Early Detection of Fusarium Basal Rot Infection in Onions and Shallots Based on VOC Profiles Analysis.

Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection's duration with an average R2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection.

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose.

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes.

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

Investigation of paediatric PKU breath malodour, comparing glycomacropeptide with phenylalanine free L-amino acid supplements.

In clinical practice, caregivers of children with phenylketonuria (PKU) report that their children have breath malodour. This might be linked to the regular consumption of low phenylalanine (Phe)/Phe-free protein substitutes (PS), which are an essential component of a low-Phe diet. Oral malodour can negatively affect interpersonal communication, lead to bullying, low self-esteem and social isolation. In this longitudinal cross-over study, exhaled volatile organic compounds (VOCs) were measured using gas chromatography-ion mobility spectrometry. 40 children (20 PKU, 20 controls) were recruited. Subjects with PKU took either L-Amino Acid (L-AA) or Casein Glycomacropeptide (CGMP-AA) exclusively for 1 week, in a randomised order. On the seventh day, seven exhaled breath samples were collected over a 10 h period. Subjects then transferred to the other PS for a week and on day seven, provided seven further breath samples. All subjects had a standardised menu using low-Phe food alternatives and all food intake was measured and recorded. In the PKU group, the aim was to collect samples 30 min after consuming PS. In 3 subjects, breath was collected 5 min post-PS consumption. Fasted L-AA and CGMP-AA breath samples contained a similar number of VOC peaks (10-12) as controls. Longitudinal breath testing results demonstrate that there was no significant difference in the number of exhaled VOCs, comparing L-AA or CGMP-AA with controls, or between PS (12-18 VOC peaks). Breath analysed immediately after consumption of PS (n = 3) showed an immediate increase in the number of VOC peaks (25-30), but these were no longer detectable at 30 min post-consumption. This suggests PS have a transient effect on exhaled breath. Measurements taken 30 min after consuming L-AA or CGMP-AA were not significantly different to controls. This indicates that timing food and drinks with PS consumption may be a potential solution for carers to reduce or eliminate unpleasant PS-related breath odours.

Detection of potato storage disease via gas analysis: a pilot study using field asymmetric ion mobility spectrometry.

Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation).