Spectral Preprocessing Methods

Bian, Xihui

doi:10.1007/978-981-19-1625-0_4

Cited by 14 publications

(7 citation statements)

References 96 publications

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“…The methods based on the four inputs had higher accuracy than the model using all bands. The SOM prediction performance is related to the spectral preprocessing method [9,44]. It has been shown that mathematical methods for resampling the spectral curves and enhancing the linearity of the spectral features improved prediction accuracy.…”

Section: Comparison Of Grouping Methods and Inputsmentioning

confidence: 99%

“…Preprocessing is required to improve data quality and model performance. Typical preprocessing methods for spectral data include Savitzky-Golay convolutional smoothing (S-G), continuum removal (CR), resampling, mathematical transforms, and other methods [8,9]. These methods can effectively mitigate noise and improve the accuracy and credibility of the data, providing suitable data for establishing prediction models with high performance.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

Zhang,

Dong,

Liu

et al. 2024

Remote Sensing

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Soil organic matter (SOM) is an essential component of soil and is crucial for increasing agricultural production and soil fertility. The combination of hyperspectral remote sensing and deep learning can be used to predict the SOM content efficiently, rapidly, and cost-effectively on various scales. However, determining the optimal groups, inputs, and models for reducing the spatial heterogeneity of soil nutrients in large regions and to improve the accuracy of SOM prediction remains a challenge. Hyperspectral reflectance data from 1477 surface soil samples in Northeast China were utilized to evaluate three grouping methods (no groups (NG), traditional grouping (TG), and spectral grouping (SG)) and four inputs (raw reflectance (RR), continuum removal (CR), fractional-order differentiation (FOD), and spectral characteristic parameters (SCPs)). The SOM prediction accuracies of random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM) models were assessed. The results were as follows: (1) The highest accuracy was achieved using SG, SCPs, and the LSTM model, with a coefficient of determination (R2) of 0.82 and a root mean squared error (RMSE) of 0.69%. (2) The LSTM model exhibited the highest accuracy in SOM prediction (R2 = 0.82, RMSE = 0.89%), followed by the CNN model (R2 = 0.72, RMSE = 0.85%) and the RF model (R2 = 0.69, RMSE = 0.91%). (3) The SG provided higher SOM prediction accuracy than TG and NG. (4) The SCP-based prediction results were significantly better than those of the other inputs. The R2 of the SCP-based model was 0.27 higher and the RMSE was 0.40% lower than that of the RR-based model with NG. In addition, the LSTM model had higher prediction errors at low (0–2%) and high (8–10%) SOM contents, whereas the error was minimal at intermediate SOM contents (2–8%). The study results provide guidance for selecting grouping methods and approaches to improve the prediction accuracy of the SOM content and reduce the spatial heterogeneity of the SOM content in large regions.

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Section: Comparison Of Grouping Methods and Inputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

Zhang,

Dong,

Liu

et al. 2024

Remote Sensing

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“…The mean centering transformation, which is the mean of the absorption values of every sample spectrum in each wave band in the spectral data matrix, is subtracted from each value in that wave band; hence, the mean centering centers the values corresponding to each band about zero (modified [50]). The mean centering amplifies the differences between the sample spectra [51]. The mean normalization and max−min normalization normalize the spectra so that they have a common feature by dividing each absorption value of each band in the raw spectrum by the average absorption value and the range (subtracting the maximum value from the minimum value) absorption value, respectively, of the spectrum.…”

Section: Spectral Pretreatmentmentioning

confidence: 99%

“…The mean normalization and max−min normalization normalize the spectra so that they have a common feature by dividing each absorption value of each band in the raw spectrum by the average absorption value and the range (subtracting the maximum value from the minimum value) absorption value, respectively, of the spectrum. The normalization pretreatment corrects the spectral change caused by small light path differences [51].…”

Section: Spectral Pretreatmentmentioning

confidence: 99%

Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain

Kaewsorn,

Phanomsophon,

Maichoon

et al. 2023

Foods

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If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000–12,500 cm−1. The calibration sample set was separated from the prediction set by the Kennard–Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, −0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3–7506 and 4242.9–4605.4 cm−1, which included the amylose vibration band at 6834.0 cm−1, while the toughness model was from 6094.3 to 9403.8 cm−1 and included the 6834.0 and 8316.0 cm−1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.

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“…Then, a linear baseline taken between the minimum and maximum wavelengths was subtracted. Finally, the spectra were normalized by Multiplicative Signal Correction, a common approach in analytical spectroscopy [46]. This methodology was optimized through different trials so as to give the best classification performances.…”

Section: Chemometric Approachmentioning

confidence: 99%

Time-Resolved Laser-Induced Fluorescence Spectroscopy and Chemometrics for Fast Identification of U(Vi)-Bearing Minerals in a Mining Context

Sirven

Szenknect²,

Vors³

et al. 2023

Preprint

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Spectral Preprocessing Methods

Cited by 14 publications

References 96 publications

Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain

Time-Resolved Laser-Induced Fluorescence Spectroscopy and Chemometrics for Fast Identification of U(Vi)-Bearing Minerals in a Mining Context

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