Fusarium proliferatum is considered to be a pathogen of many economically important plants, including garlic. The objective of this research was to apply near-infrared spectroscopy (NIRS) to rapidly determine fungal concentration in intact garlic cloves, avoiding the laborious and time-consuming procedures of traditional assays. Preventive detection of infection before seeding is of great interest for farmers, because it could avoid serious losses of yield during harvesting and storage. Spectra were collected on 95 garlic cloves, divided in five classes of infection (from 1-healthy to 5-very highly infected) in the range of fungal concentration 0.34–7231.15 ppb. Calibration and cross validation models were developed with partial least squares regression (PLSR) on pretreated spectra (standard normal variate, SNV, and derivatives), providing good accuracy in prediction, with a coefficient of determination (R2) of 0.829 and 0.774, respectively, a standard error of calibration (SEC) of 615.17 ppb, and a standard error of cross validation (SECV) of 717.41 ppb. The calibration model was then used to predict fungal concentration in unknown samples, peeled and unpeeled. The results showed that NIRS could be used as a reliable tool to directly detect and quantify F. proliferatum infection in peeled intact garlic cloves, but the presence of the external peel strongly affected the prediction reliability.

Quantitative Determination of Fusarium proliferatum Concentration in Intact Garlic Cloves Using Near-Infrared Spectroscopy

TAMBURINI, Elena
Primo
;
MAMOLINI, Elisabetta
Secondo
;
DE BASTIANI, Morena
Penultimo
;
MARCHETTI, Maria Gabriella
Ultimo
2016

Abstract

Fusarium proliferatum is considered to be a pathogen of many economically important plants, including garlic. The objective of this research was to apply near-infrared spectroscopy (NIRS) to rapidly determine fungal concentration in intact garlic cloves, avoiding the laborious and time-consuming procedures of traditional assays. Preventive detection of infection before seeding is of great interest for farmers, because it could avoid serious losses of yield during harvesting and storage. Spectra were collected on 95 garlic cloves, divided in five classes of infection (from 1-healthy to 5-very highly infected) in the range of fungal concentration 0.34–7231.15 ppb. Calibration and cross validation models were developed with partial least squares regression (PLSR) on pretreated spectra (standard normal variate, SNV, and derivatives), providing good accuracy in prediction, with a coefficient of determination (R2) of 0.829 and 0.774, respectively, a standard error of calibration (SEC) of 615.17 ppb, and a standard error of cross validation (SECV) of 717.41 ppb. The calibration model was then used to predict fungal concentration in unknown samples, peeled and unpeeled. The results showed that NIRS could be used as a reliable tool to directly detect and quantify F. proliferatum infection in peeled intact garlic cloves, but the presence of the external peel strongly affected the prediction reliability.
2016
Tamburini, Elena; Mamolini, Elisabetta; DE BASTIANI, Morena; Marchetti, Maria Gabriella
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2363353
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