SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2021
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IF 4.098
Maogang Li,Yaozhou Feng,Yan Yu,Tianlong Zhang,Chunhua Yan,Hongsheng Tang,Qinglin Sheng,Hua Li
Infrared spectroscopy (IR) combined with multivariate calibration technology can be used as a potential method to quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in soil, which provides a rapid data support for soil risk assessment. However, IR spectrum contains lots of useless information, its predictive performance is poor. Variable selection is an effective strategy to eliminate irrelevant wavelengths and enhance predictive performance. In this study, IR combined with partial least squares (PLS) was proposed to quantify anthracene and fluoranthene in soil. In order to improve the predictive performance of the PLS calibration model, the synergy interval PLS (siPLS) method was first used for “rough selection” to select feature bands; on this basis, “fine selection” was performed to extract the feature variables. In “fine selection”, three different feature variables selection methods, such as successive projection algorithm (SPA), genetic algorithm (GA), and particle swarm optimization (PSO), were compared for their performance in extracting effective variables. The results show that the siPLS-GA calibration model receive a lowest root mean square error (RMSE) and a largest determination coefficient (R 2 ). Results of external validation demonstrate an excellent predictive performance of siPLS-GA calibration model, with the R 2 = 0.9830, RMSE = 0.5897 mg/g and R 2 = 0.9849, RMSE = 0.4739 mg/g for anthracene and fluoranthene, respectively. In summary, siPLS combined with GA can accurately extract the effective information of the target substance and improve the predictive performance of the PLS calibration model based on IR spectroscopy.
研究领域:
环境科学
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分析化学
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产品关联
DOI
PubMed
高影响因子
Applied Sciences-Basel
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2019
·Vol. 9
·期 24
·5336
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IF 2.217
Qi XIA,Lei-ming YUAN,Xiaojing CHEN,Liuwei MENG,Guangzao HUANG
Featured ApplicationThe quality of methanol gasoline is important to internal combustion engines. Infrared spectroscopy technology shows its feasibility to qualitatively and quantitatively measure the quality of gasoline. Combined with the chemometrics, this technology can be used to determinate the adulteration category or fineness, providing the qualified gasoline for the market. Spectra analytical technology is a promising method in applying in the gasoline industry and speeding up the industrial application.AbstractMethanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.Keywords:methanol gasoline;infrared spectroscopy;partial least square discriminant analysis (PLS-DA);multivariate regression;variable selection
研究领域:
化学计量学
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能源科学
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产品关联
DOI
高影响因子
Methods and Applications in Fluorescence
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2018
·Vol. 6
·期 2
·025006
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IF 2.165
Jiajia Shan,Xue Wang,Hao Zhou,Shuqing Han,Dimas Firmanda Al Riza,Naoshi Kondo
Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.
研究领域:
分析化学
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食品科学
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化学计量学
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产品关联
DOI
PubMed
高影响因子
Journal of Food Measurement and Characterization
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2020
·Vol. 14
·期 5
·2549-2558
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IF 1.648
Hu Jun,Liu Yande,He Yong,Sun Xudong,Li Bin
Food safety has been an issue of common concern in today’s society. Excessive benzoic acid (BA) additives in wheat flour would pose a huge threat to human health. In this paper, terahertz (THz) time-domain spectroscopy was used to detect BA quantitatively, which is a preservative additive in flour. Samples were prepared by grinding, drying, weighing, mixing and performing successively according to the designed concentration gradient (concentration 0.04%, 0.08%, 0.1%, 0.2%, 0.4%, 0.5%, 1%, 1.5%, …, 10%), with the BA concentration ranging from 0.040% to 19.99%. Then the THz Spectra of wheat flour samples with different concentrations of BA were collected. After preprocessing the original terahertz spectra with multiple scattering correction and other methods, Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), Principal Component Analysis (PCA) and Uninformative Variable Elimination (UVE) were used to select effective spectral bands, and then Partial Least Squares (PLS) and Least Square Support Vector Machine (LS-SVM) models were established respectively. It turned out that the absorption peak of BA was at 1.94 THz of terahertz absorption coefficient, which increased with the rise of BA concentration. The results also indicated that better modeling results could be obtained by using CARS method for variable selection. And the established CARS-PCA-LS-SVM model had the best prediction result by combining PCA with CARS, with the correlation coefficient of the prediction (R p ) of 0.9956 and Root Mean Square Error of Prediction (RMSEP) of 0.64%. Hence, it can be concluded that terahertz technology combined with LS-SVM model can well achieve the quantitative detection of BA concentration in flour. Although the model is not universal since samples size is not large enough, the research method provides a basis for the detection of BA additives in flour.
研究领域:
食品科学
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分析化学
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光谱学
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食品安全
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计算生物学
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分子检测技术
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DOI
高影响因子
MEASUREMENT
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2025
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IF 5.6
Xiaodong Mao,Shimin Yang,Zhikai Huang,Jun Hu
Excessive thiabendazole residues in agricultural products pose a safety risk. This study proposes a novel approach for trace-level, non-destructive detection of thiabendazole using terahertz metamaterial enhancement and intelligent optimization modeling. In this study, the “L”-shaped THz metamaterial sensor was fabricated to collect transmission spectra (0.5–3.5 THz). A support vector regression (SVR) model, optimized by the weighted mean of vectors (INFO) algorithm for global hyperparameter optimization, was constructed. Dimensionality reduction was performed using Iteratively Retaining Informative Variables (IRIV), Variable Importance Projection (VIP), and Variable Iterative Spatial Shrinkage Approach (VISSA). The results show that the INFO-SVR model outperformed traditional optimization methods. The VISSA-INFO-SVR model showed the best performance, achieving the Limit of Detection ( LOD ) of 0.697 μg/ml. F-tests and t-tests (p > 0.05) further confirmed the statistical reliability of the prediction results. This study integrates the INFO algorithm with terahertz metamaterial spectral modeling, combined with feature extraction techniques, to achieve highly sensitive, non-destructive detection of thiabendazole residues.
研究领域:
分析化学
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食品科学
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农业化学
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生物传感
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药物残留检测
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光谱学
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计算建模
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产品关联
DOI
高影响因子
FOOD CONTROL
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2023
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IF 6
Shang Wang,Silin Huang,Yuying Han,Jian Wu,Tianhui Jiao,Jie Wei,Xiaomei Chen,Qingmin Chen,Quansheng Chen
Total volatile basic nitrogen (TVB-N) content is one of the critical safety evaluation indexes in seafood . This study investigated the colorimetric sensing array (CSA) technique with two data collection blocks, imaging and visible near-infrared spectroscopy, for non-destructive prediction of TVB-N content in Fujian oysters. Three data fusion strategies coupled with variable selection approaches including variable combination population analysis (VCPA), ant colony optimization, and competitive adaptive reweighted sampling were applied to establish the partial least squares (PLS) model for predicting the TVB-N content of oysters. Better prediction performance was obtained using data fusion of two information blocks than using individual block data. The results showed that the high-level-VCPA strategy showed the best performance with the correlation coefficient of the prediction set (R p ) at 0.9128. This work demonstrates that the use of CSA has the potential for non-destructive monitoring of TVB-N content in Fujian oysters.
研究领域:
食品科学
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光谱学
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传感器技术
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水产养殖
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产品关联
DOI
高影响因子
FOOD CHEMISTRY
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2020
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IF 6.306
Tian-Qin Peng,Xiao-Li Yin,Hui-Wen Gu,Weiqing Sun,Baomiao Ding,Xian-Chun Hu,Li-An Ma,Shu-Dong Wei,Zhi Liu,Shi-Yi Ye
Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.
研究领域:
分析化学
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食品科学
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化学计量学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）