Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data
The paper presents the spectroscopic data obtained from a homemade NIR spectrometer developed for agricultural quality analysis, along with the calibration and validation of a model database for predicting agricultural soil properties. We collected NIR spectral data from 190 soil samples taken at a...
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| author | Natchanon Santasup Parichat Theanjumpol Choochad Santasup Sila Kittiwachana Nipon Mawan Nuttapon Khongdee |
| author_facet | Natchanon Santasup Parichat Theanjumpol Choochad Santasup Sila Kittiwachana Nipon Mawan Nuttapon Khongdee |
| author_sort | Natchanon Santasup |
| collection | DOAJ |
| description | The paper presents the spectroscopic data obtained from a homemade NIR spectrometer developed for agricultural quality analysis, along with the calibration and validation of a model database for predicting agricultural soil properties. We collected NIR spectral data from 190 soil samples taken at a depth of 0-20 cm from agricultural areas in northern Thailand, including vegetable farms, orchards, and field crops. The acquisition process started by air-drying the soil and sieving it through 2.0 mm and 0.5 mm mesh. Six preprocessing techniques, including Savitzky-Golay smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative, second derivative, and mean centering, were used with partial least squares (PLS) regression to create the prediction model for soil organic matter and total carbon. Seventy percent of the sample was divided into calibration and the remaining thirty percent was validation. The most suitable model for assessing soil organic matter (SOM) and total carbon is Savitzky-Golay smoothing through the PLSR model, with a coefficient of determination (R2) of 0.79 and 0.78, a root mean square error (RMSE) of 0.701% and 0.382% for validation samples, respectively. Thus, the NIR dataset spanning 900-1,700 nm proved to be an ideal wavelength range for developing a portable/handheld NIR spectrometer, with potential for further accuracy improvements through model refinement. |
| format | Article |
| id | doaj-art-b9b7d308c1904b748999593b73fd8362 |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-b9b7d308c1904b748999593b73fd83622025-08-20T03:18:20ZengElsevierData in Brief2352-34092025-08-016111184010.1016/j.dib.2025.111840Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley DataNatchanon Santasup0Parichat Theanjumpol1Choochad Santasup2Sila Kittiwachana3Nipon Mawan4Nuttapon Khongdee5Department of Plant and Soil Science, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand; Postharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, ThailandPostharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, ThailandDepartment of Plant and Soil Science, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, ThailandDepartment of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, ThailandDepartment of Highland Agriculture and Natural Resources, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, ThailandDepartment of Highland Agriculture and Natural Resources, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand; Corresponding author.The paper presents the spectroscopic data obtained from a homemade NIR spectrometer developed for agricultural quality analysis, along with the calibration and validation of a model database for predicting agricultural soil properties. We collected NIR spectral data from 190 soil samples taken at a depth of 0-20 cm from agricultural areas in northern Thailand, including vegetable farms, orchards, and field crops. The acquisition process started by air-drying the soil and sieving it through 2.0 mm and 0.5 mm mesh. Six preprocessing techniques, including Savitzky-Golay smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative, second derivative, and mean centering, were used with partial least squares (PLS) regression to create the prediction model for soil organic matter and total carbon. Seventy percent of the sample was divided into calibration and the remaining thirty percent was validation. The most suitable model for assessing soil organic matter (SOM) and total carbon is Savitzky-Golay smoothing through the PLSR model, with a coefficient of determination (R2) of 0.79 and 0.78, a root mean square error (RMSE) of 0.701% and 0.382% for validation samples, respectively. Thus, the NIR dataset spanning 900-1,700 nm proved to be an ideal wavelength range for developing a portable/handheld NIR spectrometer, with potential for further accuracy improvements through model refinement.http://www.sciencedirect.com/science/article/pii/S2352340925005670ChemometricPre-processing techniqueModel developmentSoil spectroscopySoil fertility |
| spellingShingle | Natchanon Santasup Parichat Theanjumpol Choochad Santasup Sila Kittiwachana Nipon Mawan Nuttapon Khongdee Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data Data in Brief Chemometric Pre-processing technique Model development Soil spectroscopy Soil fertility |
| title | Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data |
| title_full | Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data |
| title_fullStr | Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data |
| title_full_unstemmed | Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data |
| title_short | Dataset of near-infrared (NIR) spectral data for prediction of organic matter and total carbon in agricultural soil using homemade NIR spectrometerMendeley Data |
| title_sort | dataset of near infrared nir spectral data for prediction of organic matter and total carbon in agricultural soil using homemade nir spectrometermendeley data |
| topic | Chemometric Pre-processing technique Model development Soil spectroscopy Soil fertility |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925005670 |
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