Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms

Miao, Xin; Miao, Ying; Liu, Yang; Tao, Shaohua; Zheng, Huabin; Wang, Jiemin; Wang, Weiqin; Tang, Qiyuan

doi:10.1016/j.saa.2022.121733

Cited by 23 publications

(3 citation statements)

References 39 publications

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“…This function is based on the principle of the siPLS algorithm. The core procedure of this function is to divide the pre-treated spectrum into 10 segments at equal intervals and randomly combine 1–6 segments to construct a calibration model [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Fan,

Qin,

et al. 2023

Foods

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Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models’ predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

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Section: Methodsmentioning

confidence: 99%

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Fan,

Qin,

et al. 2023

Foods

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“…If it is difficult to achieve the desired effect by applying the full spectral bands of the samples for near-infrared spectral analysis, therefore feature screening can reduce the complexity of the model, select the feature variables related to the target components among the many near-infrared spectral bands, reduce the influence of irrelevant variables on the model, and thus improve the prediction accuracy [10] [11] and [12]. The main methods for feature band screening are Moving Window PLS (MWPLS) [13], Interval PLS (iPLS) [14], and Synergy Interval PLS (SiPLS) [15], which are developed by partial least squares (PLS).…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction of Wastewater Index Using CNN Architecture and PLS Series Statistical Methods

Mo,

Xu,

Meng

et al. 2024

OJS

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Chemical oxygen demand (COD) is an important index to measure the degree of water pollution. In this paper, near-infrared technology is used to obtain 148 wastewater spectra to predict the COD value in wastewater. First, the partial least squares regression (PLS) model was used as the basic model. Monte Carlo cross-validation (MCCV) was used to select 25 samples out of 148 samples that did not conform to conventional statistics. Then, the interval partial least squares (iPLS) regression modeling was carried out on 123 samples, and the spectral bands were divided into 40 subintervals. The optimal subintervals are 20 and 26, and the optimal correlation coefficient of the test set (R T ) is 0.58. Further, the waveband is divided into five intervals: 17, 19, 20, 22 and 26. When the number of joint intervals under each interval is three, the optimal R T is 0.71. When the number of joint subintervals is four, the optimal R T is 0.79. Finally, convolutional neural network (CNN) was used for quantitative prediction, and R T was 0.9. The results show that CNN can automatically screen the features inside the data, and the quantitative prediction effect is better than that of iPLS and synergy interval partial least squares model (SiPLS) with joint subinterval three and four, indicating that CNN can be used for quantitative analysis of water pollution degree.

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“…Research on obtaining plant vigor information has been actively conducted using electromagnetic waves in various wavelength ranges; this has included using machine Sensors 2024, 24, 1160 2 of 13 vision for color image analysis for the purpose of cultivation and food process management [11][12][13][14][15][16]. For example, Lee et al developed a method for measuring spatial variability in the field for crop production by combining machine vision and thermographic techniques [17].…”

Section: Introductionmentioning

confidence: 99%

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement

Hashimoto,

Suehara,

Kameoka

2024

Sensors

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By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein–nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

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Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms

Cited by 23 publications

References 39 publications

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Rapid Prediction of Wastewater Index Using CNN Architecture and PLS Series Statistical Methods

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement

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