Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy
Abstract
1. Introduction
2. Basic Principles of Vibrational Spectroscopy
2.1. Vis–NIR
2.2. NIR
2.3. MIR
2.4. Raman
2.5. HSI
3. Spectral Analysis Framework
3.1. Sample Preparation and Data Acquisition
3.2. Dataset Partitioning
3.3. Preprocessing Methods
3.4. Feature Extraction or Feature Selection
3.5. Model Calibration
3.6. Model Evaluation
4. Application of Spectral Technology in Monitoring Mycotoxins
4.1. Vis–NIR
4.2. NIR
Samples | Toxins | Range | Processing | Modeling | Performance | Ref. |
---|---|---|---|---|---|---|
Wheat | AFB1 | 4.1465–44.6981 ug/kg | Ground | PLSR | R2 = 0.9935 | [105] |
Peanut | AFB1 | 2.1207–290.0161 ug/kg | Ground | SVR | R2 = 0.9761 | [107] |
Maize | AFB1 | 2.6214–63.0195 ug/kg | Ground | PLSR, SVR | PLSR: R2 = 0.9260 SVR: R2 = 0.9707 | [108] |
Wheat | ZEN | 19.6–65.0 ug/kg | Ground | SVR | R2 = 0.99 | [106] |
Wheat | ZEN | 20.2380–483.9630 ug/kg | Ground | PLSR, SVR | PLSR: R2 = 0.9212 SVR: R2 = 0.9434 | [109] |
Maize | AFB1 | Ground | BPNN | R = 0.9951 | [110] | |
Maize | AF, AFB1 | AF: 0.015–73.07 ug/kg AFB1: 0.015–30.17 ug/kg | Unground | PLS, ANN, PCA-DA | AF: R2 = 0.78, Acc = 100% AFB1: R2 = 0.82, Acc = 97.4% | [111] |
Black beans | Fumonisin B1 | 0–10 mg/kg | Unground/ Ground | PLSR | R2 = 0.92 | [112] |
Peanut | AFB1 | 2.21–23.79 g/kg | Unground | LDA, PLSR | R2 = 0.942, Acc = 100% | [113] |
Peanut | AFB1 | 0.63–56.0 ug/kg | Ground | CNN | R2 = 0.99 | [114] |
Rice | AFB1 | 0–41.990 ug/kg | Ground | PLS-DA, PLSR | R = 0.952, Acc = 90% | [115] |
Maize | FB1, FB2 | FB1: 62.5–4000 ug/kg FB2: 62.5–2861 ug/kg | Ground | PLSR, ANN | FB1: R2 = 0.91 FB2: R2 = 0.93 | [116] |
Barley | Enniatin | 5.4–7459.2 ug/kg | Ground | PLS-DA | Sensitivity = 94.2% | [117] |
Peanut | AFB1 | 2.44–223.76 ug/kg | Unground | Naïve Bayes | Acc = 86.96% | [118] |
Maize | FB1, FB2 | 0–217.45 mg/kg | Ground | PLSR, SVR, LPLS-S, PLS-DA, SVM-DA | R2 = 0.91, Acc = 89.3% | [119] |
Maize | AFB1 | 2.6214–63.0195 ug/kg | Ground | CNN | R2 = 0.9955 | [120] |
4.3. MIR
Samples | Toxins | Range | Processing | Modeling | Performance | Ref. |
---|---|---|---|---|---|---|
Wheat | FBH | / | Unground/ Ground | RF, LDA | Kernels: Acc = 93.1% Flour: Acc = 100% | [97] |
Wheat | DON | / | Ground | PLS-DA | Balanced dataset: true positive rate (TPR): 0.81 Imbalanced dataset: true negative rate (TNR): 0.85 | [121] |
Peanut | AF | 3.24–2951.21 ppb | Unground | OPLS-DA, SIMCA, PLSR | R = 0.85, Sensitivity = 94.7% | [123] |
Wheat | OTA | 0.15–54 ug/kg | Unground | PLS-DA, PC-LDA | Acc = 96% | [122] |
4.4. Raman
Samples | Toxins | Range | SERS Substrate | Performance | LOD | Ref. |
---|---|---|---|---|---|---|
Wheat Maize | OTA ZEN | OTA: 0.01–100 ng/mL ZEN: 0.05–500 ng/mL | Reporting probe: Au@Ag core–shell nanoparticles modified 4-MBA and DTNB Capture probe: Gold nanorods (AuNRs) modified complementary DNA (SH-cDNA) | OTA: R2 = 0.986 ZEN: R2 = 0.987 | OTA: 0.018 ng/mL ZEN: 0.054 ng/mL | [134] |
Maize | ZEN | 5–400 μg/kg | AuMBA@AgMBANPs | R2 = 0.9989 | 3 μg/kg | [130] |
Maize | ZEN | 3–200 ng/mL | MSN–Rh6g–AuNPs | R2 = 0.988 | 0.0064 ng/mL | [131] |
Maize | ZEN | 10–1000 μg/kg | Core–shell Au@AgNPs with embedded reporter molecules (4-MBA) | R2 = 0.993 | 3.6 μg/kg | [133] |
Wheat | AFB1 | 0.1–5 ng/mL | Au@Ag core–shell nanoparticle (Au@Ag CSNPs) | R2 = 0.9963 | 0.03 ng/mL | [135] |
Coix seed | AFB1 | 0.01–100 ng/mL | gold magnetic nanoparticles (GMNPs) and Ag NPs | R2 = 0.9948 | 0.0060 ng/mL | [136] |
Wheat | FB1 | 0.01–1 µg/L | Se-WCDs-Au-Janus Ag NPs | R2 = 0.9883 | 0.005 μg/L | [137] |
Peanut | AFB1 | 0.01–100 ng/mL | AuNPs and MNPs | R2 = 0.9742 | 5.81 pg/mL | [132] |
4.5. HSI
Samples | Toxins | Range | Processing | Data Type | Modeling | Performance | Ref. |
---|---|---|---|---|---|---|---|
Wheat | DON | 0.9–57 mg/kg | Ground | Spectral value | PLSR, RF, SVR, CNN | R2 = 0.96 | [141] |
Maize | AFB1 | 0–1206 ug/kg | Ground | Spectral value | LDA, SVM, QDA | Vis–NIR: Acc = 82.6% Fluorescence: Acc = 95.7% SWIR: Acc = 95.7% Raman: Acc = 87% | [142] |
Wheat | DON | 0–135.7 mg/kg | Unground | Spectral value | LDA, Naïve Bayes, KNN, ANN, PLSR | R2 = 0.88, Acc = 76.9% | [138] |
Wheat | DON | <LOD−6.233 mg/kg | Ground | Spectral features, Image features | LDA, PLSR | R = 0.691, Acc = 96.92% | [140] |
Wheat | DON | <LOD − 2.7 mg/kg | Unground | Spectral features | LDA | Acc = 92.5% | [143] |
Wheat | DON Fusarium | / | Unground | Spectral features, Image features | KNN | DON: Acc = 80%, Fusarium: Acc = 85% | [144] |
Wheat | DON | <LOD−507.28 mg/kg | Unground | Spectral feature | PLS-DA, PLS, SVM, LPLS-S, | , R2 = 0.81 | [139] |
Peanut | AFB1, AFB2, AFG2, AF | AFB1: 0.148–84.038 AFB2: 0.011–73.625 AFG2: 0–9.163 AF: 0.159–166.826 | Unground | Spectral feature | PLS-DA, LDA, SIMCA, K-NN PLSR, PCR | AFB1: R2 = 0.8863, Acc = 89.66% AFB2: R2 = 0.7864 AFG2: R2 = 0.6612 AF: R2 = 0.8559, Acc = 79.31% | [145] |
Maize Kernels | DON, FB1, FB2, FB1 + FB2 | DON: <LOD−18.622 FB1: <LOD−37.591 FB2: <LOD−27.066 FB1 + FB2: <LOD − 63.891 | Unground | Spectral feature | RF, ANN, KNN, logistic regression, PLSR | DON: Acc = 98.60% FB1 + FB2: Acc = 84.40% DON+ FB1 + FB2: Acc = 89.8% DON: R = 0.904 FB1: R = 0.868 FB2: R = 0.901 FB1 + FB2: R = 0.901 | [146] |
Oat | DON | <LOD-2706 µg/kg | Unground/ Ground | Spectral feature | RF, ANN, KNN Naïve Bayes, PLSR | Unground: Acc = 77.8% Ground: Acc = 70.8% Unground: R = 0.92 Ground: R = 0.90 | [147] |
Peanut | AFB1 | 0–200 ppb | Unground | Spectral feature Texture feature Color feature | LDA, PLS-DA, SVM | Acc = 94% | [148] |
Maize | AF | LOD−>2000 ppb | Unground | Spectral feature | PLS-DA | 20 ppb: Acc = 89.8% 100 ppb: Acc = 89.3% | [149] |
Maize | AF | LOD−>2000 ppb | Unground | Spectral feature | LDA | 20 ppb: Acc = 86.7% 100 ppb: Acc = 89.6% | |
Maize | AF FM | / | Unground | Spectral feature | PLS-DA, SVM | Vis–NIR: Acc = 89.1% Fluorescence: Acc = 71.7% SWIR: Acc = 95.7% | [150] |
Maize | ZEN | 19.98–102.30 µg/kg | Unground | Spectral feature | BPNN, PLS-DA, SVM | R2 = 0.95, Acc = 93.33% | [151] |
Peanut | AF | / | Unground | Spectral feature | PLS-DA, PCA-LDA, LDA, ISOGA-CNN, CNN, CNN-LSTM, A-CNN-LSTM | Binary: Acc = 93.33% Six: Acc = 100% | [152] |
5. Difference Analysis of Different Vibrational Spectroscopy Techniques
6. Challenges, Trends and Outlook
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Samples | Toxins | Range | Processing | Modeling | Performance | Ref. |
---|---|---|---|---|---|---|
Wheat | DON | 0–49.3 mg/kg | Ground Unground | Random forest (RF) Extra trees (ET) AdaBoost (AB) | Kernels: ET: R2 = 0.77 RF: Acc = 84.2% Flour: ET: R2 = 0.94 AB: Acc = 89% | [101] |
Wheat | Fusarium head blight(FHB) | / | Ground Unground | Linear discriminant Analysis (LDA), RF | Kernels: Acc = 100% Flour: Acc = 100% | [97] |
Maize | Aflatoxin | 0–50 ng/g | Unground | LDA, PLSR | R2 = 0.99, Acc = 100% | [100] |
Wheat | Fusarium and Aspergillus | / | Unground | LDA, PLSR | R2 = 0.89, Acc = 91.7% | [98] |
Peanut | AFB1 | 10–1000 ppb | Unground | PLS-DA | 20 ppb: Acc = 90% 40 ppb: Acc = 94.29% | [99] |
Peanut | AFB1 | 1.37–268.16 ug/kg | Ground | PLS-DA, PLS | R = 0.956, Acc = 91.9% | [102] |
White | AFB1 | 6.57–124.29 ug/kg | Unground | PLSR | >35: R2 = 0.69 <35: R2 = 0.61 | [103] |
Maize | Versicolorin A | / | Ground | K-nearest neighbors (KNN), SVM, XGBoost | SVM: Acc = 90% XGBoost: R2: 0.97 | [104] |
Samples | Toxins | Range | Processing | Modeling | Performance | Ref. |
---|---|---|---|---|---|---|
Maize | AFB1 | 2.6214–63.0195 ug/kg | Ground | SVM | R2 = 0.9715 | [124] |
Wheat | ZEN | 2–63 ug/kg | Ground | CNN | R2 = 0.9837 | [125] |
Wheat | AFB1 | 2.040–92.534 ug/kg | Ground | PLSR | R2 = 0.9927 | [126] |
Peanut | AFB1 | 2.1207–290.0161 ug/kg | Ground | PLSR | R = 0.9558 | [127] |
Maize | ZEN | 6.90–800.20 ug/kg | Ground | PLSR | R = 0.9260 | [128] |
Peanut | AF | 30–400 ppb | Unground | SIMCA | Acc = 80.8% | [129] |
Spectral Technology | Principle | Benefits | Shortcomings | Sensitivity | Sample Requirements |
---|---|---|---|---|---|
Vis–NIR | Color + vibration frequency multiplication/combination frequency | Fast, low cost, sensitive to color changes | Low specificity, spectral overlap | μg/kg-mg/kg | Ground, Unground |
NIR | The vibration of the hydrogen-containing group is multiplied or combined | Fast, good penetration, mature, high online potential | Water effect, model dependence is strong | μg/kg | Ground, Unground |
MIR | Fundamental frequency vibration | Rich molecular structure information, high specificity, molecular fingerprint interval | Shallow penetration, moisture-sensitive | μg/kg | Ground, Unground, Tablet |
Raman | Inelastic scattering vibration | Water compatibility | Weak signal and prone to fluorescence interference | μg/kg | Ground, Unground |
SERS | Raman scattering, surface signal enhancement | Ultra-high sensitivity, High specificity | Strong dependence on the base, challenges in reproducibility/stability, and high cost | ng/kg-pg/kg | Extraction, Base Preparation |
HSI | Spectral spatial imaging | Spatial information distribution, heterogeneity analysis | Large volume of data, complex processing, and expensive instruments | μg/kg-mg/kg | Ground, Unground |
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Deng, J.; Zhao, M.; Jiang, H. Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy. Foods 2025, 14, 2688. https://doi.org/10.3390/foods14152688
Deng J, Zhao M, Jiang H. Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy. Foods. 2025; 14(15):2688. https://doi.org/10.3390/foods14152688
Chicago/Turabian StyleDeng, Jihong, Mingxing Zhao, and Hui Jiang. 2025. "Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy" Foods 14, no. 15: 2688. https://doi.org/10.3390/foods14152688
APA StyleDeng, J., Zhao, M., & Jiang, H. (2025). Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy. Foods, 14(15), 2688. https://doi.org/10.3390/foods14152688