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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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2
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He W, Ye Z, Li M, Yan Y, Lu W, Xing G. Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN. FRONTIERS IN PLANT SCIENCE 2023; 14:1181322. [PMID: 37560031 PMCID: PMC10407792 DOI: 10.3389/fpls.2023.1181322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.
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Affiliation(s)
- Wei He
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Zhihao Ye
- Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Mingshuang Li
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Yulu Yan
- Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Wei Lu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Guangnan Xing
- Soybean Research Institute, Ministry of Agriculture and Rural Affairs (MARA) National Center for Soybean Improvement, Ministry of Agriculture and Rural Affairs (MARA) Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
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3
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Johnson JB, Walsh KB, Naiker M, Ameer K. The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review. Molecules 2023; 28:molecules28073215. [PMID: 37049978 PMCID: PMC10096661 DOI: 10.3390/molecules28073215] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/29/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.
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Affiliation(s)
- Joel B Johnson
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kerry B Walsh
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Mani Naiker
- School of Health, Medical & Applied Science, Central Queensland University, North Rockhampton, QLD 4701, Australia
| | - Kashif Ameer
- Institute of Food Science and Nutrition, University of Sargodha, Sargodha 40100, Pakistan
- Department of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju 61186, Republic of Korea
- School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea
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Wang Y, Ren Z, Chen Y, Lu C, Deng WW, Zhang Z, Ning J. Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation. Food Chem 2023; 401:134090. [DOI: 10.1016/j.foodchem.2022.134090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/13/2022] [Accepted: 08/29/2022] [Indexed: 01/30/2023]
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5
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Junges CH, Guerra CC, Canedo-Reis NAP, Gomes AA, Ferrão MF. Discrimination of whole grape juice using fluorescence spectroscopy data with linear discriminant analysis coupled to genetic and ant colony optimisation algorithms. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:187-195. [PMID: 36514991 DOI: 10.1039/d2ay01636b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In this study, a new approach was developed for classifying grape juices produced in Brazil using unfolded excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics, with respect to the agricultural production system, namely the conventional or organic agricultural one. Linear discriminant analysis (LDA) coupled to ant colony optimisation (ACO) and the genetic algorithm (GA) were used to select a more effective subset of variables to discriminate grape juice samples. The best results demonstrated highly efficient classification of grape juice samples according to a conventional or organic production process with an accuracy rate of up to 97% for the models and 94% in the prediction of these classes for samples external to the model. The models showed high selectivity and sensitivity with a rate of up to 100% for the training and test datasets, in addition to determining the most significant variables that explain the separation of classes. The proposed method proves to be viable, as it is fast and requires minimal sample preparation, allowing quality control in the food industry.
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Affiliation(s)
- Carlos H Junges
- Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil.
| | - Celito C Guerra
- Laboratório de Cromatografia e Espectrometria de Massas (LACEM), Unidade Uva e Vinho, Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), Rua Livramento, 515, Bento Gonçalves, Rio Grande do Sul, Brazil
| | - Natalia A P Canedo-Reis
- Programa de Pós-Graduação em Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal do Rio Grande do Sul, Avenida Ipiranga, 2752, Porto Alegre, Rio Grande do Sul, CEP 90610-000, Brazil
| | - Adriano A Gomes
- Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil.
| | - Marco F Ferrão
- Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil.
- Instituto Nacional de Ciência e Tecnologia-Bioanalítica (INCT-Bioanalítica), Cidade Universitária Zeferino Vaz, s/n, Campinas, São Paulo (SP), CEP 13083-970, Brazil
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6
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Manzoor MF, Hussain A, Naumovski N, Ranjha MMAN, Ahmad N, Karrar E, Xu B, Ibrahim SA. A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products. Front Nutr 2022; 9:901342. [PMID: 35928834 PMCID: PMC9343702 DOI: 10.3389/fnut.2022.901342] [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: 03/21/2022] [Accepted: 05/31/2022] [Indexed: 01/10/2023] Open
Abstract
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
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Affiliation(s)
| | - Abid Hussain
- Department of Agriculture and Food Technology, Faculty of Life Science, Karakoram International University, Gilgit-Baltistan, Pakistan
| | - Nenad Naumovski
- School of Rehabilitation and Exercise Science, Faculty of Health, University of Canberra, Canberra, ACT, Australia
- Functional Foods and Nutrition Research (FFNR) Laboratory, University of Canberra, Bruce, ACT, Australia
| | | | - Nazir Ahmad
- Department of Nutritional Sciences, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Emad Karrar
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- *Correspondence: Bin Xu
| | - Salam A. Ibrahim
- Food Microbiology and Biotechnology Laboratory, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
- Salam A. Ibrahim
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Determination of aflatoxin B1 value in corn based on Fourier transform near-infrared spectroscopy: Comparison of optimization effect of characteristic wavelengths. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113657] [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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8
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Beć KB, Grabska J, Huck CW. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022; 11:foods11101465. [PMID: 35627034 PMCID: PMC9140213 DOI: 10.3390/foods11101465] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 01/27/2023] Open
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.
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9
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Ouyang Q, Liu L, Zareef M, Wang L, Chen Q. Application of portable visible and near-infrared spectroscopy for rapid detection of cooking loss rate in pork: Comparing spectra from frozen and thawed pork. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Li H, Haruna SA, Wang Y, Mehedi Hassan M, Geng W, Wu X, Zuo M, Ouyang Q, Chen Q. Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration. Food Chem 2022; 372:131146. [PMID: 34627091 DOI: 10.1016/j.foodchem.2021.131146] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/13/2021] [Accepted: 09/13/2021] [Indexed: 01/07/2023]
Abstract
Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.
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Affiliation(s)
- Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yin Wang
- Zhenjiang Agricultural Product Quality Inspection and Testing Center, PR China
| | - Md Mehedi Hassan
- 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
| | - Xiangyang Wu
- School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Min Zuo
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, 100048 Beijing, PR China; School of Computer and Information Engineering, Beijing Technology and Business University, 100048, PR China
| | - Qin Ouyang
- 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.
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11
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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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12
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Zhu C, Jiang H, Chen Q. Rapid determination of process parameters during simultaneous saccharification and fermentation (SSF) of cassava based on molecular spectral fusion (MSF) features. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 264:120245. [PMID: 34364037 DOI: 10.1016/j.saa.2021.120245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/27/2021] [Accepted: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Simultaneous saccharification and fermentation (SSF) of cassava is one of the key steps in the production of fuel ethanol. In order to improve the monitoring efficiency of the ethanol production process and the product yield, this study puts forward a new idea for monitoring of the cassava SSF process based on the molecular spectroscopy fusion (MSF) technique. Savisky-Golay (SG) combined with standard normal variable (SNV) was used to preprocess the obtained Raman spectra and near-infrared (NIR) spectra. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic wavelengths of the preprocessed Raman spectra and the NIR spectra, and the optimized features were fused in the feature layer. The support vector machine (SVM) model of the process parameters during the cassava SSF based on the MSF features was established. The experimental results showed that compared with the best CARS-SVM model based on the single-molecule spectral features, the performance of the best CARS-SVM model based on fusion features has been significantly improved. For detection of the glucose content, the RMSEP, RP2 and RPD of the best CARS-SVM model were 5.398, 0.957 and 4.922, respectively. For detection of the ethanol content, the RMSEP, RP2 and RPD of the best CARS-SVM model were 4.394, 0.977 and 6.758, respectively. The obtained results reveal that the combination of MSF technique and appropriate chemometric methods can achieve high-precision quantitative detection of the process parameters during the cassava SSF. This study can provide technical basis and experimental reference for the development of portable spectrometer equipment for process monitoring of the cassava SSF.
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Affiliation(s)
- Chengyun Zhu
- School of Physics and Electronic Engineering, Yancheng Teachers University, Yancheng 224007, PR China
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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13
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Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.08.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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14
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Wang Y, Li L, Liu Y, Cui Q, Ning J, Zhang Z. Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110599] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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15
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Wang Y, Liu Y, Chen Y, Cui Q, Li L, Ning J, Zhang Z. Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111737] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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16
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Ong P, Chen S, Tsai CY, Chuang YK. Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 255:119657. [PMID: 33744842 DOI: 10.1016/j.saa.2021.119657] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 02/16/2021] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.
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Affiliation(s)
- Pauline Ong
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
| | - Suming Chen
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Chao-Yin Tsai
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yung-Kun Chuang
- Master Program in Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; School of Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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17
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Monitoring the withering condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS). J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110534] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Jiang H, He Y, Chen Q. Determination of acid value during edible oil storage using a portable NIR spectroscopy system combined with variable selection algorithms based on an MPA-based strategy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3328-3335. [PMID: 33222172 DOI: 10.1002/jsfa.10962] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/12/2020] [Accepted: 11/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near-infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA-based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage. RESULTS The results revealed that, compared with the full-spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g-1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively. CONCLUSION The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 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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Jin G, Wang YJ, Li M, Li T, Huang WJ, Li L, Deng WW, Ning J. Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system. Food Chem 2021; 358:129815. [PMID: 33915424 DOI: 10.1016/j.foodchem.2021.129815] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wen-Jing Huang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wei-Wei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China.
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21
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Li L, Wang Y, Jin S, Li M, Chen Q, Ning J, Zhang Z. Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 246:118991. [PMID: 33068895 DOI: 10.1016/j.saa.2020.118991] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/18/2020] [Accepted: 09/21/2020] [Indexed: 06/11/2023]
Abstract
Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Jin
- 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
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, 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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Ren G, Ning J, Zhang Z. Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118918. [PMID: 32942112 DOI: 10.1016/j.saa.2020.118918] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 05/05/2023]
Abstract
The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.
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Affiliation(s)
- Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China.
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Jiang H, Liu T, Chen Q. Dynamic monitoring of fatty acid value in rice storage based on a portable near-infrared spectroscopy system. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 240:118620. [PMID: 32599483 DOI: 10.1016/j.saa.2020.118620] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 06/13/2020] [Indexed: 06/11/2023]
Abstract
The fatty acid value of rice is one of the important indexes to reflect its freshness. A portable near-infrared spectroscopy (NIRS) system was designed and assembled to dynamically monitor fatty acid values in rice storage in this study. First, the near-infrared (NIR) spectra of rice samples in different storage periods were obtained using the portable NIRS system. Then, a weighted multiplicative scatter correction with variable selection (WMSCVS) algorithm was applied to the original spectra for scattering correction, and to compress variable space for achieving characteristic wavelengths. Finally, a partial least square (PLS) regression model was established using the characteristic wavelengths to realize the rapid monitoring of fatty acid values in rice storage. The results showed that the performance of the optimal PLS model based on the features by the WMSCVS algorithm was significantly better than those of the optimal PLS models based on SNV and MSC pre-processing spectra, with the determination coefficient (RP2) of 0.9615 and the root mean square error of prediction (RMSEP) of 0.3626 in the predictive process. The overall results demonstrate that it is feasible to use the portable NIRS system developed by our team to quickly monitor the fatty acid values in rice storage.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Tong Liu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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24
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Lin X, Sun DW. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.06.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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He Y, Jiang H, Chen Q. High-precision identification of the actual storage periods of edible oil by FT-NIR spectroscopy combined with chemometric methods. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:3722-3728. [PMID: 32729876 DOI: 10.1039/d0ay00779j] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The actual storage period of edible oil is one of the important indicators of edible oil quality. A high-precision identification method based on the near-infrared (NIR) spectroscopy technique for the actual storage period of edible oil is proposed in this study. Firstly, a Fourier transform NIR (FT-NIR) spectrometer was used to collect NIR spectra of edible oil samples in different storage periods, and the obtained spectra were pretreated by standard normal transformation (SNV). Then, the characteristics of the pretreated spectra were analyzed by principal component analysis (PCA), and the spatial distribution of edible oil samples in different storage periods was visually presented using a PCA score plot. Finally, three pattern recognition methods, which were K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were compared to establish a qualitative identification model of edible oil in different storage periods. The results showed that the recognition performance of the SVM model was significantly superior to that of the KNN and RF models, especially in terms of generalization performance, and the SVM model had a recognition rate of 100% when predicting independent samples in the prediction set. It is suggested that FT-NIR spectroscopy combined with appropriate chemometric methods is feasible to realize fast and high-precision identification of actual storage periods of edible oil and provided an effective analysis tool for edible oil storage quality detection.
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Affiliation(s)
- Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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26
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Jin G, Wang Y, Li L, Shen S, Deng WW, Zhang Z, Ning J. Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109216] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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27
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Huang J, Ren G, Sun Y, Jin S, Li L, Wang Y, Ning J, Zhang Z. Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics. Food Sci Nutr 2020; 8:2015-2024. [PMID: 32328268 PMCID: PMC7174226 DOI: 10.1002/fsn3.1489] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/11/2020] [Accepted: 02/04/2020] [Indexed: 01/24/2023] Open
Abstract
The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.
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Affiliation(s)
- Jing Huang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Guangxin Ren
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Yemei Sun
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
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28
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Antioxidant Activity of Blueberry ( Vaccinium spp.) Cultivar Leaves: Differences Across the Vegetative Stage and the Application of Near Infrared Spectroscopy. Molecules 2019; 24:molecules24213900. [PMID: 31671911 PMCID: PMC6864474 DOI: 10.3390/molecules24213900] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 10/23/2019] [Accepted: 10/28/2019] [Indexed: 12/04/2022] Open
Abstract
Blueberries production has increased in the last few years boosted somehow by the World Health Organization (WHO) recommendations for a healthier nutrition and their recognized potential to treat several diseases. The production increase lead to high amounts of discarded leaves that could be very valuable. In this context, the antioxidant activity of Vaccinium spp. leaves, by means of the total phenolic (TPC) and flavonoid (TFC) content and total antioxidant capacity (TAC) was determined. Adult leaves of twenty-seven Vaccinium cultivars collected in three geographic regions and three seasons of the year were included. The antioxidant activity was additionally estimated with near infrared (NIR) spectroscopy and data transferability across the regions and seasons was evaluated. The TPC, TFC and TAC ranged from 39.6–272.8 mg gallic acid, 41.2–269.1 mg catechin and 22.6–124.8 mM Trolox per g of dry leaf, respectively. Globally through the seasons, the higher values of the three parameters were obtained in December. Regarding the geographic region, region A provided the cultivars with the higher antioxidant content. Titan was the cultivar with higher TPC and TAC and Misty the one with the higher TFC. NIR spectroscopy combined with the partial least squares analysis was able to successfully predict the antioxidant activity with coefficients of determination and range error ratios ranging from 0.84–0.99 and 11.2–26.8. Despite some identified limitations on data transferability, NIR spectroscopy proved to be a reliable, low cost and quick method to predict the antioxidant activity of the considered cultivar leaves.
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