Postharvest firmness behaviour of near-isogenic lines of melon

Tijskens, L.M.M.; Dos-Santos, Noelia; Jowkar, M.M.; Obando–Ulloa, Javier M.; Moreno, E.; Schouten, R.E.; Monforte, Antonio J.; Fernández-Trujillo, J.P.

doi:10.1016/j.postharvbio.2008.06.001

Cited by 20 publications

(18 citation statements)

References 32 publications

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“…The experimental plot consisted of six plants per replicate (three plants located in two adjacent rows) with n = 21 and n = 9 replicates in S1 and S2, respectively. Two to three fruits per replicate were harvested between 07:00 and 10:00 h in one week in each season according to previously-reported minimum and optimum harvest maturity indices for PS [6,15]. The most critical parameters for externally identifying full maturity at harvest in the field were a developed stem scar, followed by peduncle lignification, and the onset of a light yellow color in the skin surrounding the peduncle, a dull dark green skin color, and a minimum light yellow color in the skin in contact with the soil.…”

Section: Plant Materials and Experimental Designmentioning

confidence: 99%

“…The objective of the present work was to develop a methodology for the systematic observation of strong outliers in VOC analysis of samples in an integrated manner that allows some potentially anomalous data to be excluded in the determination of fruit aromas. This is particularly critical for QTL mapping and in the determination of QTL × environment effects because the variability could be higher than in other cases, particularly in plots in open fields for breeding purposes, and/or in fruit where the exact degree of maturity is difficult to assess due to biological variance, e.g., melon [15]. For this, the study was based on the same parental studied in two different seasons.…”

Section: Introductionmentioning

confidence: 99%

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Methodology to Remove Strong Outliers of Non-Climacteric Melon Fruit Aroma at Harvest Obtained by HS-SPME GC-MS Analysis

2018

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A methodology for making consistent studies of outliers of non-climacteric melon volatile organic compounds at harvest is reported. The juice was squeezed from the fruit of the 'Piel de sapo' cultivar harvested during two consecutive seasons and the aroma volatiles were extracted by headspace solid phase microextraction and measured by gas chromatography coupled to mass-spectrometry. A deconvolution analysis was performed to obtain volatile organic compounds. For multivariate the reliable identification of outliers, compound classes were studied as a percentage of total area counts of the melon compounds identified in the chromatogram by principal component analysis and partial least-squares discriminant analysis, and then verified by correlation analysis, box-whisker plot, and formal tests for univariate outliers. Principal component analysis was the key methodology for selecting outliers in variables that mostly did not follow a normal distribution. The presence of an excess in terms of relative percentage of area and the diversity of minor compounds such as alcohols, terpenes, acids, among others, are usually a sign of anomalous data that can be considered outliers in the aroma of this non-climacteric cultivar. This multivariate approach removed outliers, but kept the variability of aroma among the samples of every cultivar.

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Section: Plant Materials and Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology to Remove Strong Outliers of Non-Climacteric Melon Fruit Aroma at Harvest Obtained by HS-SPME GC-MS Analysis

2018

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“…Exponential behaviour, both decay as well as production, is frequently encountered in experimental data, e.g., firmness (Schouten et al, 2007(Schouten et al, , 2010Tijskens et al, 2009a). The model formulations, expressed in biological shift factor notation (∆t) are shown in Equation 1 (exponential decay), exponential production (Equation 2).…”

Section: The Modelsmentioning

confidence: 99%

Techniques to assess biological variation in destructive dataaa To the memory of Marjan Simčič (1960-2016), believer in and promotor of studying biological variation from the start.

Tijskens¹,

Schouten²,

Jongbloed³

et al. 2018

Acta Hortic.

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Variation is present in all measured data, due to variation between individuals (biological variation) and variation induced by the measuring system (technical variation). Biological variation present in experimental data is not the result of a random process but strictly subject to deterministic rules as found on non-destructive data. The majority of data obtained in research are obtained by destructive techniques. The rules on behaviour and magnitude of variation should however, also apply to these cross sectional data. New techniques have been developed for analysing cross sectional data including the assessment of variation: 1) Probelation. In a set of cross-sectional data, the individual with the highest value at some point in time will resemble the individual with the highest value at previous or future times, and the second highest the second highest at previous times, and so on. One can assign an identification number based on the sorted order of the measured values per measuring point in time. This number can be used as a pseudo fruit number in indexed or mixed effects regression analysis, similar to the data analysis of longitudinal data; 2) Density assessment. For not too complex kinetic processes the density function can be deduced. Measuring a large number of individuals (on a single point in time) provides the possibility to assess directly the variation in the data; 3) Quantile regression. This technique also relies on ranking the data per measuring time. The probelation number is now converted into a probability, and the mean and standard deviation is estimated directly along with the kinetic parameter, using simple non-linear regression. Based on simulated data sets, all three techniques are demonstrated, and the results compared with the input values. Explained parts (R 2 adj) obtained are generally well over 90%, provided that the technical variation is not excessively large.

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“…In a study on the development of firmness of near isogenic lines of melons (26 lines in 2 consecutive seasons) during postharvest storage (Tijskens et al, 2009), data on non-destructively measured firmness were analysed using the biological shift factor systems according to an exponential model (first order kinetics Eqs. (1)-(5).…”

Section: Firmness Of Near Isogenic Lines Of Melonsmentioning

confidence: 99%

Basic principles of analysing biological and technical variation in non-destructive data

Tijskens¹,

Schouten²,

Konopacki

et al. 2015

Computers and Electronics in Agriculture

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Postharvest firmness behaviour of near-isogenic lines of melon

Cited by 20 publications

References 32 publications

Methodology to Remove Strong Outliers of Non-Climacteric Melon Fruit Aroma at Harvest Obtained by HS-SPME GC-MS Analysis

Methodology to Remove Strong Outliers of Non-Climacteric Melon Fruit Aroma at Harvest Obtained by HS-SPME GC-MS Analysis

Techniques to assess biological variation in destructive dataaa To the memory of Marjan Simčič (1960-2016), believer in and promotor of studying biological variation from the start.

Basic principles of analysing biological and technical variation in non-destructive data

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