Search claims · PlantClaims

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Linear regression predicted peach compression force with R² 0.87 and 9.66% average error.

Taxon: PeachPrunus persicat0
Outcome: compression forcee0
Value: 9.66%e0

Verbatim

e0
“R 2 = 0.87 for compression force; R 2 = 0.79 for sugar content; R 2 = 0.81 for pH) and relatively low average percentage errors (9.66%, 7.68% and 3.6%, for compression force, sugar content and pH, respectively)”
✓ located verbatim in the source · abstract, characters 269–479

t0
“Peach”
✓ located verbatim in the source · title, characters 14–19

Title

Evaluation of Peach Quality Attribute Using an Electronic Nose

Hongmei Zhang; Jun Wang; J Brezmes; et al. Sensors and Materials 2009. doi:10.18494/sam.2009.585

Other claims from this paper (5)

Method / resource Linear regression predicted peach sugar content with R² 0.79 and 7.68% average error.
Method / resource Linear regression predicted peach pH with R² 0.81 and 3.6% average error.
Method / resource Neural network predicted peach compression force with R² 0.90 and 6.39% average error.
Method / resource Neural network predicted peach sugar content with R² 0.81 and 6.21% average error.
Method / resource Neural network predicted peach pH with R² 0.87 and 3.13% average error.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Neural network predicted peach compression force with R² 0.90 and 6.39% average error.

Taxon: PeachPrunus persicat0
Outcome: compression forcee0
Value: 6.39%e0

Verbatim

e0
“R 2 = 0.90, 0.81 and 0.87 for compression force, sugar content and pH, respectively) between predicted and measured values and relatively low average percentage errors (6.39%, 6.21% and 3.13% for compression force, sugar content and pH, respectively)”
✓ located verbatim in the source · abstract, characters 583–833

t0
“Peach”
✓ located verbatim in the source · title, characters 14–19

Title

Evaluation of Peach Quality Attribute Using an Electronic Nose

Hongmei Zhang; Jun Wang; J Brezmes; et al. Sensors and Materials 2009. doi:10.18494/sam.2009.585

Other claims from this paper (5)

Method / resource Linear regression predicted peach compression force with R² 0.87 and 9.66% average error.
Method / resource Linear regression predicted peach sugar content with R² 0.79 and 7.68% average error.
Method / resource Linear regression predicted peach pH with R² 0.81 and 3.6% average error.
Method / resource Neural network predicted peach sugar content with R² 0.81 and 6.21% average error.
Method / resource Neural network predicted peach pH with R² 0.87 and 3.13% average error.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The validated mesocarp tissue block DE model reproduced block compression behaviour up to 20% deformation with an average relative error of 9.5%.

Taxon: tomatoSolanum lycopersicumt0
Organ / stage: mesocarp blocke2
Outcome: average relative errore2
Value: 9.5%e2
Qualifier: up to 20% deformatione2
Comparator: experimental force-deformation behaviore2

Verbatim

t0
“irrigation‐caused tomato cracking”
✓ located verbatim in the source · title, characters 125–158

e2
“The validated mesocarp tissue block DE model could reproduce the experimental force-deformation behavior of a mesocarp block in compression up to 20% deformation with an average relative error of 9.5%”
✓ located verbatim in the source · abstract, characters 713–913

Title

Analysis of the correlation between mesocarp biomechanics and its cell turgor pressure: A combined FEM‐DEM investigation for irrigation‐caused tomato cracking

Dongdong Li; Ying Liu; Tobi Fadiji; et al. Journal of Texture Studies 2022. doi:10.1111/jtxs.12720

Other claims from this paper (4)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Linear regression predicted peach pH with R² 0.81 and 3.6% average error.

Taxon: PeachPrunus persicat0
Outcome: pHe0
Value: 3.6%e0

Verbatim

e0
“R 2 = 0.87 for compression force; R 2 = 0.79 for sugar content; R 2 = 0.81 for pH) and relatively low average percentage errors (9.66%, 7.68% and 3.6%, for compression force, sugar content and pH, respectively)”
✓ located verbatim in the source · abstract, characters 269–479

t0
“Peach”
✓ located verbatim in the source · title, characters 14–19

Title

Evaluation of Peach Quality Attribute Using an Electronic Nose

Hongmei Zhang; Jun Wang; J Brezmes; et al. Sensors and Materials 2009. doi:10.18494/sam.2009.585

Other claims from this paper (5)

Method / resource Linear regression predicted peach compression force with R² 0.87 and 9.66% average error.
Method / resource Linear regression predicted peach sugar content with R² 0.79 and 7.68% average error.
Method / resource Neural network predicted peach compression force with R² 0.90 and 6.39% average error.
Method / resource Neural network predicted peach sugar content with R² 0.81 and 6.21% average error.
Method / resource Neural network predicted peach pH with R² 0.87 and 3.13% average error.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Linear regression predicted peach sugar content with R² 0.79 and 7.68% average error.

Taxon: PeachPrunus persicat0
Outcome: sugar contente0
Value: 7.68%e0

Verbatim

e0
“R 2 = 0.87 for compression force; R 2 = 0.79 for sugar content; R 2 = 0.81 for pH) and relatively low average percentage errors (9.66%, 7.68% and 3.6%, for compression force, sugar content and pH, respectively)”
✓ located verbatim in the source · abstract, characters 269–479

t0
“Peach”
✓ located verbatim in the source · title, characters 14–19

Title

Evaluation of Peach Quality Attribute Using an Electronic Nose

Hongmei Zhang; Jun Wang; J Brezmes; et al. Sensors and Materials 2009. doi:10.18494/sam.2009.585

Other claims from this paper (5)

Method / resource Linear regression predicted peach compression force with R² 0.87 and 9.66% average error.
Method / resource Linear regression predicted peach pH with R² 0.81 and 3.6% average error.
Method / resource Neural network predicted peach compression force with R² 0.90 and 6.39% average error.
Method / resource Neural network predicted peach sugar content with R² 0.81 and 6.21% average error.
Method / resource Neural network predicted peach pH with R² 0.87 and 3.13% average error.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Neural network predicted peach pH with R² 0.87 and 3.13% average error.

Taxon: PeachPrunus persicat0
Outcome: pHe0
Value: 3.13%e0

Verbatim

e0
“R 2 = 0.90, 0.81 and 0.87 for compression force, sugar content and pH, respectively) between predicted and measured values and relatively low average percentage errors (6.39%, 6.21% and 3.13% for compression force, sugar content and pH, respectively)”
✓ located verbatim in the source · abstract, characters 583–833

t0
“Peach”
✓ located verbatim in the source · title, characters 14–19

Title

Evaluation of Peach Quality Attribute Using an Electronic Nose

Hongmei Zhang; Jun Wang; J Brezmes; et al. Sensors and Materials 2009. doi:10.18494/sam.2009.585

Other claims from this paper (5)

Method / resource Linear regression predicted peach compression force with R² 0.87 and 9.66% average error.
Method / resource Linear regression predicted peach sugar content with R² 0.79 and 7.68% average error.
Method / resource Linear regression predicted peach pH with R² 0.81 and 3.6% average error.
Method / resource Neural network predicted peach compression force with R² 0.90 and 6.39% average error.
Method / resource Neural network predicted peach sugar content with R² 0.81 and 6.21% average error.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Neural network predicted peach sugar content with R² 0.81 and 6.21% average error.

Taxon: PeachPrunus persicat0
Outcome: sugar contente0
Value: 6.21%e0

Verbatim

e0
“R 2 = 0.90, 0.81 and 0.87 for compression force, sugar content and pH, respectively) between predicted and measured values and relatively low average percentage errors (6.39%, 6.21% and 3.13% for compression force, sugar content and pH, respectively)”
✓ located verbatim in the source · abstract, characters 583–833

t0
“Peach”
✓ located verbatim in the source · title, characters 14–19

Title

Evaluation of Peach Quality Attribute Using an Electronic Nose

Hongmei Zhang; Jun Wang; J Brezmes; et al. Sensors and Materials 2009. doi:10.18494/sam.2009.585

Other claims from this paper (5)

Method / resource Linear regression predicted peach compression force with R² 0.87 and 9.66% average error.
Method / resource Linear regression predicted peach sugar content with R² 0.79 and 7.68% average error.
Method / resource Linear regression predicted peach pH with R² 0.81 and 3.6% average error.
Method / resource Neural network predicted peach compression force with R² 0.90 and 6.39% average error.
Method / resource Neural network predicted peach pH with R² 0.87 and 3.13% average error.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The validated cell FE model with 12.9 kPa internal turgor pressure reproduced single-cell compression behaviour up to 11% deformation with an average relative error of 5.8%.

Taxon: tomatoSolanum lycopersicumt0
Organ / stage: single celle1
Outcome: average relative errore1
Value: 5.8%e1
Qualifier: internal turgor pressure of 12.9 kPae1
Comparator: experimental force-deformation behaviore1

Verbatim

t0
“irrigation‐caused tomato cracking”
✓ located verbatim in the source · title, characters 125–158

e1
“The validated cell FE model with an internal turgor pressure of 12.9 kPa could reproduce the experimental force-deformation behavior of a single cell in compression up to 11% deformation with an average relative error of 5.8%”
✓ located verbatim in the source · abstract, characters 486–711

Title

Analysis of the correlation between mesocarp biomechanics and its cell turgor pressure: A combined FEM‐DEM investigation for irrigation‐caused tomato cracking

Dongdong Li; Ying Liu; Tobi Fadiji; et al. Journal of Texture Studies 2022. doi:10.1111/jtxs.12720

Other claims from this paper (4)

“average compression force”

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