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Yang Y, Huang J, Feng S, Cao X, Tong H, Su L, Zhang X, Wu M. Near-infrared spectroscopy for the quality control of Sarassum fusiforme: Prediction of antioxidant capability of Sarassum fusiforme at different growth stages. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124694. [PMID: 38914030 DOI: 10.1016/j.saa.2024.124694] [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: 11/30/2023] [Revised: 04/12/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024]
Abstract
The healthy benefits of seaweed have increased its market demand in recent times. Quality control is crucial for seaweed to ensure the customers' interest and the sustainable development of seaweed farming industry. This study developed a quality control method for seaweed Sargassum fusiforme, rapid and simple, using near-infrared spectroscopy (NIR) and chemometrics for the prediction of antioxidant capacity of S. fusiforme from different growth stages, S. fusiforme was distinguished according to growth stage by partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The antioxidant properties including 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) were quantified using competitive adaptive reweighted sampling (CARS)-PLS model. Based on the spectra data preprocessed by multiplicative scatter and standard normal variate methods, the PSO-SVM models can accurately identify the growth stage of all S. fusiforme samples. The CARS-PLS models exhibited good performance in predicting the antioxidant capacity of S. fusiforme, with coefficient of determination (RP2) and root mean square error (RMSEP) values in the independent prediction sets reaching 0.9778 and 0.4018 % for ABTS, 0.9414 and 2.0795 % for DPPH, and 0.9763 and 2.4386 μmol L-1 for FRAP, respectively. The quality and market price of S. fusiforme should increase in the order of maturation < growth < seedling regarding the antioxidant property. The overall results indicated that the NIR spectroscopy accompanied by chemometrics can assist for the quality control of S. fusiforme in a more rapid and simple manner. This study also provided a customer-oriented concept of seaweed quality grading based on deep insight into the antioxidant capability of S. fusiforme at different growth stages, which is highly valuable for precise quality control and standardization of seaweed market.
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Affiliation(s)
- Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
| | - Jing Huang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Shenshurun Feng
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Xiaoqing Cao
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Haibin Tong
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Laijin Su
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Xu Zhang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Mingjiang Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
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Guan Y, Wang S, Lei G, Hu Z, Chen H, Gu H, Yin X, She Y, Long W, Fu H. A colorimetric sensor based on 4-MPBA Au@AgNPs for accurately identification of EnshiYulu tea grade. Food Chem 2024; 451:139442. [PMID: 38688099 DOI: 10.1016/j.foodchem.2024.139442] [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: 01/30/2024] [Revised: 03/07/2024] [Accepted: 04/20/2024] [Indexed: 05/02/2024]
Abstract
Enshi Yulu green tea (ESYL) is the most representative traditional steamed green tea in Enshi, Hubei. Different ESYL grades exhibit distinct flavors, tastes, and prices. In this study, a visual sensor based on 4-MPBA Au@AgNPs was developed for the rapid and accurate identification of ESYL grades. The recognition mechanism involved the binding of 4-MPBA Au@AgNPs with polyphenolic compounds in ESYL to form borate esters and the conversion of Ag+ to Ag0, with the generated Ag0 depositing on the surface of 4-MPBA Au@AgNPs. The results showed that the sensor can amplify the color differences of different grades of ESYL. The visual results were also validated by the partial least squares discriminant analysis model, demonstrating an enhancement in recognition accuracy from 68.2 % to 95.5 % compared to the original extraction solution. The colorimetric sensor developed in this study is expected to provide a new approach for traceability research of other foods.
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Affiliation(s)
- Yuting Guan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Siyu Wang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Guanghua Lei
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Zikang Hu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Huiwen Gu
- College of Life Sciences, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434025, China
| | - Xiaoli Yin
- College of Life Sciences, College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434025, China
| | - Yuanbin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China.
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China.
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Cao X, Huang J, Chen J, Niu Y, Wei S, Tong H, Wu M, Yang Y. Rapid Measurement of Antioxidant Properties of Dendrobium officinale Using Near-Infrared Spectroscopy and Chemometrics. Foods 2024; 13:1769. [PMID: 38890997 PMCID: PMC11171845 DOI: 10.3390/foods13111769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/20/2024] Open
Abstract
Dendrobium officinale (D. officinale), often used as a dual-use plant with herbal medicine and food applications, has attracted considerable attention for health-benefiting components and wide economic value. The antioxidant ability of D. officinale is of great significance to ensure its health care value and safeguard consumers' interests. However, the common analytical methods for evaluating the antioxidant ability of D. officinale are time-consuming, laborious, and costly. In this study, near-infrared (NIR) spectroscopy and chemometrics were employed to establish a rapid and accurate method for the determination of 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) in D. officinale. The quantitative models were developed based on the partial least squares (PLS) algorithm. Two wavelength selection methods, namely the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) method, were used for model optimization. The CARS-PLS models exhibited superior predictive performance compared to other PLS models. The root mean square errors of cross-validation (RMSECVs) for ABTS, FRAP, and DPPH were 0.44%, 2.64 μmol/L, and 2.06%, respectively. The results demonstrated the potential application of NIR spectroscopy combined with the CARS-PLS model for the rapid prediction of antioxidant activity in D. officinale. This method can serve as an alternative to conventional analytical methods for efficiently quantifying the antioxidant properties in D. officinale.
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Affiliation(s)
| | | | | | | | | | | | | | - Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; (X.C.); (J.H.); (J.C.); (Y.N.); (S.W.); (H.T.); (M.W.)
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Liu Y, Pan K, Liu Z, Dai Y, Duan X, Wang M, Shen Q. Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:3362. [PMID: 38894153 PMCID: PMC11174505 DOI: 10.3390/s24113362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.
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Affiliation(s)
| | | | | | | | | | | | - Qiang Shen
- Tea Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550025, China; (Y.L.); (K.P.); (Z.L.); (Y.D.); (X.D.); (M.W.)
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Zhu R, Wu X, Wu B, Gao J. High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis. Curr Res Food Sci 2024; 8:100766. [PMID: 38770517 PMCID: PMC11103371 DOI: 10.1016/j.crfs.2024.100766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/18/2024] [Accepted: 05/07/2024] [Indexed: 05/22/2024] Open
Abstract
Peanut kernels, known for their high nutritional value and palatability, are classified as nut food. In this study, peanut kernel samples from six distinct cities in Shandong Province, China, were examined to categorize and trace their origins. Near-infrared (NIR) spectra of samples were captured using a portable NIR-M-R2 spectrometer. After the application of Savitzky-Golay (SG) filtering, the classification was attempted using principal component analysis (PCA) plus linear discrimination analysis (LDA). Additionally, maximum uncertainty linear discriminant analysis (MLDA) was applied for comparison. A specific number of eigenvectors could respectively maximize the classification accuracies, 81.48% for PCA + LDA and 76.54% for MLDA. In order to further improve the classification accuracies, Adaboost-MLDA was proposed to develop a stronger classifier. This method, after 18 iterations, achieved remarkable effects, achieving a high accuracy of 95.06%. In a similar vein, the enhancement with preprocessing techniques multiplicative scatter correction (MSC) + SG and standard normal variate (SNV) + SG raised accuracies to 98.77% and 97.53%, respectively. The results of classifying first-order and second-order derivative spectra using Adaboost-MLDA were also described, achieving accuracies near 100%. The experiment demonstrates that integrating Adaboost with NIR spectroscopy offers a highly accurate method for peanut kernel classification, promising for practical applications in food quality control.
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Affiliation(s)
- Rui Zhu
- Mengxi Honors College, Jiangsu University, Zhenjiang, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
- High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, China
| | - Jiaxing Gao
- School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, China
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Zhang J, Wu X, He C, Wu B, Zhang S, Sun J. Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties. Foods 2024; 13:1439. [PMID: 38790739 PMCID: PMC11119828 DOI: 10.3390/foods13101439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.
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Affiliation(s)
- Jiawei Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shuyu Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
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Wu B, Tang W, Zhou J, Jia H, Shen H, Qi Z. Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades. Heliyon 2024; 10:e27732. [PMID: 38486786 PMCID: PMC10938135 DOI: 10.1016/j.heliyon.2024.e27732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 12/10/2023] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Mee tea, one of the major types of green tea in China, is often used for export because of its elegant appearance, high fragrance and strong taste. However, the quality of tea differs greatly due to the difference in raw material selection and production technology level. In order to accurately and quickly differentiate different grades of Mee tea, fuzzy fast pseudoinverse linear discriminant analysis (FFPLDA) was proposed based on fast pseudoinverse linear discriminant analysis (FPLDA) for extracting discriminant information from near-infrared (NIR) spectra. Firstly, NIR spectra of Mee tea samples were acquired, and then they were preprocessed by multiplicative scatter correlation (MSC). Secondly, the compression of data was achieved by principal component analysis (PCA). Thirdly, linear discriminant analysis (LDA), FPLDA, FFPLDA and fuzzy Foley-Sammon transformation (FFST) were respectively performed to retrieve discriminant information from NIR data. Finally, the K-nearest neighbor (KNN) was utilized to classify Mee tea grades. In this study, experimental results showed that the accuracy of FFPLDA was higher than that of LDA, FFST and FPLDA. Therefore, NIR spectroscopy coupled with FFPLDA and KNN has a good effect in discrimination of Mee tea grades and also a great application potential.
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Affiliation(s)
- Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China
| | - Wenbo Tang
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Jin Zhou
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Hongwen Jia
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China
| | - Hualei Shen
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Zuxuan Qi
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
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Yin L, Jayan H, Cai J, El-Seedi HR, Guo Z, Zou X. Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics. Foods 2023; 12:2968. [PMID: 37569237 PMCID: PMC10419230 DOI: 10.3390/foods12152968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/28/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.
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Affiliation(s)
- Limei Yin
- Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Heera Jayan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Jianrong Cai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
| | - Hesham R. El-Seedi
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, Biology Medical Center, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden;
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (H.J.); (J.C.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China
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Li L, Sheng X, Zan J, Yuan H, Zong X, Jiang Y. Monitoring the dynamic change of catechins in black tea drying by using near-infrared spectroscopy and chemometrics. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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10
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Zhao S, Adade SYSS, Wang Z, Wu J, Jiao T, Li H, Chen Q. On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods. Food Chem 2023; 423:136208. [PMID: 37163914 DOI: 10.1016/j.foodchem.2023.136208] [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: 12/17/2022] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 05/12/2023]
Abstract
Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.
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Affiliation(s)
- Songguang Zhao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Zhen Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jizhong Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Tianhui Jiao
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - 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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Ye Y, Yan W, Peng L, Zhou J, He J, Zhang N, Cheng S, Cai J. Insights into the key quality components in Se-Enriched green tea and their relationship with Selenium. Food Res Int 2023; 165:112460. [PMID: 36869476 DOI: 10.1016/j.foodres.2023.112460] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/01/2023] [Accepted: 01/02/2023] [Indexed: 01/07/2023]
Abstract
Selenium-enriched green tea (Se-GT) is of increasing interest because of its health benefits, but its quality components obtained limited research. In this study, Enshi Se-enriched green tea (ESST, high-Se green tea), Pingli Se-enriched green tea (PLST, low-Se green tea), and Ziyang green tea (ZYGT, common green tea) were subjected to sensory evaluation, chemical analysis, and aroma profiling. Chemical profiles in Se-GT were consistent with the taste attributes of the sensory analysis. 9 volatiles were identified as key odorants of Se-GT based on multivariate analysis. Correlations between Se and quality components were further assessed and highly Se-related compounds contents in these three tea samples were compared. The results showed that most amino acids and non-gallated catechins were highly negatively correlated with Se, while gallated catechins exhibited strong positive correlation with Se. And there were strong and significant associations between the key aroma compounds and Se. Moreover, 11 differential markers were found between Se-GTs and common green tea, including catechin, serine, glycine, threonine, l-theanine, alanine, valine, isoleucine, leucine, histidine, and lysine. These findings provide great potential for quality evaluation of Se-GT.
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Affiliation(s)
- Yuanyuan Ye
- National R&D Center for Se-rich Agricultural Products Processing, Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China; Key Laboratory for Deep Processing of Major Grain and Oil, Ministry of Education, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan Polytechnic University, Wuhan 430023, China
| | - Wei Yan
- Hubei Key Laboratory of Nutritional Quality and Safety of Agro-products, Wuhan 430064, China
| | - Lijun Peng
- Hubei Key Laboratory of Nutritional Quality and Safety of Agro-products, Wuhan 430064, China
| | - Jiaojiao Zhou
- National R&D Center for Se-rich Agricultural Products Processing, Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
| | - Jiangling He
- National R&D Center for Se-rich Agricultural Products Processing, Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
| | - Na Zhang
- National R&D Center for Se-rich Agricultural Products Processing, Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
| | - Shuiyuan Cheng
- National R&D Center for Se-rich Agricultural Products Processing, Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China
| | - Jie Cai
- National R&D Center for Se-rich Agricultural Products Processing, Hubei Engineering Research Center for Deep Processing of Green Se-rich Agricultural Products, School of Modern Industry for Selenium Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China; Key Laboratory for Deep Processing of Major Grain and Oil, Ministry of Education, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan Polytechnic University, Wuhan 430023, China.
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12
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Wu X, Zeng S, Fu H, Wu B, Zhou H, Dai C. Determination of Corn Protein Content Using Near-Infrared Spectroscopy Combined with A-CARS-PLS. Food Chem X 2023; 18:100666. [PMID: 37096170 PMCID: PMC10121631 DOI: 10.1016/j.fochx.2023.100666] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV = 0.0336, R2 c = 0.9951 in the calibration set; RMSEP = 0.0688, R2 p = 0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.
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13
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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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14
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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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15
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An M, Cao C, Wu Z, Luo K. Detection Method for Walnut Shell-Kernel Separation Accuracy Based on Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:8301. [PMID: 36365998 PMCID: PMC9658913 DOI: 10.3390/s22218301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
In this study, Near-infrared (NIR) spectroscopy was adopted for the collection of 1200 spectra of three types of walnut materials after breaking the shells. A detection model of the walnut shell-kernel separation accuracy was established. The preprocessing method of de-trending (DT) was adopted. A classification model based on a support vector machine (SVM) and an extreme learning machine (ELM) was established with the principal component factor as the input variable. The effect of the penalty value (C) and kernel width (g) on the SVM model was discussed. The selection criteria of the number of hidden layer nodes (L) in the ELM model were studied, and a genetic algorithm (GA) was used to optimize the input layer weight (W) and the hidden layer threshold value (B) of the ELM. The results revealed that the classification accuracy of SVM and ELM models for the shell, kernel, and chimera was 97.78% and 97.11%. The proposed method can serve as a reference for the detection of walnut shell-kernel separation accuracy.
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Affiliation(s)
- Minhui An
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Chengmao Cao
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
| | - Zhengmin Wu
- School of Tea and Food Science, Anhui Agricultural University, Hefei 230036, China
| | - Kun Luo
- School of Engineering, Anhui Agricultural University, Hefei 230036, China
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16
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NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea. Foods 2022; 11:foods11192976. [PMID: 36230052 PMCID: PMC9563823 DOI: 10.3390/foods11192976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/29/2022] Open
Abstract
Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (RP = 0.9407, RPD = 3.00), FAA (RP = 0.9110, RPD = 2.21) and TP/FAA (RP = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.
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17
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Yang Y, She X, Cao X, Yang L, Huang J, Zhang X, Su L, Wu M, Tong H, Ji X. Comprehensive evaluation of Dendrobium officinale from different geographical origins using near-infrared spectroscopy and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 277:121249. [PMID: 35483257 DOI: 10.1016/j.saa.2022.121249] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/19/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (Rp2) in the independent test sets were 12.7768 g kg-1 and 0.9586, 1.1346 g kg-1 and 0.9670, 0.3938 g kg-1 and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.
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Affiliation(s)
- Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China; Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Xiangting She
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Xiaoqing Cao
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Liuchang Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Jiamin Huang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Xu Zhang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Laijin Su
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China
| | - Mingjiang Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
| | - Haibin Tong
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou 325035, China.
| | - Xiaoliang Ji
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
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18
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Haruna SA, Li H, Wei W, Geng W, Yao-Say Solomon Adade S, Zareef M, Ivane NMA, Chen Q. Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:2989-2999. [PMID: 35916118 DOI: 10.1039/d2ay00875k] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (Rp = 0.937, RPD = 3.38) and CP (Rp = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
- Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P. M. B 3244, Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | | | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
- College of Food and Biological Engineering, Jimei University, Xiamen, 361021, P. R. China
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19
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Establishment of a rapid detection model for the sensory quality and components of Yuezhou Longjing tea using near-infrared spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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20
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Nagy MM, Wang S, Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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21
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Qi Z, Wu X, Yang Y, Wu B, Fu H. Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis. Foods 2022; 11:foods11050763. [PMID: 35267396 PMCID: PMC8909659 DOI: 10.3390/foods11050763] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 02/06/2023] Open
Abstract
In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects.
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Affiliation(s)
- Zuxuan Qi
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Yangjian Yang
- Research Institute of Zhejiang University-Taizhou, Taizhou 317700, China
- Correspondence:
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China;
| | - Haijun Fu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
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22
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Li L, Wang Y, Cui Q, Liu Y, Ning J, Zhang Z. Qualitative and quantitative quality evaluation of black tea fermentation through noncontact chemical imaging. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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23
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Effectiveness of visible - Near infrared spectroscopy coupled with simulated annealing partial least squares analysis to predict immunoglobulins G, A, and M concentration in bovine colostrum. Food Chem 2022; 371:131189. [PMID: 34600367 DOI: 10.1016/j.foodchem.2021.131189] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 01/19/2023]
Abstract
Visible - near infrared spectroscopy coupled with variable selection using simulated annealing PLS regression was tested to predict immunoglobulin fractions (g/L) of bovine colostrum, namely IgG, IgA and IgM. Immunoglobulins were quantified in 678 samples using the gold standard radial immunodiffusion. Samples were divided in calibration (50%) and validation (50%) datasets. Maximum number of selected variables were limited to 200 and root mean squared error in cross validation (RMSECV) was used as loss function. Performance of the final model developed using the calibration dataset was assessed on the validation dataset. Overall, simulated annealing PLS improved validation RMSECV compared to ordinary PLS regression by 3% to 17%. The present study demonstrated the effectiveness of the calibration model for accurate quantification of IgG, the most abundant immunoglobulin of bovine colostrum (RMSECV = 13.28 g/L; R2 = 0.83). These outcomes could be useful to assess colostrum quality intended for animal and human usage.
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24
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Hou Y, Gao X, Li S, Cai X, Li P, Li W, Li Z. Variable Selection Based on Gray Wolf Optimization Algorithm for the Prediction of Saponin Contents in Xuesaitong Dropping Pills Using NIR Spectroscopy. J Pharm Innov 2022. [DOI: 10.1007/s12247-022-09620-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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25
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Shen S, Hua J, Zhu H, Yang Y, Deng Y, Li J, Yuan H, Wang J, Zhu J, Jiang Y. Rapid and real-time detection of moisture in black tea during withering using micro-near-infrared spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112970] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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26
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Ong P, Tung IC, Chiu CF, Tsai IL, Shih HC, Chen S, Chuang YK. Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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27
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Qiu L, Zhang M, Mujumdar AS, Chang L. Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq "Crimsin Glory" H.T.) petals during infrared drying. Food Chem 2022; 369:130951. [PMID: 34474289 DOI: 10.1016/j.foodchem.2021.130951] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/31/2021] [Accepted: 08/23/2021] [Indexed: 01/31/2023]
Abstract
Infrared drying (IRD) was used for the dehydration process of rose petals for the purpose of improving drying efficiency as well as retaining product quality. A methodology to predict the antioxidant capacities of rose petals which include DPPH, ABTS radical scavenging capacities and ferric-ion reducing antioxidant power (FRAP) values during infrared drying (IRD) was established in this study. Partial least squares regression (PLSR) and back propagation-artificial neural network (BP-ANN) modelling were used to establish the relationships between the near infrared (NIR) spectrum and the antioxidant capacities. Results of model fitting showed that BP-ANN model displayed higher prediction accuracy than PLSR model for determining the DPPH, ABTS radical scavenging capacities and FRAP of rose petals during IRD based on NIR spectral data. The results obtained indicate that NIR spectroscopic parameters combined with multivariate calibration could be used reliably to predict the antioxidant capacities of IR-dried rose petals via appropriate mathematical models.
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Affiliation(s)
- Liqing Qiu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; International Joint Laboratory on Food Safety, Jiangnan University, 214122 Wuxi, Jiangsu, China.
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Ste. Anne decBellevue, Quebec, Canada
| | - Lu Chang
- Shandong Huamei Biology Science & Technology Co, Pingyin, China
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28
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Chen MJ, Yin HL, Liu Y, Wang RR, Jiang LW, Li P. Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:114-124. [PMID: 34913444 DOI: 10.1039/d1ay01634b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There has been no study on using near-infrared spectroscopy (NIRS) to predict the hotness of fresh pepper. This study is aimed at developing a non-destructive and accurate method for determining the hotness of fresh peppers using portable NIRS and the variable selection strategy. Spectra from different locations on samples were obtained non-destructively with a single scan. Quantitative models were established using partial least squares (PLS) with a variable selection method or fusion method. The results showed that near-stalk was the best spectral acquisition location for quantitative analysis. The variable selection strategy allows the selection of targeted characteristic variables and improves the results. A fusion method, namely variable adaptive boosting partial least squares (VABPLS), was selected for optimal prediction of the performance. In the optimized model, the root mean square errors of prediction for the validation set (RMSEPvs) of capsaicin, dihydrocapsaicin and pungency degree were 0.295, 0.143 and 47.770, respectively, while the root mean square errors of prediction for the prediction set (RMSEPps) collected one month later were 0.273, 0.346 and 75.524, respectively.
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Affiliation(s)
- Meng-Juan Chen
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Han-Liang Yin
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Yang Liu
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Rong-Rong Wang
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Li-Wen Jiang
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
| | - Pao Li
- College of Food Science and Technology, Hunan Provincial Key Laboratory of Food Science and Biotechnology, Hunan Agricultural University, Changsha 410125, P. R. China.
- Hunan Agricultural Product Processing Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, P. R. China
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Wang H, Hua J, Yu Q, Li J, Wang J, Deng Y, Yuan H, Jiang Y. Widely targeted metabolomic analysis reveals dynamic changes in non-volatile and volatile metabolites during green tea processing. Food Chem 2021; 363:130131. [PMID: 34120048 DOI: 10.1016/j.foodchem.2021.130131] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 05/07/2021] [Accepted: 05/13/2021] [Indexed: 12/14/2022]
Abstract
Non-volatile metabolites significantly influence the color, taste, and aromatic qualities of green tea. However, the evolutionary trajectories of non-volatile metabolites, and their transformational relationship with volatile metabolites during processing, remain unclear. In this study, ultra-performance liquid chromatography-tandem mass spectrometry and gas chromatography-tandem mass spectrometry were used to analyze a widely targeted metabolome during green tea processing. In total, 527 non-volatile metabolites, covering 11 subclasses, were identified, along with 184 volatile metabolites, covering 8 subclasses. Significant variations in metabolites were observed during processing, especially in the fixation stage, and the conversion intensity of non-volatile metabolites was consistent with the law of "Fixation > Drying > Rolling." A total of 153 non-volatile metabolites were screened out, and amino acids and esters were found to be closely associated with volatile metabolite formation. The results of the present study provide a theoretical basis that could guide green tea processing based on desired quality and components.
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Affiliation(s)
- Huajie Wang
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China
| | - Jinjie Hua
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China
| | - Qinyan Yu
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China
| | - Jia Li
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China
| | - Jinjin Wang
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China
| | - Yuliang Deng
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China
| | - Haibo Yuan
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China.
| | - Yongwen Jiang
- Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South Road, Hangzhou, Zhejiang 310008, PR China.
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Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra. SENSORS 2021; 21:s21093266. [PMID: 34065067 PMCID: PMC8125958 DOI: 10.3390/s21093266] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 11/16/2022]
Abstract
This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.
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Ren G, Liu Y, Ning J, Zhang Z. Assessing black tea quality based on visible–near infrared spectra and kernel-based methods. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103810] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy. Food Chem 2021; 353:129372. [PMID: 33725540 DOI: 10.1016/j.foodchem.2021.129372] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 02/01/2021] [Accepted: 02/12/2021] [Indexed: 12/29/2022]
Abstract
Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (Rp) of Rp > 0.97, Rp > 0.98 and Rp > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.
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Wang Y, Li M, Li L, Ning J, Zhang Z. Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer. Food Chem 2020; 345:128816. [PMID: 33316713 DOI: 10.1016/j.foodchem.2020.128816] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 11/07/2020] [Accepted: 12/02/2020] [Indexed: 01/14/2023]
Abstract
Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.
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Affiliation(s)
- Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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Guo Z, Barimah AO, Guo C, Agyekum AA, Annavaram V, El-Seedi HR, Zou X, Chen Q. Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy SERS off-signal nanosensor for quantitative detection of mercury in black tea. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 242:118747. [PMID: 32717525 DOI: 10.1016/j.saa.2020.118747] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/14/2020] [Indexed: 06/11/2023]
Abstract
Black tea like other food crops is prone to mercury ion (Hg2+) contamination right from cultivation to industrial processing. Due to the dangerous health effects posed even in trace contents, sensitive detection and quantification sensors are required. This study employed the surface-enhanced Raman scattering (SERS) enhancement property of 4-aminothiophenol (4-ATP) as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg2+ in standard solutions and spiked tea samples. Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry (ICP-MS) chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg2+. Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares (ACO-PLS) exhibited the best correlation efficient and minimum errors for Hg2+ standard solutions (Rc = 0.984, Rp = 0.974, RMSEC = 0.157 μg/mL, RMSEP = 0.211 μg/mL) and spiked tea samples (Rc = 0.979, Rp = 0.963, RMSEC = 0.181 μg/g and RMSEP = 0.210 μg/g). The limit of detection of the proposed sensor was 4.12 × 10-7 μg/mL for Hg2+ standard solutions and 2.83 × 10-5 μg/g for Hg2+ spiked tea samples. High stability and reproducibility with relative standard deviation of 1.14% and 0.84% were detected. The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg2+ in tea.
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Affiliation(s)
- Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Alberta Osei Barimah
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chuang Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Akwasi A Agyekum
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | | | - Hesham R El-Seedi
- Division of Pharmacognosy, Department of Medicinal Chemistry, Uppsala University, Box 574, SE-75 123 Uppsala, Sweden; International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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