Author
Listed:
- Xiaohuan Guo
(Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
These authors equally contributed to this work.)
- Beibei Jia
(Key Laboratory of Food Quality and Safety for State Market Regulation, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
These authors equally contributed to this work.)
- 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)
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.
Suggested Citation
Xiaohuan Guo & Beibei Jia & Haicheng Zhang & Xinzhi Ni & Hong Zhuang & Yao Lu & Wei Wang, 2023.
"Evaluation of Aspergillus flavus Growth and Detection of Aflatoxin B 1 Content on Maize Agar Culture Medium Using Vis/NIR Hyperspectral Imaging,"
Agriculture, MDPI, vol. 13(2), pages 1-13, January.
Handle:
RePEc:gam:jagris:v:13:y:2023:i:2:p:237-:d:1040931
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:237-:d:1040931. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.