Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

Li, Chunxu; Zhao, Jinghan; Li, Yaoxiang; Meng, Yongbin; Zhang, Zheyu

doi:10.3390/f12121809

Cited by 16 publications

(9 citation statements)

References 42 publications

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“…In this study, PLSR was used to construct monitoring models for the content of total flavonoids and total phenols in grains. Combined with the previous experience [ 19 , 40 ], the cross-validation method was used to determine the optimal number of latent variables, and the model with the optimal number of latent variables was the best model under the preprocessing. The results showed that the calibration accuracy and validation accuracy of the total flavonoids content monitoring model based on different preprocessing algorithms were similar, and the best model was obtained under SD transformation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Yang

Song

Wei

et al. 2023

Foods

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Tartary buckwheat is a common functional food. Its grains are rich in flavonoids and phenols. The rapid measurement of flavonoids and phenols in buckwheat grains is of great significance in promoting the development of the buckwheat industry. This study, based on multiple scattering correction (MSC), standardized normal variate (SNV), reciprocal logarithm (Lg), first-order derivative (FD), second-order derivative (SD), and fractional-order derivative (FOD) preprocessing spectra, constructed hyperspectral monitoring models of total flavonoids content and total phenols content in tartary buckwheat grains. The results showed that SNV, Lg, FD, SD, and FOD preprocessing had different effects on the original spectral reflectance and that FOD can also reflect the change process from the original spectrum to the integer-order derivative spectrum. Compared with the original spectrum, MSC, SNV, Lg, FD, and SD transformation spectra can improve the correlation between spectral data and total flavonoids and total phenols in varying degrees, while the correlation between FOD spectra of different orders and total flavonoids and total phenols in grains was different. The monitoring models of total flavonoids and total phenols in grains based on MSC, SNV, Lg, FD, and SD transformation spectra achieved the best accuracy under SD and FD transformation, respectively. Therefore, this study further constructed monitoring models of total flavonoids and total phenols content in grains based on the FOD spectrum and achieved the best accuracy under 1.6 and 0.6 order derivative preprocessing, respectively. The R2c, RMSEc, R2v, RMSEv, and RPD were 0.8731, 0.1332, 0.8384, 0.1448, and 2.4475 for the total flavonoids model, and 0.8296, 0.2025, 0.6535, 0.1740, and 1.6713 for the total phenols model. The model can realize the rapid measurement of total flavonoids content and total phenols content in tartary buckwheat grains, respectively.

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Section: Discussionmentioning

confidence: 99%

“…In its modeling process, it is necessary to select the number of potential variables. This study used the leave one cross-validation method to determine the number of latent variables [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Yang

Song

Wei

et al. 2023

Foods

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“…However, different studies used different methods in evaluating degraded forest ecosystems, resulting in different criteria for selecting and grading indicators for forest ecological restoration [25][26][27][28]. This shows the limitations in the current research, which restrict the development of ecological restoration practices and restoration ecology, thus necessitating harmonized criteria for evaluating the restoration effect for forest ecological function [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Ecological Function Restoration Effect for Degraded Natural Forests in Xiaoxinganling, China

Qu,

Dong,

Zhang

et al. 2024

Sustainability

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To study the effect of ecological function restoration in degraded natural forests in Xiaoxinganling, Heilongjiang Province, China, we set up 90 plots of degraded natural forests of different types and ages in the Xinqing Group Branch of Yichun Forestry Industry Group in Xiaoxinganling, Heilongjiang Province, China. Moreover, 43 evaluation indexes, including litter characteristics, species diversity, soil physical properties, soil chemical properties, and canopy structural parameters, were selected to determine the effect of ecological function restoration in the degraded natural forests in the study area. Principal component analysis (PCA) was used to comprehensively evaluate the ecological function of the degraded forests. The results of the study showed that, based on the forest type scores, mixed coniferous and broadleaf forests, mixed coniferous forests, and broadleaf forests had higher scores, while the scores of pure Betula platyphylla forests, pure Larix gmelinii forests, and the three low-quality forests were lower. This showed that the ecological restoration effect for mixed forests in the Xinqing Group Branch of Yichun Sengyong Group of Xinjiangqing Group, Xiaoxinganling, Heilongjiang Province, China, was better than that for pure forests and low-quality forests. Based on forest age, the scores of near-mature forests and mature forests were generally higher than those of young forests and over-mature forests, indicating that the ecological restoration effect for near-mature forests and mature forests in Xinqing Group Branch of Yichun Forestry Industry Group in Xiaoxinganling, Heilongjiang Province, China, was better than that for young forests and over-mature forests. These evaluation results can provide a theoretical basis for subsequent research on the ecological restoration effect in degraded natural forests in Xiaoxinganling, Heilongjiang Province, China.

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“…By collecting the reflectance, absorption, and scattering spectral data of samples in the visible/near-infrared spectral range, visible/near-infrared spectroscopy can provide information about the external characteristics, internal physical structure, and chemical composition of the samples. In recent years, both domestic and international research has successfully utilized visible/near-infrared spectroscopy to predict soluble solids content, moisture content, vitamin C content, and other parameters in fruits and vegetables [ 9 , 10 , 11 , 12 , 13 , 14 ]. These research results demonstrate the enormous potential of visible/near-infrared spectroscopy in the quality assessment of fruits and vegetables.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy

Li,

Wang,

Wang

2024

Foods

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Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.

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Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy

Cited by 16 publications

References 42 publications

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Evaluation of Ecological Function Restoration Effect for Degraded Natural Forests in Xiaoxinganling, China

Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy

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