scholarly journals Volatile Fingerprinting (SPME-GC-FID) to Detect and Discriminate Diseases of Potato Tubers

Plant Disease ◽  
2002 ◽  
Vol 86 (2) ◽  
pp. 131-137 ◽  
Author(s):  
A. C. Kushalappa ◽  
L. H. Lui ◽  
C. R. Chen ◽  
B. Lee
Keyword(s):  
Phytophthora Infestans ◽  
Solid Phase ◽  
Back Propagation ◽  
Erwinia Carotovora ◽  
Pythium Ultimum ◽  
Potato Tubers ◽  
Disease Group ◽  
Flame Ionization ◽  
Volatile Profiles ◽  
Propagation Network

Volatiles from Russet Burbank potatoes inoculated with Erwinia carotovora subsp. carotovora, E. carotovora subsp. atroseptica, Pythium ultimum, Phytophthora infestans, or Fusarium sambucinum were monitored by sampling the head space 3, 4, and 5 days after inoculation, using a solid phase microextraction (SPME) fiber to trap and gas chromatography with flame ionization detector (GC-FID) to fingerprint volatiles. Noninoculated (NON) potatoes served as the control. Volatile fingerprints varied among diseases. Within a disease, the fingerprints varied with time since inoculation and among blocks. In general, more volatiles were observed on the fourth and fifth day after inoculation than on the third day. The amount of volatile compounds produced (peak area) within a disease group increased with incubation time; however, the variation among blocks was much higher. The amount of volatiles produced, in general, was associated with disease severity. Disease-specific volatiles were observed. The F. sambucinum chromatogram had two unique peaks at retention time (RT) = 14.1 and 17.3 min. P. infestans produced few peaks and the profile was quite similar to NON. In contrast, E. carotovora subsp. carotovora, E. carotovora subsp. atroseptica, and Pythium ultimum produced many peaks, and the P. ultimum was different from the bacteria, in that the chromatogram peaks at RT = 4.04 and 8.76 min were absent. Instead, it produced a distinct peak at RT = 1.71 min. E. carotovora subsp. carotovora and E. carotovora subsp. atroseptica couldn't be discriminated based on unique peaks; however, they varied in concentration of volatiles produced. E. carotovora subsp. carotovora produced more of RT = 2.0 min and less of RT = 2.3 and 2.44 min than E. carotovora subsp. atroseptica. A back-propagation network (using neural networks) was developed to classify volatile profiles into six disease-groups. Cross-validation classification probabilities were NON = 71, E. carotovora subsp. carotovora = 71, E. carotovora subsp. atroseptica = 71, P. ultimum = 67, Phytophthora infestans = 46, and F. sambucinum = 75%.

scholarly journals Authentification of fruit spirits using HS-SPME/GC-FID and OPLS methods

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tomáš Bajer ◽  
Martin Hill ◽  
Karel Ventura ◽  
Petra Bajerová
Keyword(s):  
Solid Phase ◽  
External Validation ◽  
Basic Result ◽  
Hierarchical Cluster ◽  
Pome Fruit ◽  
Orthogonal Projections ◽  
Flame Ionization ◽  
Volatile Profiles ◽  
Fruit Spirits

Abstract This research provides an accurate description of the origin for fruit spirits. In total, 63 samples of various kinds of fruit spirits (especially from apples, pears, plums, apricots and mirabelle) were analysed using headspace-solid phase microextraction and gas chromatography with flame-ionization detector. Obtained volatile profiles were treated and analysed by multivariate regression with a reduction of dimensionality-orthogonal projections to latent structure for the classification of fruit spirits according to their fruit of origin. Basic result of statistical analysis was the differentiation of spirits to groups with respect to fruit kind. Tested kinds of fruit spirits were strictly separated from each other. The selection was achieved with a specificity of 1.000 and a sensitivity of 1.000 for each kind of spirit. The statistical model was verified by an external validation. Hierarchical cluster analysis (calculation of distances by Ward’s method) showed a similarity of volatile profiles of pome fruit spirits (apple and pear brandies) and stone fruit spirits (especially mirabelle and plum brandies).

scholarly journals Effect of Harvest and Drying on Composition of Volatile Profile of Elderflowers (Sambucus nigra) from Wild

2017 ◽  
Vol 12 (12) ◽  
pp. 1934578X1701201 ◽  
Author(s):  
Tomáš Bajer ◽  
Petra Bajerová ◽  
Karel Ventura
Keyword(s):  
Solid Phase ◽  
Insect Pheromones ◽  
Volatile Profile ◽  
Sambucus Nigra ◽  
Total Peak Area ◽  
Physicochemical Processes ◽  
Flame Ionization ◽  
Drying Procedures ◽  
Volatile Profiles ◽  
Site Field

Volatile compounds emitted by elderflowers ( Sambucus nigra L.) at various stages (blooming on the bush, and at different stages after harvesting) were analyzed to investigate changes in the composition of volatile profiles induced by two drying procedures. Solid-phase microextraction and simultaneous distillation-solvent extraction were used for concentration of volatiles. Analyses of extracts were performed using gas chromatography coupled with a mass spectrometer or a flame ionization detector. On-site field sampling of volatiles followed-up by chromatographic analysis provided interesting information, including insect pheromones from 55 % to 79 % of the total peak area in the GC/MS chromatograms. Composition of aroma compounds of harvested elderflowers was strongly influenced by the type of drying procedure, where the content of some volatiles decreased and some substances even occur due to ongoing physicochemical processes. Changes in volatile profile caused by harvesting were also observed.

scholarly journals A Methodology to Detect and Quantify Five Pathogens Causing Potato Tuber Decay Using Real-Time Quantitative Polymerase Chain Reaction

2006 ◽  
Vol 96 (9) ◽  
pp. 1037-1045 ◽  
Author(s):  
Z. K. Atallah ◽  
W. R. Stevenson
Keyword(s):  
Polymerase Chain Reaction ◽  
Real Time ◽  
Quantitative Polymerase Chain Reaction ◽  
Erwinia Carotovora ◽  
Pythium Ultimum ◽  
Single Copy ◽  
Potato Tubers ◽  
Chain Reaction ◽  
Dry Rot ◽  
Polymerase Chain

Late blight (Phytophthora infestans), pink rot (Phytophthora erythroseptica), leak (Pythium ultimum), dry rot (Fusarium sambucinum), and soft rot (Erwinia carotovora subsp. carotovora and subsp. atroseptica) are particularly damaging diseases of stored potato tubers worldwide. In this study, we present a methodology to detect and quantify the causal agents of the five aforementioned diseases from whole potato tubers, using real-time quantitative-polymerase chain reaction. Six primer pairs were designed to amplify targets smaller than 150-bp DNA in single copy protein-coding gene targets of each of the pathogens and the potato host. Using a large collection of pure culture DNA samples, all primer pairs specifically detected the DNA target in the intended pathogenic species. Amplification efficiencies over a five-log dilution series ranged between 95 and 100% and were unaffected by the presence of large amounts of host DNA. The detection level of the primers reached 0.5 pg of target DNA. Pathogens were detected in 100 pg of total DNA extracted from 170 to 250 g of tubers, 4 days after inoculation, regardless of the presence of symptoms. The presence of P. erythroseptica, Pythium ultimum, or E. carotovora was also detected in 1 ng of DNA extracted from potato tubers collected from a commercial storage facility. This study provides the first step in a methodology to predict the storability of potato tubers following harvest.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Inhibition of Fungal Growth by Macerated Plant Tissues in a Closed Environment

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 476e-476
Author(s):  
Craig S. Charron ◽  
Catherine O. Chardonnet ◽  
Carl E. Sams
Keyword(s):  
Radial Growth ◽  
Fungal Pathogens ◽  
Solid Phase ◽  
Indian Mustard ◽  
Fungal Growth ◽  
Allyl Isothiocyanate ◽  
Pythium Ultimum ◽  
Alternative Methods ◽  
Gas Chromatography Mass Spectrometry ◽  
Clean Air

The U.S. Clean Air Act bans the use of methyl bromide after 2001. Consequently, the development of alternative methods for control of soilborne pathogens is imperative. One alternative is to exploit the pesticidal properties of macerated tissues of Brassica spp. This study tested the potential of several Brassica spp. for control of fungal pathogens. Pythium ultimum Trow or Rhizoctonia solani Kühn plugs on potato-dextrose agar on petri dishes were sealed in 500-ml glass jars (at 22 °C) containing macerated leaves (10 g) from one of six Brassica spp. Radial growth was measured 24, 48, and 72 h after inoculation. Indian mustard (B. juncea) was the most suppressive, followed by `Florida Broadleaf' mustard (B. juncea). Volatile compounds in the jars were sampled with a solid-phase microextraction device (SPME) and identified by gas chromatography-mass spectrometry (GC-MS). Allyl isothiocyanate (AITC) comprised over 90% of the total volatiles measured from Indian mustard and `Florida Broadleaf' mustard. Isothiocyanates were detected in jars with all plants except broccoli. (Z)-3-hexenyl acetate was emitted by all plants and was the predominant volatile of `Premium Crop' broccoli (B. oleracea L. var. italica), `Michihili Jade Pagoda' Chinese cabbage (B. pekinensis), `Charmant' cabbage (B. oleracea L. var. capitata), and `Blue Scotch Curled' kale (B. oleracea L. var. viridis). To assess the influence of AITC on radial growth of P. ultimum and R. solani, AITC was added to jars to give headspace concentrations of 0.10, 0.20, and 0.30 mg·L–1 (mass of AITC per volume of headspace). Growth of both fungi was inhibited by 0.10 mg·L–1 AITC. 0.20 mg·L–1 AITC was fungicidal to P. ultimum although the highest AITC level tested (0.30 mg·L–1) did not terminate R. solani growth. These results indicate that residues from some Brassica spp. may be a viable part of a soilborne pest control strategy.

scholarly journals Elucidation of the Volatilome of Packaged Spanish-Style Green Olives of Conservolea and Halkidiki Varieties Using SPME-GC/MS

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 75
Author(s):  
Alexandra Nanou ◽  
Athanasios Mallouchos ◽  
Efstathios Z. Panagou
Keyword(s):  
Volatile Compounds ◽  
Solid Phase ◽  
Acid Ethyl Ester ◽  
Volatile Components ◽  
Propanoic Acid ◽  
Qualitative Composition ◽  
Organoleptic Characteristics ◽  
Increasing Trend ◽  
Volatile Profiles ◽  
Over Time

Olives are characterized by a wide variety of volatile compounds, which are primarily products of microbial metabolism that contribute to the organoleptic characteristics of the final product and especially to its flavor. The volatilome in Spanish-style processed green olives of Conservolea and Halkidiki cultivars were analytically characterized. A solid phase micro-extraction (SPME) technique was used for the extraction of volatile components from the olive samples that were further identified and quantified by gas chromatography coupled to mass spectrometry (GC–MS). Eighty-eight (88) compounds were identified, including several aldehydes, ketones, acids, terpenes, but mainly esters and alcohols. Results showed that there were no significant differences in the qualitative composition of the volatile profiles between the two varieties. Acetic and propanoic acids, thymol, ethanol, 2-butanol, 1-propanol, ethyl acetate as well as ethyl propanoate were the most dominant compounds found in both cultivars. However, some quantitative differences were spotted between the two varieties regarding some of the identified volatile compounds. The quantity of 2-butanol was higher in the Halkidiki variety, while propanoic acid ethyl ester was found in higher amounts in the Conservolea variety. Furthermore, differences in the quantities of some volatile compounds over time were observed. Most of the identified compounds presented an increasing trend during storage.

scholarly journals Changes in Phenols, Polysaccharides and Volatile Profiles of Noni (Morinda citrifolia L.) Juice during Fermentation

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2604
Author(s):  
Zhulin Wang ◽  
Rong Dou ◽  
Ruili Yang ◽  
Kun Cai ◽  
Congfa Li ◽  
...  
Keyword(s):  
Octanoic Acid ◽  
Solid Phase ◽  
Principal Component ◽  
Hexanoic Acid ◽  
Morinda Citrifolia ◽  
Gas Chromatography Mass Spectrometry ◽  
Aroma Characteristics ◽  
Volatile Profiles ◽  
The Difference ◽  
Aroma Components

The change in phenols, polysaccharides and volatile profiles of noni juice from laboratory- and factory-scale fermentation was analyzed during a 63-day fermentation process. The phenol and polysaccharide contents and aroma characteristics clearly changed according to fermentation scale and time conditions. The flavonoid content in noni juice gradually increased with fermentation. Seventy-three volatile compounds were identified by solid-phase microextraction coupled with gas chromatography–mass spectrometry (SPME-GC-MS). Methyl hexanoate, 3-methyl-3-buten-1-ol, octanoic acid, hexanoic acid and 2-heptanone were found to be the main aroma components of fresh and fermented noni juice. A decrease in octanoic acid and hexanoic acid contents resulted in the less pungent aroma in noni juice from factory-scale fermentation. The results of principal component analysis of the electronic nose suggested that the difference in nitrogen oxide, alkanes, alcohols, and aromatic and sulfur compounds, contributed to the discrimination of noni juice from different fermentation times and scales.

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

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