Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis

By P. Williams, Paul Geladi, T. J. Britz, Marena Manley. 2012

143 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Food Science

View Paper: https://www.semanticscholar.org/paper/812b2f9079e16e3b60e4283e3fac2a3de2a5b025

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Da‐Wen Sun
2. Hongbin Pu
3. Zhiwei Zhu
4. Hui Huang
5. Li Liu
6. Lu Wang
7. Wei Wang
8. Michael O. Ngadi

In Biophysics

1. Da‐Wen Sun
2. Hongbin Pu
3. Wei Wang
4. Zhiwei Zhu
5. Xinzhi Ni
6. Lu Wang
7. Anne Humeau-Heurtier

In Food Science

1. Hongbin Pu
2. Zhiwei Zhu
3. Lu Wang
4. Da‐Wen Sun

Citations in the corpus

• Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging (2019, background): “…wavelengths were 1197 nm and 1394 nm. Absorption at the smaller of these wavelengths is attributed to ergosterol, while the larger wavelength (1394 nm) is close to 1405 nm, which Williams et al. (2012) found important in hyperspectral image analysis of corn inoculated with Fusarium verticillioides.”
• Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, background): “PCA and PLSR are the mostly used modeling method.”
• Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging (2014, methodology): “Hyperspectral imaging has already been proven successful in the evaluation of corn moisture and oil content (Shahin & Symons, 2011; Williams, Manley, Fox, & Geladi, 2010; Williams et al., 2012).”
• Utilisation of visible/near-infrared hyperspectral images to classify aflatoxin B1 contaminated maize kernels (2018, background): “…investigations involving VNIR hyperspectral imaging or short-wave NIR hyperspectral imaging have shown the feasibility of aflatoxin detection and fungal identification studies on maize (Pearson & Wicklow, 2006; Wang, Heitschmidt et al., 2015; Wang, Ni et al., 2015; Williams et al., 2012).”
• Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, background): “Recently, Williams et al. (2012) performed nondestructive HSI analysis of maize kernels infected by fungi and concluded that early detection of fungal contamination and activity was possible.”