Abstract
Xinyu pear is one of the important pear cultivars in China. Owing to its rich nutritional composition, high quality, and distinctive flavor, it is highly favored in the market. In this study, near-infrared spectroscopy was employed to determine the soluble solid content (SSC) of Xinyu pears. To investigate the influence of spectral preprocessing on SSC prediction, near-infrared spectra of two batches of Xinyu pear samples were collected using the same portable spectrometer under different acquisition parameters, resulting in differences in spectral bands. A linear interpolation method was introduced to the first batch to generate a new dataset to match the dimensionality of the second batch, and a total of three datasets were used. Five preprocessing methods, including moving average smoothing (MA), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (D1), and second derivative (D2), together with three regression models, namely partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN), were systematically evaluated and compared in terms of predictive accuracy. Overall, PLSR achieved the best prediction performance, followed by CNN and SVR. Certain differences in model performance were observed among the three datasets. In general, MA exhibited the best overall performance across different datasets and models. Although SNV and MSC were slightly inferior to MA, they showed relatively stable predictive accuracy. By contrast, prediction models based on derivative spectra generally performed poorly. To further exploit the complementary information contained in differently preprocessed spectra, a four-branch CNN model was constructed using raw spectra, MA-preprocessed spectra, SNV-preprocessed spectra, and MSC-preprocessed spectra as separate inputs. Based on the fused features extracted by the CNN, PLSR and SVR models were subsequently developed. The prediction correlation coefficients of the feature-fusion CNN model on the prediction sets of the three datasets were 0.8811, 0.8259, and 0.7064, respectively. For the original datasets of the first and second batches, the feature-fusion model outperformed all single-preprocessing models. For the dataset generated by linear interpolation, the predictive performance of the feature-fusion strategy was comparable across the three models; specifically, its accuracy in SVR exceeded that of all single-preprocessing models, while its accuracies in CNN and PLSR surpassed those of most preprocessing methods. These results demonstrate that integrating feature information from spectra subjected to different preprocessing methods is a feasible strategy for improving prediction accuracy. This study provides an effective reference for SSC prediction in Xinyu pears based on portable spectrometers.
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Claims 13
Claim · model-written summary
PLSR achieved the best prediction performance for pear soluble solid content, followed by CNN and SVR.
Verbatim
e1“Overall, PLSR achieved the best prediction performance, followed by CNN and SVR”
Claim · model-written summary
Moving average smoothing (MA) gave the best overall performance across datasets and models, with SNV and MSC slightly inferior to MA.
Verbatim
e1“In general, MA exhibited the best overall performance across different datasets and models. Although SNV and MSC were slightly inferior to MA”
Claim · model-written summary
SNV and MSC showed relatively stable predictive accuracy.
Verbatim
e1“SNV and MSC were slightly inferior to MA, they showed relatively stable predictive accuracy”
Claim · model-written summary
Prediction models based on derivative spectra generally performed poorly.
- Outcome
- generally performed poorlye1
Not reportedComparator
Verbatim
e1“By contrast, prediction models based on derivative spectra generally performed poorly”
Claim · model-written summary
Certain differences in model performance were observed among the three datasets.
Verbatim
e1“Certain differences in model performance were observed among the three datasets”
Claim · model-written summary
The feature-fusion CNN model reached a prediction correlation coefficient of 0.8811 on the prediction set of the first dataset.
Verbatim
e1“The prediction correlation coefficients of the feature-fusion CNN model on the prediction sets of the three datasets were 0.8811, 0.8259, and 0.7064, respectively”
Claim · model-written summary
The feature-fusion CNN model reached a prediction correlation coefficient of 0.8259 on the prediction set of the second dataset.
Verbatim
e1“The prediction correlation coefficients of the feature-fusion CNN model on the prediction sets of the three datasets were 0.8811, 0.8259, and 0.7064, respectively”
Claim · model-written summary
The feature-fusion CNN model reached a prediction correlation coefficient of 0.7064 on the prediction set of the third (interpolated) dataset.
Verbatim
e1“The prediction correlation coefficients of the feature-fusion CNN model on the prediction sets of the three datasets were 0.8811, 0.8259, and 0.7064, respectively”
Claim · model-written summary
For the original datasets of the first and second batches, the feature-fusion model outperformed all single-preprocessing models.
Verbatim
e1“For the original datasets of the first and second batches, the feature-fusion model outperformed all single-preprocessing models”
Claim · model-written summary
For the linearly interpolated dataset, the feature-fusion strategy in SVR exceeded the accuracy of all single-preprocessing models.
Verbatim
e1“its accuracy in SVR exceeded that of all single-preprocessing models”
Claim · model-written summary
For the linearly interpolated dataset, the feature-fusion accuracies in CNN and PLSR surpassed those of most preprocessing methods.
Verbatim
e1“its accuracies in CNN and PLSR surpassed those of most preprocessing methods”
Claim · model-written summary
For the linearly interpolated dataset, the predictive performance of the feature-fusion strategy was comparable across the three models.
Verbatim
e1“the predictive performance of the feature-fusion strategy was comparable across the three models”
Claim · model-written summary
Integrating feature information from spectra with different preprocessing methods is a feasible strategy for improving prediction accuracy of soluble solid content in pears.
Verbatim
e1“integrating feature information from spectra subjected to different preprocessing methods is a feasible strategy for improving prediction accuracy”
t0“Xinyu Pears”