Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., mu g/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean +/- standard deviation) was obtained by applying cut-offs of 10,000 mu g/kg DM (i.e., 96.0 +/- 2.7%) or 34 (i.e., 97.1 +/- 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 +/- 2.8% was obtained by using a cut-off limit of 350 mu g/kg DM as a total sum or 98.6 +/- 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.

Ghilardelli, F., Barbato, M., Gallo, A., A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy, <<TOXINS>>, 2022; 14 (5): N/A-N/A. [doi:10.3390/toxins14050323]

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli, F;Barbato, M;Gallo, A
2022

Abstract

Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., mu g/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean +/- standard deviation) was obtained by applying cut-offs of 10,000 mu g/kg DM (i.e., 96.0 +/- 2.7%) or 34 (i.e., 97.1 +/- 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 +/- 2.8% was obtained by using a cut-off limit of 350 mu g/kg DM as a total sum or 98.6 +/- 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.
Inglese
Ghilardelli, F., Barbato, M., Gallo, A., A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy, <<TOXINS>>, 2022; 14 (5): N/A-N/A. [doi:10.3390/toxins14050323]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/219577
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