Cydonia oblonga 1,669 claims

Claim · model-written summary

All Vis/NIR regression models gave reliable predictions of PPO, POD and PME activities in quince fruit.

Taxon

quinceCydonia oblongae1

Outcome

PPO, POD and PME activitiese1

Value

R² > 0.92, RPD > 2.8e2

Verbatim

t0

“quince fruit”

e1

“predict PPO, POD and PME activities in quince fruit non-destructively”

e2

“All models provided reliable predictions (R² > 0.92, RPD > 2.8)”

Claim · model-written summary

Full-spectrum and effective-wavelength models showed comparable performance.

Taxon

quinceCydonia oblongae1

Outcome

performancee3

Comparator

full-spectrume3

Verbatim

t0

“quince fruit”

e1

“predict PPO, POD and PME activities in quince fruit non-destructively”

e3

“A comparable performance was observed between full-spectrum”

Claim · model-written summary

Full-spectrum models benefit from broader chemical representativeness and potentially enhanced robustness compared with EW-based models.

Taxon

quinceCydonia oblongae1

Outcome

broader chemical representativeness and potentially enhanced robustnesse4

Comparator

EW-based modelse5

Verbatim

t0

“quince fruit”

e1

“predict PPO, POD and PME activities in quince fruit non-destructively”

e4

“Full-spectrum models benefit from broader chemical representativeness and potentially enhanced robustness”

e5

“whereas EW-based models offer clear advantages in terms of reduced dimensionality, improved interpretability, and suitability for real-time or embedded sensing applications”

Claim · model-written summary

EW-based models offer advantages in reduced dimensionality, interpretability and suitability for real-time sensing compared with full-spectrum models.

Taxon

quinceCydonia oblongae1

Outcome

reduced dimensionality, improved interpretability, and suitability for real-time or embedded sensing applicationse5

Comparator

Full-spectrume4

Verbatim

t0

“quince fruit”

e1

“predict PPO, POD and PME activities in quince fruit non-destructively”

e4

“Full-spectrum models benefit from broader chemical representativeness and potentially enhanced robustness”

e5

“whereas EW-based models offer clear advantages in terms of reduced dimensionality, improved interpretability, and suitability for real-time or embedded sensing applications”

Claim · model-written summary

The methodology is broadly applicable for on-line quality control and real-time monitoring of enzymatic browning and softening in other fruit.

Taxon

quinceCydonia oblongae1

Outcome

broad applicability for on-line quality control and real-time monitoring of enzymatic browning and softeninge6

Verbatim

t0

“quince fruit”

e1

“predict PPO, POD and PME activities in quince fruit non-destructively”

e6

“The methodology offers broad applicability for on-line quality control and real-time monitoring of enzymatic browning and softening in other fruit commodities”

Claim · model-written summary

WSS-YOLO achieved 86.4% precision on the quince maturity dataset, improving on the YOLOv11n baseline.

Taxon

quinceCydonia oblongae0

Outcome

precisione0

Direction

improvinge0

Value

86.4%e0

Comparator

YOLOv11n baselinee0

Verbatim

e0

“Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively”

Claim · model-written summary

WSS-YOLO achieved 87.5% recall on the quince maturity dataset, improving on the YOLOv11n baseline.

Taxon

quinceCydonia oblongae0

Outcome

recalle0

Direction

improvinge0

Value

87.5%e0

Comparator

YOLOv11n baselinee0

Verbatim

e0

“Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively”

Claim · model-written summary

WSS-YOLO achieved 93.4% mAP@0.5 on the quince maturity dataset, improving on the YOLOv11n baseline.

Taxon

quinceCydonia oblongae0

Outcome

mAP@0.5e0

Direction

improvinge0

Value

93.4%e0

Comparator

YOLOv11n baselinee0

Verbatim

e0

“Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively”

Claim · model-written summary

WSS-YOLO has only 2.23 M parameters.

Taxon

quinceCydonia oblongae0

Outcome

parameterse1

Value

2.23 Me1

Verbatim

e0

“Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively”

e1

“The model contains only 2.23 M parameters and requires 4.1 G FLOPs”

Claim · model-written summary

WSS-YOLO requires 4.1 G FLOPs.

Taxon

quinceCydonia oblongae0

Outcome

FLOPse1

Value

4.1 Ge1

Verbatim

e0

“Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively”

e1

“The model contains only 2.23 M parameters and requires 4.1 G FLOPs”