Evaluation of Aspergillus flavus Growth and Detection of Aflatoxin B 1 Content on Maize Agar Culture Medium Using Vis/NIR Hyperspectral Imaging

Xiaohuan Guo, Beibei Jia, Haicheng Zhang, Xinzhi Ni, Hong Zhuang, Yao Lu and Wei Wang ()
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Xiaohuan Guo: Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Beibei Jia: Key Laboratory of Food Quality and Safety for State Market Regulation, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
Haicheng Zhang: Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Xinzhi Ni: Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
Hong Zhuang: Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
Yao Lu: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Wei Wang: Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 2, 1-13

Abstract: The physiological and biochemical processes of Aspergillus flavus ( A. flavus ) are complex. Monitoring the metabolic evolution of products during the growth of A. flavus is critical to the overall understanding of the fungal and aflatoxin production detection mechanism. The dynamic growth process of A. flavus and the aflatoxin B 1 (AFB 1 ) accumulation in culture media was investigated with a visible/near-infrared hyperspectral imaging (Vis/NIR HSI) system in the range of 400 to 1000 nm. First, the growth of A. flavus and the synthesis pattern of AFB 1 were monitored on maize agar medium (MAM) culture for 120 h with a 24-h time-lapse imaging interval. Second, to classify the A. flavus growth, a principal component analysis (PCA) was employed, and a support vector machine (SVM) model was established with the PC 1 –PC 3 as inputs. The results suggested that the PCA-SVM method could distinguish the A. flavus growth time with a classification accuracy larger than 0.97, 0.91, and 0.92 for calibration, validation, and cross-validation, respectively. Third, regression models to predict the AFB 1 accumulation using hyperspectral images were developed by comparing different pre-processing methods and key wavelengths. The successive projection algorithm (SPA) was adopted to distill the key wavelengths. The experimental results indicated that the standard normal variate transformation (SNV) with the partial least squares regression (PLSR) achieved the optimal regression performance with an R C value of 0.98–0.99 for calibration and R V values of 0.95–0.96 for validation. Finally, a spatial map of the AFB 1 concentration was created using the PLSR model. The spatial regularity of the AFB 1 concentration was comparable to the measurement performed. The study proved the potential of the Vis/NIR HSI to characterize the A. flavus growth and the concentration of AFB 1 on the MAM over time.

Keywords: Aspergillus flavus; AFB 1; Vis/NIR hyperspectral imaging (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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