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Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2018年第5期26卷 275-286页

作者： Arslan, Muhammad Zou Xiaobo Hu Xuetao Tahir, Haroon Elrasheid Shi, Jiyong Khan, Moazzam Rafiq Zareef, Muhammad Jiangsu Univ Sch Food & Biol Engn Zhenjiang Jiangsu Peoples R China Univ Agr Faisalabad Natl Inst Food Sci & Technol Faisalabad Pakistan

Fourier-transform near infrared spectroscopy coupled with chemometric algorithms was applied comparatively for the quantification of chemical compositions in black wolfberry. The compositional parameters, i.e. total flavonoid content, total anthocyanin content, total carotenoid content, total sugar, and total acid were performed for quantification. Model results were evaluated using the correlation coefficients of determination for calibration (R-2) and prediction (r(2)), root-mean-square error of prediction and residual predictive deviation. The findings revealed that the performances of models based on variable selection such as synergy interval-PLS, backward interval-PIS and genetic algorithm-PLS were better than the classical PLS. The performance of the developed models yielded 0.88 <= R-2 <= 0.97, 0.87 <= r(2) <= 0.94 and 1.75 <= RPD <= 4.00. The overall results showed that the FT-NIR spectroscopy in conjunction with chemometric algorithms could be used for the quantification of the chemical composition of black wolfberry samples.

关键词： Near infrared spectroscopy black wolfberry chemometric algorithms variable selection preprocessing

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Near-infrared spectroscopy coupled chemometric algorithms for prediction of antioxidant activity of black goji berries (Lycium ruthenicum Murr.)

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018年第4期12卷 2366-2376页

作者： Arslan, Muhammad Zou Xiaobo Tahir, Haroon Elrasheid Xuetao, Hu Rakha, Allah Basheer, Sajid Hao, Zhao Jiangsu Univ Sch Food & Biol Engn 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China Univ Agr Faisalabad Natl Inst Food Sci & Technol Faisalabad 38000 Pakistan

In this study, near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression and various efficient variable selection algorithms, synergy interval-PLS (Si-PLS), backward interval PLS (Bi-PLS) and genetic algorithm-PLS (GA-PLS) were applied comparatively for the prediction of antioxidant activity in black wolfberry (BW). The eight assays were used for quantification of antioxidant content. The developed models were assessed using correlation coefficients (R-2) of the calibration (Cal.) and prediction (Pre.);root mean square error of prediction, RMSEP;standard Error of Cross-Validation, RMSECV and residual predictive deviation, RPD. The performance of the built model greatly improved by the application of Si-PLS, Bi-PLS and GA-PLS compared with full spectrum PLS. The R-2 values determined for calibration and prediction set ranged from 0.8479 to 0.9696 and 0.8401 to 0.9638, respectively. These findings revealed that NIR spectroscopy combined with chemometric algorithms can be used for quantification of antioxidant activity in BW samples.

关键词： NIR spectroscopy Black wolfberry Antioxidant assays chemometric algorithms Variable selection

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Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution

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FOOD CHEMISTRY 2018年 240卷 231-238页

作者： Kutsanedzie, Felix Y. H. Chen, Quansheng Hassan, Md Mehedi Yang, Mingxiu Sun, Hao Rahman, Md Hafizur Jiangsu Univ Sch Food & Biol Engn Xuefu Rd 301 Zhenjiang 212013 Peoples R China

Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS);synergy interval-PLS (Si-PLS);synergy interval-genetic algorithm-PLS (Si-GAPLS);Ant colony optimization - PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc);root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951 <= Rp <= 0.975;and 3.15 <= RPD <= 4.32. The models' prediction stability improved in the order of PLS < CARS-PLS < ACO-PLS < Si-PLS < Si-GAPLS. FT-NIRS combined with Si-GAPLS may be employed for in-situ and noninvasive quantification of TFC in cocoa beans for quality and safety monitoring.

关键词： chemometric algorithms Prediction Preprocessed spectra Spectral interval Variable selection

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Rapid non-destructive monitoring and quality assessment of the fumigation process of Shanxi aged vinegar based on Vis-NIR hyperspectral imaging combined with multiple chemometric algorithms

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 320卷 124539页

作者： Zhang, Xiaorui Huang, Xingyi Aheto, Joshua Harrington Xu, Foyan Dai, Chunxia Ren, Yi Wang, Li Yu, Shanshan Jiangsu Univ Sch Food & Biol Engn Xuefu Rd 301 Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Elect & Informat Engn Xuefu Rd 301 Zhenjiang 212013 Jiangsu Peoples R China Suzhou Polytech Inst Agr Sch Smart Agr Xiyuan Rd 279 Suzhou 215008 Jiangsu Peoples R China

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV.

关键词： Shanxi aged vinegar Fumigation process Hyperspectral imaging technology Nondestructive detection chemometric algorithms

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Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2021年第4期29卷 191-200页

作者： Bilal, Muhammad Xiaobo, Zou Arslan, Muhmmad Tahir, Haroon Elrasheid Sun, Yue Aadil, Rana Muhammad Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Jiangsu Peoples R China Univ Agr Faisalabad Natl Inst Food Sci & Technol Faisalabad Pakistan

In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples. The compositional parameters, such as DPPH, ABTS, FRAP, TPC, FCA, TFC, and TAC, were quantified using NIR spectroscopy. The developed models were assessed using correlation coefficients of the calibration (R-2) and prediction (r(2));root mean standard error of cross-validation, RMSECV;root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The results of the R-2 and r(2) set varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.

关键词： Antioxidant assays chemometric algorithms NIR spectroscopy peanut and variables selection

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NIR Spectroscopy Coupled chemometric algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)

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INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2019年第3-4期15卷

作者： Arslan, Muhammad Zou Xiaobo Tahir, Haroon Elrasheid Hu Xuetao Rakha, Allah Zareef, Muhammad Seweh, Emmanuel Amomba Basheer, Sajid Jiangsu Univ Sch Food & Biol Engn 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China Univ Agr Faisalabad Natl Inst Food Sci & Technol Faisalabad 38000 Pakistan

In this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms;synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction;root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.

关键词： antioxidant assays chemometric algorithms jujube fruit NIR spectroscopy variable selection

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Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms

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JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019年第11期99卷 5019-5027页

作者： Wang, Jingjing Zareef, Muhammad He, Peihuan Sun, Hao Chen, Quansheng Li, Huanhuan Ouyang, Qin Guo, Zhiming Zhang, Zhengzhu Xu, Delian Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Jiangsu Peoples R China Anhui Agr Univ State Key Lab Tea Plant Biol & Utilizat Hefei Anhui Peoples R China Natl Tea Ind Technol Syst Comprehens Expt Stn Wuxi Wuxi Jiangsu Peoples R China

BACKGROUND The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1(st)D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (R-P), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. (c) 2019 Society of Chemical Industry

关键词： matcha tea grade classification near-infrared spectroscopy chemometric algorithms tea polyphenols amino acids

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Near-Infrared Spectroscopy Coupled chemometric algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds

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SENSORS 2020年第17期20卷 4905.-4905.页

作者： Li, Hongbo Jiang, Dapeng Cao, Jun Zhang, Dongyan Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

Lipid content is an important indicator of the edible and breeding value ofPinus koraiensisseeds. Difference in origin will affect the lipid content of the inner kernel, and neither can be judged by appearance or morphology. Traditional chemical methods are small-scale, time-consuming, labor-intensive, costly, and laboratory-dependent. In this study, near-infrared (NIR) spectroscopy combined with chemometrics was used to identify the origin and lipid content ofP. koraiensisseeds. Principal component analysis (PCA), wavelet transformation (WT), Monte Carlo (MC), and uninformative variable elimination (UVE) methods were used to process spectral data and the prediction models were established with partial least-squares (PLS). Models were evaluated byR2for calibration and prediction sets, root mean standard error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Two dimensions of input data produced a faster and more accurate PLS model. The accuracy of the calibration and prediction sets was 98.75% and 97.50%, respectively. When the Donoho Thresholding wavelet filter 'bior4.4' was selected, the WT-MC-UVE-PLS regression model had the best predictions. TheR2for the calibration and prediction sets was 0.9485 and 0.9369, and the RMSECV and RMSEP were 0.0098 and 0.0390, respectively. NIR technology combined with chemometric algorithms can be used to characterizeP. koraiensisseeds.

关键词： NIR spectroscopy Pinus koraiensisseeds chemometric algorithms preprocessing feature selection

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Flexible SERS sensor using AuNTs-assembled PDMS film coupled chemometric algorithms for rapid detection of chloramphenicol in food

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FOOD CHEMISTRY 2023年第1期418卷 135998页

作者： Li, Huanhuan Geng, Wenhui Zheng, Zihan Haruna, Suleiman A. Chen, Quansheng Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China Jimei Univ Coll Ocean Food & Biol Engn Xiamen 361021 Peoples R China

The misuse of chloramphenicol (CAP) has led to the development of drug-resistant strains that pose significant threats to public health. Here, we propose a universal flexible surface-enhanced Raman spectroscopy (SERS) sensor utilizing gold nanotriangles (AuNTs) and polydimethylsiloxane (PDMS) film for rapid detection of CAP in food samples. Initially, AuNTs@PDMS with unique optical and plasmonic properties were used to collect spectra of CAP. Afterward, four chemometric algorithms were executed and compared. Accordingly, random frog-partial least squares (RF-PLS) exhibited optimum results with correlation coefficient of prediction (Rp = 0.9802) and the lowest root-mean-square error of prediction (RMSEP = 0.348 mu g/mL). Furthermore, the sensor's efficacy to detect CAP in milk samples was confirmed, and the findings were compatible with the conventional HPLC approach (P > 0.05). Therefore, the proposed flexible SERS sensor could effectively be used to monitor milk quality and safety.

关键词： Surface -enhanced Raman scattering Chloramphenicol Flexible PDMS AuNTs chemometric algorithms

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Au@Ag nanoflowers based SERS coupled chemometric algorithms for determination of organochlorine pesticides in milk

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LWT-FOOD SCIENCE AND TECHNOLOGY 2021年 150卷 111978-111978页

作者： Zhu, Afang Xu, Yi Ali, Shujat Ouyang, Qin Chen, Quansheng Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Jiangsu Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China

In this study, SERS coupled with chemometric algorithms like PLS (partial least square), Si-PLS (synergy intervalPLS), GA-PLS (genetic algorithm-PLS) and UVE-PLS (uninformative variable elimination-PLS) were applied for the detection of 2,4-D and imidacloprid in milk. The fabricated gold and silver core-shell nanoflowers (Au@Ag NFs) were used as SERS nanosensor which exhibited strong signal over the concentration range of 1.0 x 10(-3) to 1.0 x 10(2) ng/mL. Performances of the models were evaluated based on the attained correlation coefficients of the prediction (R-p) and calibration (R-c), root mean square error in calibration (RMSECV) and prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The models exhibited improved results in the order of PLS < Si-PLS < GA-PLS < UVE-PLS. The UVE-PLS model showed superior performance, yielded R-c = 0.9801 and R-p = 0.9878 with RPD = 6.32 for 2,4-D, and R-c = 0.9732 and R-p = 0.9726 with RPD = 4.30 for imidacloprid. The strategy could be employed for safety and quality monitoring of milk.

关键词： Organochlorine pesticides (OCPs) Surface-enhanced Raman spectroscopy (SERS) Au@Ag nanoflowers chemometric algorithms Milk

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