Examination of Volatile Signatures of Fusarium Bulb Rot in Garlic Using Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry and Solid-Phase Microextraction Gas Chromatography/Mass Spectrometry

Abstract

Fusarium bulb rot, caused by Fusarium proliferatum, is a worldwide disease of garlic, both in the open field and during storage. Early diagnosis of the disease during storage is difficult due to the morphology of the bulbs and cloves. Volatile organic compounds (VOCs) are secondary metabolites produced by several microorganisms, including phytopathogenic fungi and bacteria. In recent years, the development of several techniques for the detection and characterization of VOCs has prompted their use, among others, as a diagnostic tool for the early and non-destructive analysis of many diseases of species of agricultural interest. In this paper, proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and solid-phase microextraction gas chromatography/mass spectrometry (SPME-GC/MS) were successfully utilized to characterize the volatolome of commercial garlic cloves, artificially and naturally infected with F. proliferatum, for the early discrimination between diseased and healthy ones. A partial least squares discriminant analysis (PLSDA) and a principal component analysis (PCA) allowed for the separation of infected and healthy cloves and the identification of specific VOCs produced by the fungus during the infection. The results obtained in this work could be utilized for the development of simpler, more economical, and more portable devices for the early detection of infected garlic bulbs during storage.

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species

“…A partial least squares discriminant analysis (PLSDA) linear approach was used to characterize species or fresh/dried peppers according to the VOCs identified with the PTR-ToF-MS technique. PLSDA consists of a PLS regression analysis in which the response variable is categorical (Y-block; replaced by a set of dummy variables describing the species or the fresh/dried samples), expressing the class membership of the statistical units [ 22 , 23 , 24 ]. The general architecture of the non-linear model used is a shallow neural network (SNN) [ 25 ], which consists of a single hidden layer with a given number of neurons (50) with a rectified linear unit (ReLU) activation function and one output layer with a softmax activation function (normalized exponential function) that normalizes the output in a range of values between 0 and 1.…”

Recent Advances in Understanding and Controlling Fusarium Diseases of Alliums

“…Ethanol, ethyl formate, ethyl acetate, 2-methyl-1-propanol, methyl thioacetate, n-propyl acetate, and 3-methyl-1-butanol were identified as potential biomarkers for Fusarium species in onion. Infantino et al [54] identified nine VOCs that differentiated healthy garlic cloves from cloves infected with F. proliferatum. Additionally, a principal component analysis of different datasets (healthy/artificially inoculated/naturally infected cloves) proved this technique to be a useful tool for the early detection of disease development.…”