627 | 0.8525 | 0.7470 | |
ICO | 10 | 0.9532 | 0.4594 | 0.6775 | 1.1045 | |
SNV | MWPLS | 5 | 0.6796 | 1.2020 | 0.4701 | 1.4159 |
iPLS | 2 | 0.7059 | 1.1517 | 0.3759 | 1.5366 | |
UVE-SPA | 9 | 0.9260 | 0.5776 | 0.7418 | 0.9884 | |
ICO | 10 | 0.9472 | 0.4880 | 0.7442 | 0.9838 | |
MSC | MWPLS | 4 | 0.7517 | 1.0582 | 0.7800 | 0.9124 |
iPLS | 5 | 0.7803 | 0.9954 | 0.5893 | 1.2465 | |
UVE-SPA | 10 | 0.9446 | 0.5000 | 0.7432 | 0.9857 | |
ICO | 10 | 0.9487 | 0.4810 | 0.7336 | 1.0040 |
Pretreatment Methods | Variable Selection Techniques | nLV | Rcv2 | RMSECV | Rpre2 | RMSEP |
---|---|---|---|---|---|---|
Raw spectra | MWPLS | 5 | 0.6927 | 2.4039 | 0.7977 | 1.8784 |
iPLS | 3 | 0.6561 | 2.5431 | 0.6473 | 2.4801 | |
UVE-SPA | 10 | 0.7901 | 1.9870 | 0.8906 | 1.3813 | |
ICO | 7 | 0.9523 | 0.9470 | 0.7139 | 2.2340 | |
S–G smoothing | MWPLS | 5 | 0.6953 | 2.3939 | 0.8126 | 1.8079 |
iPLS | 3 | 0.6600 | 2.5285 | 0.6479 | 2.4783 | |
UVE-SPA | 10 | 0.7972 | 1.9528 | 0.8621 | 1.5508 | |
ICO | 7 | 0.9513 | 0.9566 | 0.7345 | 2.1519 | |
SNV | MWPLS | 4 | 0.7902 | 1.9864 | 0.6922 | 2.3168 |
iPLS | 4 | 0.6161 | 2.6869 | 0.6398 | 2.5064 | |
UVE-SPA | 7 | 0.9079 | 1.3160 | 0.8616 | 1.5535 | |
ICO | 7 | 0.9648 | 0.8134 | 0.7553 | 2.0661 | |
MSC | MWPLS | 4 | 0.7675 | 2.0909 | 0.7488 | 2.0932 |
iPLS | 4 | 0.7371 | 2.2233 | 0.7426 | 2.1186 | |
UVE-SPA | 7 | 0.8973 | 1.3899 | 0.8778 | 1.4601 | |
ICO | 7 | 0.9748 | 0.6890 | 0.7858 | 1.9330 |
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Jiao, X.; Guo, D.; Zhang, X.; Su, Y.; Ma, R.; Chen, L.; Tian, K.; Su, J.; Sahati, T.; Aierkenjiang, X.; et al. The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese Cyperus esculentus L. Foods 2025, 14, 366. https://doi.org/10.3390/foods14030366
Jiao X, Guo D, Zhang X, Su Y, Ma R, Chen L, Tian K, Su J, Sahati T, Aierkenjiang X, et al. The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese Cyperus esculentus L. Foods. 2025; 14(3):366. https://doi.org/10.3390/foods14030366
Chicago/Turabian StyleJiao, Xiaobo, Dongliang Guo, Xinjun Zhang, Yunpeng Su, Rong Ma, Lewen Chen, Kun Tian, Jingyu Su, Tangnuer Sahati, Xiahenazi Aierkenjiang, and et al. 2025. "The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese Cyperus esculentus L." Foods 14, no. 3: 366. https://doi.org/10.3390/foods14030366
APA StyleJiao, X., Guo, D., Zhang, X., Su, Y., Ma, R., Chen, L., Tian, K., Su, J., Sahati, T., Aierkenjiang, X., Xia, J., & Xie, L. (2025). The Application of Near-Infrared Spectroscopy Combined with Chemometrics in the Determination of the Nutrient Composition in Chinese Cyperus esculentus L. Foods, 14(3), 366. https://doi.org/10.3390/foods14030366