NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples

Miao, Xin; Miao, Ying; Gong, Hang; Tao, Shaohua; Chen, Zuwu; Wang, Jiemin; Chen, Yingzi; Chen, Yancheng

doi:10.1016/j.saa.2021.119700

Cited by 37 publications

(8 citation statements)

References 39 publications

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“…In the same way, the optimal n value of hemicellulose was 30, and 5 characteristic spectral regions (13, 22, 25, 27, and 29) and 306 CWVs were selected. It can be seen from Table 2 that it is crucial to select an appropriate n value when using the BIPLS algorithm to optimize the characteristic wavelength [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

Wang

Feng

et al. 2022

Molecules

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The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.

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Section: Resultsmentioning

confidence: 99%

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

Wang

Feng

et al. 2022

Molecules

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“…Spectral data preprocessing is an essential step in spectral analysis, focusing on data quality enhancement through a series of sophisticated methods. [24][25][26] This process commences with the optimization of the data's initial state, involving noise reduction and baseline adjustment. Subsequently, standardization is employed to ensure the data's uniformity and comparability, which is critical for the accuracy of further analyses and model development.…”

Section: Spectral Data Preprocessing and Data Analysis Methodsmentioning

confidence: 99%

Spectraformer: deep learning model for grain spectral qualitative analysis based on transformer structure

Chen,

Zhou,

Ren

2024

RSC Adv.

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“…CARS is a feature selection method that combines Monte Carlo (MC) sampling with Partial Least Squares (PLS) model regression coefficients, mimicking the principle of “survival of the fittest” in Darwinian theory [ 34 , 35 ]. In the CARS algorithm, adaptive weighted sampling is used to retain points with larger absolute values of regression coefficients in the PLS model as a new subset, removing points with smaller weights, and then establishing a PLS model based on the new subset.…”

Section: Methodsmentioning

confidence: 99%

Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging

Xue,

Xu,

Yang

et al. 2024

Sensors

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The moisture content of corn seeds is a crucial indicator for evaluating seed quality and is also a fundamental aspect of grain testing. In this experiment, 80 corn samples of various varieties were selected and their moisture content was determined using the direct drying method. The hyperspectral imaging system was employed to capture the spectral images of corn seeds within the wavelength range of 1100–2498 nm. By utilizing seven preprocessing techniques, including moving average, S–G smoothing, baseline, normalization, SNV, MSC, and detrending, we preprocessed the spectral data and then established a PLSR model for comparison. The results show that the model established using the normalization preprocessing method has the best prediction performance. To remove spectral redundancy and simplify the prediction model, we utilized SPA, CASR, and UVE algorithms to extract feature wavelengths. Based on three algorithms (PLSR, PCR, and SVM), we constructed 12 predictive models. Upon evaluating these models, it was determined that the normalization-SPA-PLSR algorithm produced the most accurate prediction. This model boasts high RC2 and RP2 values of 0.9917 and 0.9914, respectively, along with low RMSEP and RMSECV values of 0.0343 and 0.0257, respectively, indicating its exceptional stability and predictive capabilities. This suggests that the model can precisely estimate the moisture content of maize seeds. The results showed that hyperspectral imaging technology provides technical support for rapid and non-destructive prediction of corn seed moisture content and new methods in seed quality evaluation.

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NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples

Cited by 37 publications

References 39 publications

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection

Spectraformer: deep learning model for grain spectral qualitative analysis based on transformer structure

Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging

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