Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

Constructing an origin discrimination model of japonica rice in Heilongjiang Province based on confocal microscopy Raman spectroscopy technology

An origin discrimination model for rice from five production regions in Heilongjiang Province was constructed based on the combination of confocal microscopy Raman spectroscopy and chemometrics. A total of 150 field rice samples were collected from the Fangzheng, Chahayang, Jiansanjiang, Xiangshui, and Wuchang production areas. The optimal sample processing conditions, instrument parameter settings, and spectrum acquisition techniques were identified by investigating the influencing factor. The Raman spectra of milled rice within the range of 100-3200 cm-1 were selected as the raw data, and the optimal preprocessing method combination consisting of normalization, Savitzky-Golay smoothing, and multivariate scatter correction was identified. Subsequently, the competitive adaptive reweighted sampling and discrete binary particle swarm optimization algorithms were employed to optimize the feature wavelength selection, resulting in the screening of 226 and 1899 feature wavelength variables, respectively. Using the full Raman spectrum data and feature wavelength data as inputs, partial least squares discriminant analysis, support vector machine and extreme learning machine origin discrimination models were constructed. The results indicated that the BPSO-GA-SVM model exhibited the best predictive ability, achieving a testing set accuracy of 86.67%.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Identification and quantitative detection of illegal additives in wheat flour based on near-infrared spectroscopy combined with chemometrics

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

Monitoring of volatile fatty acids during anaerobic digestion of olive pomace by means of a hand held near infrared spectrometer

The accumulation of volatile fatty acids (VFAs) over anaerobic digestion (AD) leads to malfunctioning of industrial reactors, hence decreasing biogas production. Real-time monitoring of VFAs is a challenge due to the complexity and high cost of current methods for their quantification. For this reason, this research evaluated the application of near infrared (NIR) spectroscopy to quantify volatile fatty acids as a tool for AD reactors monitoring. To do that, 129 samples from various AD reactors fed with olive oil pomace were taken and their NIR spectra were acquired with a hand-held spectrometer. After performing grid search, three spectral variable selection methods, namely competitive adaptive reweighted sampling, uninformative variable elimination (UVE) and successive projections algorithm, were assayed before developing PLRS models to correlate the NIR light transmittance through the samples at the wavelengths selected by those methods with their VFAs concentrations. UVE led to the best performance for all the VFAs assayed. Thus, R2 of validation of UVE-PLSR models for acetic, propionic, butyric, valeric and total VFAs were 0.895, 0.622, 0.866, 0.898 and 0.871, respectively. The predictive model for total VFAs achieved the highest accuracy (RMSEV = 539.5 mg/L), explained by the correlation between the light absorption at the wavelengths selected by UVE and the chemical characteristics of VFAs. All in all, the prediction errors achieved suggest that a portable near infrared spectrometer can be used for monitoring VFAs in AD processes.

Rapid detection of chemical oxygen demand, pH value, total nitrogen, total phosphorus, and ammonia nitrogen in biogas slurry by near infrared spectroscopy

Effective treatment of sewage requires accurate measurement of important water quality parameters, such as chemical oxygen demand (COD), pH value, total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3-N). Traditional detection techniques can result in secondary contamination and are time- and labor-intensive. Near infrared spectroscopy was used in this study to create a model of these parameters of pig manure anaerobic fermentation sewage. The models' viability for quickly estimating the aforementioned water quality characteristics was reviewed, and the models' performance in predicting the results of several samples (biogas slurry, supernatant, and biogas residue) was contrasted. By analyzing the near infrared spectrograms with a spectral range of 4000 cm-1 and 12 500 cm-1 and using second derivative (SD), Savitzky-Golay smoothing (SG) and standard normal variable (SNV) to preprocess the spectra, partial least squares (PLS) was selected to establish the prediction model. The results showed that the effect of the NIR model constructed from the supernatant was better than that of biogas slurry and biogas residue. The determination coefficients for COD, pH value, NH3-N and TN were 0.69, 0.87, 0.81, and 0.94, respectively. This study could provide reference for on-line monitoring of wastewater in the future.

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.