Near-infrared hyperspectral imaging for identification of aflatoxin contamination on corn kernels

Author(s):  
Feifei Tao ◽  
Haibo Yao ◽  
Zuzana Hruska ◽  
Russell Kincaid ◽  
Kanniah Rajasekaran
2019 ◽  
Vol 62 (5) ◽  
pp. 1065-1074
Author(s):  
Qifang Wu ◽  
Huirong Xu

Abstract. Pistachios are susceptible to aflatoxin contamination because of their rich nutrient content. Hyperspectral imaging (HSI), a new method for collecting spectral and image information, has been successfully employed in contamination research to classify staple agricultural products, such as maize, that are contaminated with aflatoxins. However, only a few studies have been conducted on the nondestructive discrimination among contaminated nuts using HSI for both qualitative and quantitative purposes. Thus, the feasibility of directly detecting aflatoxin B1 (AFB1) in individual pistachio kernels using visible/near-infrared HSI (VNIR HSI) was explored in this study. A total of 300 pistachio kernels were randomly selected to prepare target samples that were artificially contaminated with 5, 10, 20, 30, or 50 ppb (parts per billion) of AFB1. Principal component analysis (PCA) showed an overall separation trend between the control and all contaminated kernels. Accuracies greater than 90.0% were obtained by linear discriminant analysis (LDA) for samples that were artificially contaminated with different concentrations of AFB1 based on spectra at 694 to 988 nm that had been preprocessed with standard normal variate (SNV) and Savitzky-Golay (SG) smoothing. The correlation coefficients of calibration and validation (rc and rv) from stepwise multiple linear regression (SMLR) models were all >0.9100. Moreover, five key wavelengths (708, 771, 892, 915, and 941 nm) closely associated with AFB1 contamination were identified using principal component spectra analysis. Generally, the results indicated that VNIR HSI could be employed for preliminary screening of pistachio kernels that were artificially contaminated with AFB1, even at the 5 ppb level. However, the quantitative prediction of the specific AFB1 concentration needed to be further improved. Keywords: Aflatoxins, Detection analysis, Hyperspectral information, Pistachios, Visible/near-infrared.


Author(s):  
Chih-Cheng Pai ◽  
Yang-Chu Chen ◽  
Keng-Hao Liu ◽  
Yuan-Hsun Tsai ◽  
Po-Chi Hu ◽  
...  

2020 ◽  
Author(s):  
L. Granlund ◽  
M. Keinänen ◽  
T. Tahvanainen

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.


LWT ◽  
2021 ◽  
pp. 111737
Author(s):  
Yujie Wang ◽  
Ying Liu ◽  
Yuyu Chen ◽  
Qingqing Cui ◽  
Luqing Li ◽  
...  

2021 ◽  
Vol 175 ◽  
pp. 111497
Author(s):  
Weijie Lan ◽  
Benoit Jaillais ◽  
Catherine M.G.C. Renard ◽  
Alexandre Leca ◽  
Songchao Chen ◽  
...  

LWT ◽  
2021 ◽  
Vol 143 ◽  
pp. 111092
Author(s):  
Jose Marcelino S. Netto ◽  
Fernanda A. Honorato ◽  
Patrícia M. Azoubel ◽  
Louise E. Kurozawa ◽  
Douglas F. Barbin

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