Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB<sub>1</sub> in Corn Silage

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Daqian Wan, Haiqing Tian, Lina Guo, Kai Zhao, Yang Yu, Xinglu Zheng, Haijun Li, Jianying Sun
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Agriculture
Subjects:
AFB<sub>1</sub>
maize silage
colorimetric sensor array
portable spectrometer
machine learning
Online Access:https://www.mdpi.com/2077-0472/15/14/1507
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2077-0472

Similar Items