Reference health
Application of Multispectral Imaging Combined with Machine Learning for Rapid and Non-Destructive Detection of Zearalenone (Zen) in Maize
31/31 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 31 checked references that resolve
resolves10.1080/10408398.2019.1658570
Worldwide contamination of food-crops with mycotoxins: Validity of the widely cited ‘FAO estimate’ of 25%
resolves10.1016/S0041-0101(00)00259-2
Structure-activity relationships for human estrogenic activity in zearalenone mycotoxins
resolves10.1021/acs.jafc.6b05614
Fluorescence Polarization Immunoassay Based on a New Monoclonal Antibody for the Detection of the Zearalenone Class of Mycotoxins in Maize
resolves10.1039/C8AY01307A
A competitive immunoassay for zearalenone with integrated poly(dimethysiloxane) based microarray assay
resolves10.1016/j.foodchem.2017.03.100
Highly sensitive fluorescence sensing of zearalenone using a novel aptasensor based on upconverting nanoparticles
resolves10.1016/j.bios.2018.08.029
Progress on nanostructured electrochemical sensors and their recognition elements for detection of mycotoxins: A review
resolves10.1016/j.foodcont.2012.09.006
Development of anti-zearalenone monoclonal antibody and detection of zearalenone in corn products from China by ELISA
resolves10.1016/j.foodchem.2019.02.020
Quantitative assessment of zearalenone in maize using multivariate algorithms coupled to Raman spectroscopy
resolves10.1016/j.tifs.2014.01.005
Mycotoxins in cereals and related foodstuffs: A review on occurrence and recent methods of analysis
resolves10.1016/j.foodchem.2012.04.051
Determination of trichothecenes and zearalenones in grain cereal, flour and bread by liquid chromatography tandem mass spectrometry
resolves10.1016/j.chroma.2004.11.011
Rapid simultaneous determination of major type A- and B-trichothecenes as well as zearalenone in maize by high performance liquid chromatography–tandem mass spectrometry
resolves10.1016/j.aca.2010.03.010
Molecularly imprinted polymer solid-phase extraction for detection of zearalenone in cereal sample extracts
resolves10.1016/j.aca.2019.02.012
An electrochemical aptasensor for highly sensitive detection of zearalenone based on PEI-MoS2-MWCNTs nanocomposite for signal enhancement
resolves10.1021/jf503191e
Highly Sensitive Detection of Zearalenone in Feed Samples Using Competitive Surface-Enhanced Raman Scattering Immunoassay
resolves10.1039/C1AN15487G
Preparation of highly specific anti-zearalenone antibodies by using the cationic protein conjugate and development of an indirect competitive enzyme-linked immunosorbent assay
resolves10.1016/j.microc.2019.03.001
Single-use sensor technology for monitoring of zearalenone in foods: ZentoSens
resolves10.1016/j.jfoodeng.2013.04.001
Hyperspectral and multispectral imaging for evaluating food safety and quality
resolves10.1007/s10812-016-0217-1
Online Variety Discrimination of Rice Seeds Using Multispectral Imaging and Chemometric Methods
resolves10.1016/j.foodchem.2017.09.133
Classification of white maize defects with multispectral imaging
resolves10.1007/s12161-014-0038-x
Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach
resolves10.1016/j.foodchem.2014.10.052
Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit
resolves10.1016/j.chemolab.2012.07.010
A review of variable selection methods in Partial Least Squares Regression
resolves10.1016/S0893-6080(97)00014-2
Effects of Learning Parameters on Learning Procedure and Performance of a BPNN
resolves10.1016/S0305-0483(99)00036-5
Using principal component analysis in process performance for multivariate data
resolves10.1016/j.postharvbio.2008.03.013
Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy
resolves10.5194/gmd-7-1247-2014
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
The 6 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedref10
no DOI — not checkedFast determination of 14 mycotoxins in chestnut by dispersive solid-phase extraction coupled with ultraperformance liquid chromatography-tandem mass spectrometry
no DOI — not checkedApplication of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit
no DOI — not checkedRecent applications of multispectral imaging in seed phenotyping and quality monitoring-An overview
no DOI — not checkedA genetic algorithm for function optimization: a Matlab implementation
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