Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage
Aflatoxin B<sub>1</sub> (AFB<sub>1</sub>) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB<sub>1</sub> using color-sensitive arrays (CSAs). Twenty self-developed CSA...
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MDPI AG
2025-07-01
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| author | Daqian Wan Haiqing Tian Lina Guo Kai Zhao Yang Yu Xinglu Zheng Haijun Li Jianying Sun |
| author_facet | Daqian Wan Haiqing Tian Lina Guo Kai Zhao Yang Yu Xinglu Zheng Haijun Li Jianying Sun |
| author_sort | Daqian Wan |
| collection | DOAJ |
| description | Aflatoxin B<sub>1</sub> (AFB<sub>1</sub>) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB<sub>1</sub> using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB<sub>1</sub>-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.87), root mean square error (<i>RMSEP</i> = 0.057), and relative prediction deviation (<i>RPD</i> = 2.773). This method provides an efficient solution for silage AFB<sub>1</sub> monitoring. |
| format | Article |
| id | doaj-art-c89eab60afe448a8958e10b6484c039c |
| institution | Matheson Library |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-c89eab60afe448a8958e10b6484c039c2025-07-25T13:09:20ZengMDPI AGAgriculture2077-04722025-07-011514150710.3390/agriculture15141507Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn SilageDaqian Wan0Haiqing Tian1Lina Guo2Kai Zhao3Yang Yu4Xinglu Zheng5Haijun Li6Jianying Sun7College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaAflatoxin B<sub>1</sub> (AFB<sub>1</sub>) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB<sub>1</sub> using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB<sub>1</sub>-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.87), root mean square error (<i>RMSEP</i> = 0.057), and relative prediction deviation (<i>RPD</i> = 2.773). This method provides an efficient solution for silage AFB<sub>1</sub> monitoring.https://www.mdpi.com/2077-0472/15/14/1507AFB<sub>1</sub>maize silagecolorimetric sensor arrayportable spectrometermachine learning |
| spellingShingle | Daqian Wan Haiqing Tian Lina Guo Kai Zhao Yang Yu Xinglu Zheng Haijun Li Jianying Sun Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage Agriculture AFB<sub>1</sub> maize silage colorimetric sensor array portable spectrometer machine learning |
| title | Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage |
| title_full | Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage |
| title_fullStr | Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage |
| title_full_unstemmed | Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage |
| title_short | Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage |
| title_sort | color sensitive sensor array combined with machine learning for non destructive detection of afb sub 1 sub in corn silage |
| topic | AFB<sub>1</sub> maize silage colorimetric sensor array portable spectrometer machine learning |
| url | https://www.mdpi.com/2077-0472/15/14/1507 |
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