Detecting Aflatoxin B1 in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy

Wanqing Yao; Ruanshan Liu; Fengru Zhang; Shuang Li; Xiaoxia Huang; Hongwei Guo; Mengxia Peng; Guohua Zhong

doi:10.3390/molecules27196294

Detecting Aflatoxin B₁ in Peanuts by Fourier Transform Near-Infrared Transmission and Diffuse Reflection Spectroscopy

Molecules. 2022 Sep 23;27(19):6294. doi: 10.3390/molecules27196294.

Authors

Wanqing Yao¹, Ruanshan Liu², Fengru Zhang¹, Shuang Li³, Xiaoxia Huang¹, Hongwei Guo¹, Mengxia Peng¹, Guohua Zhong³

Affiliations

¹ Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Chemistry and Environment, Jiaying University, Meizhou 514015, China.
² School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
³ Key Laboratory of Integrated Pest Management on Crops in South China, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China.

Abstract

Aflatioxin B₁ (AFB₁) has been recognized by the International Agency of Research on Cancer as a group 1 carcinogen in animals and humans. A fast, batch, and real-time control and no chemical pollution method was developed for the discrimination and quantification prediction of AFB₁-infected peanuts by applying Fourier transform near-infrared (FT-NIR) coupled with chemometrics. Initially, the near-infrared transmission (NIRT) and diffuse reflection (NIRR) modules were applied to collect spectra of the samples. The principal component analysis (PCA) method was employed to extract the characteristic wavelength, followed by different preprocessing methods (seven methods) to build an effective linear discriminant analysis (LDA) classification and partial least squares (PLS) quantification models. The results showed that, for both the NIRT or NIRR modules, the LDA classification models satisfactorily distinguished peanuts infected with AFB₁ or from those not infected, with external validation showing a 100% correct identification rate and a 0% misjudgment rate. In addition, combined with the concentration of AFB₁ in peanuts determined by enzyme-linked immunoassay assay, the best partial least squares (PLS) models were established, with a combination of the first derivative and the Norris derivative filter smoothing pretreatment (R_c² = 0.937 and 0.984, RMSECV = 3.92% and 2.22%, RPD = 3.98 and 7.91 for NIRR and NIRT, respectively). The correlation coefficient between the predicted value and the reference value in the external verification was 0.998 and 0.917, respectively. This study highlights that both spectral acquisition modules meet the requirements of online, rapid, and accurate identification of peanut AFB₁ infection in the early stages.

Keywords: FT-NIR; aflatoxin B1; peanut; principal component analysis (PCA); spectral acquisition module.

MeSH terms

Aflatoxin B1*
Arachis*
Carcinogens / analysis
Fourier Analysis
Humans
Least-Squares Analysis
Spectroscopy, Fourier Transform Infrared / methods
Spectroscopy, Near-Infrared / methods

Substances

Carcinogens
Aflatoxin B1

Grants and funding

This study was funded by the College Rural Revitalization Community of South China Agricultural University and Jiaying University, China (No. 320D48); the Science and Technology Program of Guangdong Province, China (No. 2020A0104003, 2021A0304012 and 2020B0201001); Science and Technology Planning Project of Meizhou, China (No. 2021B0201001); the Guangdong Province Rural Science and Technology Specialist Project, China (No. 163-2019-XMZC-0009-02-0065).