FUEL
·
2022
·
IF 8.035
Zhanshi Ni,Haobo Bi,Chunlong Jiang,Hao Sun,Wenliang Zhou,Zhicong Qiu,Qizhao Lin
Faced with increasingly severe environmental and energy issues, the development and utilization of solid waste have become a trend. In this study, a thermogravimetric-Fourier infrared spectrometer-mass spectrometer (TG-FTIR-MS) was used to carry out the co-pyrolysis experiment of coal slime and cellulose. The samples with mass mixing ratios of 0:10, 1:9, 3:7, 5:5, 7:3, 9:1, and 10:0 were pyrolyzed in N 2 atmosphere. The percentages of mass loss during the pyrolysis of coal slime and cellulose were 15.69 and 94.28%, respectively. The addition of cellulose to the coal slime could significantly improve the pyrolysis performance of the sample. It was detected by FTIR that the pyrolysis of the blends mainly formed C=O-containing compounds. MS was used to monitor the ion current intensity of gaseous products of sample pyrolysis online. The average activation energy of the reaction between coal slime and cellulose calculated by the Kissinger-Akahira-Sunose method were 237.59 kJ/mol and 174.06 kJ/mol, respectively. The principal component 1 and principal component 2 dominated the co-pyrolysis process of coal slime and cellulose. The MAE, RMSE, and R 2 of the artificial neural network prediction model for predicting the pyrolysis process of the sample were 0.5249, 0.8244, and 0.9994, respectively. This research provided basic theory and data support for understanding the co-pyrolysis of coal slime and biomass.
研究领域:
能源化学
·
环境科学
·
热化学转化
·
可再生能源
·
固体废物利用
✓ 已验证
DOI
高影响因子
RSC Advances
·
2017
·Vol. 7
·期 73
·46461-46471
·
IF 3.108
Shui Jiang,Jun Wang,Yubing Sun
Chinese pecans (Carya cathayensis) continuously deteriorate during storage because of their high fatty acid contents. In this study, an electronic nose (E-nose) was introduced to characterize Chinese pecans with different storage times. Chemometric methods (principal component analysis (PCA), partial least squares regression (PLSR), and back propagation neural networks (BPNNs)) were employed to analyze E-nose data. For qualitative analysis, PCA could visualize the discrimination between different pecans based on the E-nose data. For quantitative analysis, the results indicated that BPNN models performed better both in predicting storage times and fatty acid contents than the PLSR models. In addition, a multi-target BPNN regression model was built to simultaneously predict the contents of the six main fatty acids, and the results (R2 > 0.95 in calibration sets and R2 > 0.88 in validation sets) were satisfactory. This study provides a potentially viable method for determining the storage times and fatty acid profiles of nut products.
研究领域:
食品科学
·
分析化学
·
农产品贮藏
·
营养化学
·
食品安全
✓ 已验证
DOI
高影响因子
FOOD CHEMISTRY
·
2017
·
IF 4.529
Ting Shi,MengTing Zhu,Yi Chen,XiaoLi Yan,Qian Chen,XiaoLin Wu,Jiangnan Lin,Mingyong Xie
Proton nuclear magnetic resonance ( 1 H NMR) and chemometrics were employed to detect the adulteration of camellia oil (CAO) with 3 different cheap vegetable oils. With the intensity of 15 selected 1 H NMR signals as input variables, principal component analysis (PCA) showed good group clustering results for pure and nonpure CAO, but unsatisfied identification accuracy for the adulterated oil types, indicating relatively small difference among those oils. Whereas these difference could be revealed by orthogonal projection to latent structures discriminant analysis (OPLS-DA), with identification accuracy higher than 90%. Partial least squares (PLS) was further applied for the prediction of adulteration level in CAO. With less than 6 variables screened out by variable importance in the projection (VIP) scores as potential key markers, the developed PLS models showed better accuracy. The prediction results for 10 hold-out samples also confirmed that this method was accurate and fast for the detection of CAO adulteration.
研究领域:
食品科学
·
分析化学
·
食品安全
·
代谢组学
·
油脂化学
✓ 已验证
DOI
高影响因子
Journal of Food Measurement and Characterization
·
2020
·Vol. 14
·期 5
·2549-2558
·
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
·
分析化学
·
光谱学
·
食品安全
·
计算生物学
·
分子检测技术
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）