Influence of wavelet transform settings on NIR and MIR spectrometric analyses of diesel, gasoline, corn and wheat

Pinto, Luiz Alberto; Galvão, Roberto Kawakami Harrop; Araújo, Mário César Ugulino de

doi:10.1590/s0103-50532011000100025

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…This becomes possible by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy associated with multivariate regression methods such as partial least square (PLS). Studies demonstrated the possibility to predict some diesel properties using midinfrared spectroscopy combined with chemometric tools [ 14 – 18 ], some aimed at the prediction of biodiesel content [ 16 ], and others were devoted to the identification of diesel adulteration with waste vegetable oils [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Nespeca

Hatanaka

Flumignan

et al. 2018

Journal of Analytical Methods in Chemistry

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Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

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Section: Introductionmentioning

confidence: 99%

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Nespeca

Hatanaka

Flumignan

et al. 2018

Journal of Analytical Methods in Chemistry

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Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide

et al. 2022

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Iron oxide is the main form of iron present in soils, and its accumulation and migration activities reflect the leaching process and the degree of weathering development of the soil. Therefore, it is important to have information on the iron oxide content of soils. However, due to the overlapping characteristic spectra of iron oxide and organic matter in the visible-near infrared, appropriate spectral transformation methods are important. In this paper, we first used conventional spectral transformation (continuum removal, CR; standard normal variate, SNV; absorbance, log (1/R)), continuous wavelet transform (CWT), and fractional order differential (FOD) transform to process original spectra (OS). Secondly, competitive adaptive reweighted sampling (CARS) was used to extract characteristic wavelengths. Finally, two regression models (backpropagation neural network, BPNN; support vector regression (SVR) were used to predict the content of iron oxide. The results show that the FOD can significantly improve the correlation with iron oxide compared with the CR, SNV, log (1/R) and CWT; the baseline drift and overlapping peaks decrease with increasing the order of FOD; the CARS algorithm based on 50th averaging can select more stable characteristic wavelengths; the FOD achieves better results regardless of the modelling method, and the model based on 0.5-order differential has the best prediction performance (R2 = 0.851, RMSE = 5.497, RPIQ = 3.686).

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Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis

et al. 2020

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The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (Rc2) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (Rv2) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models.

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Influence of wavelet transform settings on NIR and MIR spectrometric analyses of diesel, gasoline, corn and wheat

Cited by 3 publications

References 18 publications

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

Application of a Fractional Order Differential to the Hyperspectral Inversion of Soil Iron Oxide

Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis

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