![](https://calibrationmodel.com/wp/wp-content/uploads/2021/04/Regression-Model-Mango-DM-SEP0.725-dry-matter-prediction-in-mango-fruit-with-near-infrared-NIR-spectroscopy-1024x819.png)
CSet : calibration set, VSet : validation set, TSet : test set
Spectral Range : 285 to 1200 Nanometers [nm] (306 datapoints)
Download the calibration model file here
with some of the spectra as JCAMP-DX
to predict with free NIR-Predictor software.
![](https://calibrationmodel.com/wp/wp-content/uploads/2021/04/NIR-Prediction-Report-Mango-DM-dry-matter-prediction-in-mango-fruit-with-near-infrared-NIR-spectroscopy.png)
Open Access Data from the paper
- https://doi.org/10.1016/j.chemolab.2021.104287
- "A benchmark RMSEP of 0.79% was attained."
- "Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%."
- Puneet Mishra, Dário Passos, "A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit", Chemometrics and Intelligent Laboratory Systems,Volume 212,2021,104287,ISSN 0169-7439,https://doi.org/10.1016/j.chemolab.2021.104287.
![](https://calibrationmodel.com/wp/wp-content/uploads/2021/04/Regression-Model-Mango-DM-SEP0.725-dry-matter-prediction-in-mango-fruit-with-near-infrared-NIR-spectroscopy-1024x819.png)
CSet : calibration set, VSet : validation set, TSet : test set
Spectral Range : 285 to 1200 Nanometers [nm] (306 datapoints)
Download the calibration model file here
with some of the spectra as JCAMP-DX
to predict with free NIR-Predictor software.
![](https://calibrationmodel.com/wp/wp-content/uploads/2021/04/NIR-Prediction-Report-Mango-DM-dry-matter-prediction-in-mango-fruit-with-near-infrared-NIR-spectroscopy.png)
Open Access Data from the paper
- https://doi.org/10.1016/j.chemolab.2021.104287
- "A benchmark RMSEP of 0.79% was attained."
- "Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%."
- Puneet Mishra, Dário Passos, "A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit", Chemometrics and Intelligent Laboratory Systems,Volume 212,2021,104287,ISSN 0169-7439,https://doi.org/10.1016/j.chemolab.2021.104287.
![](https://calibrationmodel.com/wp/wp-content/uploads/2021/04/Regression-Model-Mango-DM-SEP0.725-dry-matter-prediction-in-mango-fruit-with-near-infrared-NIR-spectroscopy-1024x819.png)
CSet : calibration set, VSet : validation set, TSet : test set
Spectral Range : 285 to 1200 Nanometers [nm] (306 datapoints)
Download the calibration model file here
with some of the spectra as JCAMP-DX
to predict with free NIR-Predictor software.
![](https://calibrationmodel.com/wp/wp-content/uploads/2021/04/NIR-Prediction-Report-Mango-DM-dry-matter-prediction-in-mango-fruit-with-near-infrared-NIR-spectroscopy.png)
Open Access Data from the paper
- https://doi.org/10.1016/j.chemolab.2021.104287
- "A benchmark RMSEP of 0.79% was attained."
- "Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%."
- Puneet Mishra, Dário Passos, "A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit", Chemometrics and Intelligent Laboratory Systems,Volume 212,2021,104287,ISSN 0169-7439,https://doi.org/10.1016/j.chemolab.2021.104287.