Effects of Pretreatment Methods and Bands Selection on Soil Nutrient Hyperspectral Evaluation

Xu, Li; Xie, Deti; Fan, Fangling

doi:10.1016/j.proenv.2011.09.376

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…29,30 Compared with Partial Least Squares (PLS), Synergy Interval Partial Least Squares (Si-PLS) needs a smaller number of spectra variables, but the result is generally not less than the result from PLS model. 29,30 Compared with Partial Least Squares (PLS), Synergy Interval Partial Least Squares (Si-PLS) needs a smaller number of spectra variables, but the result is generally not less than the result from PLS model.…”

Section: Chemometric Methods and Evaluation Indexmentioning

confidence: 99%

“…The application of SG-D1 is benecial not only for the removal of noise but also for extending the differences of characteristics. 29,30 Compared with Partial Least Squares (PLS), Synergy Interval Partial Least Squares (Si-PLS) needs a smaller number of spectra variables, but the result is generally not less than the result from PLS model. [31][32][33] The Support Vector Machine (SVM) determines the appropriate trade-off between the learning ability of limited samples and the learned accuracy of specic samples for the best generalization performance.…”

Section: Chemometric Methods and Evaluation Indexmentioning

confidence: 99%

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Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

et al. 2014

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Rapid and nondestructive measurement of freshness is essential for the control of fish and the quality and safety of its products. In this study, K value was measured by high performance liquid chromatography (HPLC) and employed as an index of fish freshness. The prediction models of the silver chub freshness were developed using Fourier Transform Near Infrared Reflectance Spectroscopy (FT-NIRS) with Several Partial Least Squares (PLS, i-PLS, Si-PLS), Support Vector Machines Regression (SVMR) and Synergy interval plus Support Vector Machine Regression leading to Si-SVMR. By comparison, the performance of Si-SVMR model was superior to the others for the prediction of K value, where RMSECV ¼ 0.027095 and R c ¼ 95.59% for the calibration set, whereas RMSEP ¼ 0.036525 and R p ¼ 93.74% for the prediction set.The results indicated that FT-NIR spectroscopy together with Si-SVMR model could be a reliable method for the detection of fish freshness.

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Section: Chemometric Methods and Evaluation Indexmentioning

confidence: 99%

Section: Chemometric Methods and Evaluation Indexmentioning

confidence: 99%

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

et al. 2014

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“…transformation of the spectrum weakened the influence of multiplicative factors caused by the variation in light conditions [39]. In addition, FDR processing eliminated the interference of background noise, decomposed the mixed overlapping peaks, and improved the spectral resolution and sensitivity, making it easy to locate the highly correlated bands [55]. In this study, the correlation coefficient obtained after differential transformation also indicated that mathematical transformation enhanced the sensitivity of some spectral bands, which was consistent with the conclusions in previous studies.…”

Section: Plos Onementioning

confidence: 99%

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

et al. 2021

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Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

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“…In addition, due to the in uence of environmental factors and grain status during the spectrum acquisition process, the preprocessing method can overcome the in uence of external factors, thereby improving the signal-to-noise ratio of spectral information and improving the modeling accuracy [11][12][13] . However, the principles of different modeling methods are quite different, which will also lead to different performances of the optimal model of wheat quality and content [14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Analyzing protein concentration from intact wheat caryopsis using hyperspectral reflectance

Zhang

Hou

et al. 2023

Preprint

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Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed by using a non-destructive approach. For this purpose, spectral data and protein content data of grain and grain powder were obtained. After combining six types of pre-processed spectra and four types of multivariate statistical models, a relationship hyperspectral datasets and grain protein content is presented. It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of Rv2 = 0.814, RMSEv = 0.024, and RPDv = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of Rv2 = 0.789, RMSEv = 0.026, and RPDv = 2.177. Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat.

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Effects of Pretreatment Methods and Bands Selection on Soil Nutrient Hyperspectral Evaluation

Cited by 10 publications

References 6 publications

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

Analyzing protein concentration from intact wheat caryopsis using hyperspectral reflectance

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