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Adade SYSS, Lin H, Johnson NAN, Qianqian S, Nunekpeku X, Ahmad W, Kwadzokpui BA, Ekumah JN, Chen Q. Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models. Food Chem 2024; 454:139836. [PMID: 38810447 DOI: 10.1016/j.foodchem.2024.139836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/16/2024] [Accepted: 05/23/2024] [Indexed: 05/31/2024]
Abstract
Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R2 = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.
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Affiliation(s)
- Selorm Yao-Say Solomon Adade
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; Department of Nutrition and Dietetics, Ho Teaching Hospital, P. O. Box MA 374, Ho, Ghana; Centre for Agribusiness Development and Mechanization in Africa (CADMA AgriSolutions), Ho 00233, Ghana
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Nana Adwoa Nkuma Johnson
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; Centre for Agribusiness Development and Mechanization in Africa (CADMA AgriSolutions), Ho 00233, Ghana
| | - Sun Qianqian
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Xorlali Nunekpeku
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Waqas Ahmad
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Bridget Ama Kwadzokpui
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - John-Nelson Ekumah
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Centre for Agribusiness Development and Mechanization in Africa (CADMA AgriSolutions), Ho 00233, Ghana
| | - Quansheng Chen
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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Cui H, Gu F, Qin J, Li Z, Zhang Y, Guo Q, Wang Q. Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach. Foods 2024; 13:1722. [PMID: 38890950 PMCID: PMC11171514 DOI: 10.3390/foods13111722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/21/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.
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Affiliation(s)
- Haofan Cui
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Fengying Gu
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Jingjing Qin
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Zhenyuan Li
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Yu Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Ministry of Agriculture, Beijing 100081, China;
| | - Qin Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
| | - Qiang Wang
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing 100193, China; (H.C.); (F.G.); (J.Q.); (Z.L.); (Q.G.)
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Ren G, Yin L, Wu R, Ning J. Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123740. [PMID: 38109803 DOI: 10.1016/j.saa.2023.123740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 12/20/2023]
Abstract
Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.
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Affiliation(s)
- Guangxin Ren
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China; Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China.
| | - Lingling Yin
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China; Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China
| | - Rui Wu
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan 232038, China; Key Laboratory of Bioresource and Environmental Biotechnology of Anhui Higher Education Institutes, Huainan Normal University, Huainan 232038, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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Johnson NAN, Adade SYSS, Haruna SA, Ekumah JN, Ma Y. Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 307:123623. [PMID: 37989004 DOI: 10.1016/j.saa.2023.123623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/17/2023] [Accepted: 11/04/2023] [Indexed: 11/23/2023]
Abstract
The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.
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Affiliation(s)
- Nana Adwoa Nkuma Johnson
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China
| | - Selorm Yao-Say Solomon Adade
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Dietetics, Ho Teaching Hospital, Ho, Ghana.
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Food Science and Technology, Kano University of Science andTechnology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - John-Nelson Ekumah
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China; Department of Nutrition and Food Science, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 134, Legon, Ghana
| | - Yongkun Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 202013, Jiangsu, China.
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Wang Y, Han M, Xu Y, Wang X, Cheng M, Cui Y, Xiao Z, Qu J. Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:3854-3862. [PMID: 37496451 DOI: 10.1039/d3ay00774j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The quantitative determination of the soluble solid content (SSC) of potatoes using NIR spectroscopy is useful for predicting the internal and external quality of potato products, especially fried products. In this study, the effect of peel on the partial least squares regression (PLSR) quantitative prediction of potato SSC was investigated by transmission and reflection. The results show that the variable sorting for normalization (VSN) pre-processing method improved model accuracy. Additive multiplicative scattering effects and intensity drift interference of the peels were reduced. The model accuracy reached a correlation coefficient of prediction (RP) of 0.85. The selection algorithm using variable combination population analysis and iterative retention of information variables (VCPA-IRIV) demonstrated that peel increases unnecessary information. When the effect of irrelevant variables was reduced, the results reached RP = 0.88 and the root mean square error of prediction (RMSEP) = 0.25 in the transmission mode was close to that of the full-wavelength peeled PLSR model (RP = 0.89 and RMSEP = 0.25). This indicates that the use of the combined algorithm (VSN-VCPA-IRIV) reduces the effect of the peel and enables samples with a peel to still be predicted accurately in the full-wavelength model. It also improves detection efficiency through the extraction of the necessary variables and optimizes the stability and accuracy of the model.
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Affiliation(s)
- Yi Wang
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Minjie Han
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Yingchao Xu
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Xiangyou Wang
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Meng Cheng
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Yingjun Cui
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Zhengwei Xiao
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Junzhe Qu
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
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A Novel Colorimetric Sensor Array Coupled Multivariate Calibration Analysis for Predicting Freshness in Chicken Meat: A Comparison of Linear and Nonlinear Regression Algorithms. Foods 2023; 12:foods12040720. [PMID: 36832795 PMCID: PMC9955736 DOI: 10.3390/foods12040720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric sensor arrays (CSA) in combination with linear and nonlinear regression models. Herein, the TVB-N was determined by steam distillation, and the CSA was fabricated via the use of nine chemically responsive dyes. The corresponding dyes utilized, and the emitted volatile organic compounds (VOCs) were found to be correlated. Afterwards, the regression algorithms were applied, assessed, and compared, with the result that a nonlinear model based on competitive adaptive reweighted sampling coupled with support vector machines (CARS-SVM) achieved the best results. Accordingly, the CARS-SVM model provided improved coefficient values (Rc = 0.98 and Rp = 0.92) based on the figures of merit used, as well as root mean square errors (RMSEC = 3.12 and RMSEP = 6.75) and a ratio of performance deviation (RPD) of 2.25. Thus, this study demonstrated that the CSA paired with a nonlinear algorithm (CARS-SVM) could be employed for fast, noninvasive, and sensitive detection of TVB-N concentration in chicken meat as a major indicator of freshness in meat.
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Haruna SA, Li H, Wei W, Geng W, Luo X, Zareef M, Yao-Say Solomon Adade S, Ivane NMA, Isa A, Chen Q. Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121854. [PMID: 36162210 DOI: 10.1016/j.saa.2022.121854] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/14/2022] [Accepted: 09/03/2022] [Indexed: 06/16/2023]
Abstract
Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaofeng Luo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Adamu Isa
- Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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Rapid detection of thiabendazole in food using SERS coupled with flower-like AgNPs and PSL-based variable selection algorithms. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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