Multi-factor fusion models for soluble solid content detection in pear (Pyrus bretschneideri ‘Ya’) using Vis/NIR online half-transmittance technique

Visible and near infrared (Vis/NIR) spectroscopy combined with least squares-support vector machine (LS-SVM) was investigated for the determination of polysaccharides of auricularia auricula. A total of 240 samples were prepared from four different geographical origins. The calibration set was consisted of 180 samples (45 samples for each origin) and the remaining 60 samples for the validation set. Different preprocessing methods were compared in partial least squares (PLS) models including smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), the first and second derivative. PLS analysis was employed for the calibration models as well as extraction of certain latent variables (LVs). Simultaneously, some effective wavelengths (EWs) extracted by regression coefficients of LS-SVM were used as the inputs of LS-SVM compared with LVs. The optimal prediction results were achieved by LV-LS-SVM, and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.9413, 0.6893 and -0.0729, respectively. The results were slightly better than PLS. The prediction results of EW-LS-SVM using 3 EWs were acceptable with r=0.9290, RMSEP=0.7714 and Bias=-0.1737. The results indicated that Vis/NIR spectroscopy could be utilized as an efficient way for the determination of polysaccharides of auricularia auricula based on LS-SVM method.

Rapid and non-destructive determination of oil content in fuzzy cottonseeds via near-infrared reflectance spectroscopy

Online analysis of watercore apples by considering different speeds and orientations based on Vis/NIR full-transmittance spectroscopy

The objective of this paper was to determine soluble solids content (SSC) of intact Gannan navel orange by a portable near-infrared (NIR) spectrometer with the optical fiber in the wavelength range of 551~950nm. The effective wavelength regions (EWs) were chosen from the spectra pro-processed by second derivative by interval partial least square (iPLS) and backward interval partial least square (Bipls). Then the partial least square (PLS) and least square support vector machine (LS-SVM) models were developed with EWs. 60 unknown samples were used to evaluate the performance of them. The LS-SVM model was better than others with EWs chosen by Bilps. The correlation coefficient (R) and root mean square error of prediction (RMSEP) for LS-SVM (Bipls) were 0.86 and 0.55°Brix. The results showed that the portable NIR combination with LS-SVM was a feasible method to determine SSC of intact Gannan navel orange nondestructively.

The soluble solids content (SSC) of fruits is an important parameter that influences its internal quality. Visible near-infrared (Vis-NIR) spectroscopy is a effective means to detect the internal quality of fruits and vegetables. Measuring samples by instruments generates noise due to environmental factors and machine vibrations, which affects the accuracy of predictions. In this paper, we use standard normalized variables (SNV) and multiplicative scattering correction (MSC) to preprocess the spectral wavelengths, which can effectively reduce the effect of noise. In addition, spectral data contain many redundant variables and useless information, leading to poor prediction of the model. In order to solve this problem, this paper propose a wavelength selection method based on a hybrid strategy of Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) to screen the effective variables. And the final model is created by partial least squares (PLSR). The GA-CARS model with 84 selected variables has better predictive performance compared to the origin spectrum. In the experiments, samples are obtained from fresh citrus grown in farms around Guilin, and the spectra of citrus are detected in the range of 590 nm-940 nm with a Vis-NIR spectrometer. The experimental results showed that the performance of the prediction model is improved after wavelength screening (RMSEP=0.1581, R2=0.9245). Compared with the traditional algorithm, GA-CARS is an excellent method for screening variables, and the screened wavelengths combined with the model established by PLSR can be a rapid means to detect the SSC of citrus.

Identification of oil, sugar and crude fiber during tobacco (Nicotiana tabacum L.) seed development based on near infrared spectroscopy

For rapid detection of soluble solid content (SSC) in beer, visible/near infrared (Vis/NIR) spectra of 360 beer samples were collected by using Vis/NIR spectroradiometer. Principal component analysis (PCA) was applied for reducing the dimensionality in order to decrease the overlapped information of raw spectral data, 6 principal components (PCs) were selected. The samples were randomly separated into calibration set and validation set, and least squares support vector machine (LS-SVM) algorithm was used to build calibration model of SSC in beer, then the model was employed for the prediction of the validation set. Correlation coefficient (r) of prediction and root mean square error prediction (RMSEP) were used as evaluation standards, and the results indicated that r and RMSEP for the prediction of SSC were 0.9829 and 0.1506. The precision of prediction was obviously higher than that of back-propagation artificial neural network (BP-ANN) and partial least squares (PLS) models, hence PCA and LS-SVM algorithm model with high prediction precision could be applied to the determination of SSC in beer.

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R2) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05.

The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.

Detection of chlorpyrifos and carbendazim residues in the cabbage using visible/near-infrared spectroscopy combined with chemometrics

Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.

This paper deals with modelling on the nitrogen oxides (NOx) emission of a 600MW coal-fired boiler using artificial neural network (ANN), least squares support vector machine (LSSVM) and partial least squares (PLS) methods based on the experimental data. Some comparisons on the prediction accuracy, time consuming and some other aspects are also given. Two simulation cases are investigated to make a convincing comparison. One operation point is used to test the model in the first case and the other case is carried out to forecast two operation points. The results show that the ANN gives the best training accuracy; LSSVM exhibits a moderate activity and PLS is least time-consuming and easy to explain the importance of the independent variables.

Two chemometric methods were performed for the determination of acetic acid of fruit vinegars using near infrared (NIR) spectroscopy. Three varieties of fruit vinegars were prepared and 135 samples (45 samples for each variety) were selected for the calibration set, whereas 45 samples (15 samples for each variety) for the validation set. Partial least squares (PLS) analysis was the calibration method as well as extraction method for latent variables (LVs). The first eight LVs were employed as the inputs of least squares-support vector machine (LS-SVM) model. Then LS-SVM model with radial basis function (RBF) kernel was applied to build the regression model compared with PLS model. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.994, 0.814 and -0.091 by PLS, whereas 0.997, 0.651 and 0.011 by LS-SVM, respectively. LS-SVM model outperformed PLS model, but both models achieved an excellent prediction precision. The results indicated that NIR spectroscopy combined with chemometrics could be utilized as a high precision and fast way for the determination of acetic acid of fruit vinegars.

The application of near infrared spectroscopy for quantitative analysis of cotton-polyester textile was investigated in the present work. A total of 214 cotton-polyester fabric samples, covering the range from 0% to 100% cotton were measured and analyzed. Partial least squares and least-squares support vector machine models with all variables as input data were established. Furthermore, successive projection algorithm was used to select effective wavelengths and establish the successive projection algorithm-least-squares support vector machine models, with the comparison of two other effective wavelength selection methods: loading weights analysis and regression coefficient analysis. The calibration and validation results show that the successive projection algorithm-least-squares support vector machine model outperformed not only the partial least squares and least-squares support vector machine models with all variables as inputs, but also the least-squares support vector machine models with loading weights analysis and regression coefficient analysis effective wavelength selection. The root mean squared error of calibration and root mean squared error of prediction values of the successive projection algorithm-least-squares support vector machine regression model with the optimal performance were 0.77% and 1.17%, respectively. The overall results demonstrated that near infrared spectroscopy combined with least-squares support vector machine and successive projection algorithm could provide a simple, rapid, economical and non-destructive method for determining the composition of cotton-polyester textiles.

Visible and near infrared (Vis/NIR) spectroscopy combined with a hybrid calibrations of partial least squares (PLS), BP neural network (BPNN) and least squares-support vector machine (LS-SVM) was investigated to determine the pH values of rice vinegars. Five varieties of rice vinegars and 300 samples were prepared. After some preprocessing, PLS was implemented for calibration as well as the extraction of principal components, which would be used as the inputs of BPNN and LS-SVM models. Finally, the BPNN and LS-SVM regression models were developed with a comparison of PLS. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.982, 0.042, and -0.012 by PLS, 0.992, 0.027, and 0.004 by BPNN, while 0.996, 0.018, and 0.005 by LS-SVM, respectively. The results indicated that Vis/NIR spectroscopy combined with chemometrics could be applied as a high precision way for the determination of pH of rice vinegars.