ArticleAnalytical and bioanalytical chemistry2012
Near-infrared (NIR) hyperspectral imaging and multivariate image analysis to study growth characteristics and differences between species and strains of members of the genus Fusarium.
Article in Analytical and bioanalytical chemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality.Foods (Basel, Switzerland) · 2026Article
- Toward smart and in-situ mycotoxin detection in food via vibrational spectroscopy and machine learning.Food chemistry: X · 2025Review
- Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning.Journal of fungi (Basel, Switzerland) · 2022Article
- Phenolic and Antioxidant Analysis of Olive Leaves Extracts (Foods (Basel, Switzerland) · 2019Article
- Early Detection ofFrontiers in plant science · 2018Article
- The Application of Vibrational Spectroscopy Techniques in the Qualitative Assessment of Material Traded as Ginseng.Molecules (Basel, Switzerland) · 2016Article
- Advancements in IR spectroscopic approaches for the determination of fungal derived contaminations in food crops.Analytical and bioanalytical chemistry · 2015Article
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4 authors.
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Abstract
Near-infrared (NIR) hyperspectral imaging was used to study three strains of each of three Fusarium spp. (Fusarium subglutinans, Fusarium proliferatum and Fusarium verticillioides) inoculated on potato dextrose agar in Petri dishes after either 72 or 96 h of incubation. Multivariate image analysis was used for cleaning the images and for making principal component analysis (PCA) score plots and score images and local partial least squares discriminant analysis (PLS-DA) models. The score images, including all strains, showed how different the strains were from each other. Using classification gradients, it was possible to show the change in mycelium growth over time. Loading line plots for principal component (PC) 1 and PC2 explained variation between the different Fusarium spp. as scattering and chemical differences (protein production), respectively. PLS-DA prediction results (including only the most important strain of each species) showed that it was possible to discriminate between species with F. verticillioides the least correctly predicted (between 16 and 47 % pixels correctly predicted). For F. subglutinans, 78-100 % pixels were correctly predicted depending on the training and test sets used. Similarly, the percentage correctly predicted values of F. proliferatum were 60-80 %. Visualisation of the mycelium radial growth in the PCA score images was made possible due to the use of NIR hyperspectral imaging. This is not possible with bulk spectroscopy in the visible or NIR regions.
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