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Ma H, Guo J, Liu G, Xie D, Zhang B, Li X, Zhang Q, Cao Q, Li X, Ma F, Li Y, Wan G, Li Y, Wu D, Ma P, Guo M, Yin J. Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review. Crit Rev Food Sci Nutr 2024:1-23. [PMID: 38523442 DOI: 10.1080/10408398.2024.2329956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.
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Affiliation(s)
- Haiyang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Jiajun Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guishan Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Delang Xie
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Bingbing Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaojun Li
- School of Electronic and Electrical Engineering, Ningxia University, Yinchuan, China
| | - Qian Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Qingqing Cao
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaoxue Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Fang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Yang Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guoling Wan
- College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Yan Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Di Wu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Ping Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Mei Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Junjie Yin
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [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: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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Zhang P, Liu B, Mu X, Xu J, Du B, Wang J, Liu Z, Tong Z. Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms. Molecules 2023; 29:197. [PMID: 38202780 PMCID: PMC10780255 DOI: 10.3390/molecules29010197] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/21/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (P.Z.); (B.L.); (X.M.); (J.X.); (B.D.); (J.W.); (Z.L.)
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Yulia M, Analianasari A, Widodo S, Kusumiyati K, Naito H, Suhandy D. The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis. Foods 2023; 12:4302. [PMID: 38231760 DOI: 10.3390/foods12234302] [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: 10/30/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 01/19/2024] Open
Abstract
Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.
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Affiliation(s)
- Meinilwita Yulia
- Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa, Bandar Lampung 35141, Indonesia
- Spectroscopy Research Group (SRG), Laboratory of Bioprocess and Postharvest Engineering, Department of Agricultural Engineering, The University of Lampung, Bandar Lampung 35145, Indonesia
| | - Analianasari Analianasari
- Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa, Bandar Lampung 35141, Indonesia
| | - Slamet Widodo
- Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Engineering and Technology, IPB University, Dramaga, Bogor 16680, Indonesia
| | - Kusumiyati Kusumiyati
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Hirotaka Naito
- Department of Environmental Science and Technology, Graduate School of Bioresources, Mie University, 1577 Kurima-machiya-cho, Tsu-city 514-8507, Mie, Japan
| | - Diding Suhandy
- Spectroscopy Research Group (SRG), Laboratory of Bioprocess and Postharvest Engineering, Department of Agricultural Engineering, The University of Lampung, Bandar Lampung 35145, Indonesia
- Department of Agricultural Engineering, Faculty of Agriculture, The University of Lampung, Jl. Soemantri Brojonegoro No. 1, Bandar Lampung 35145, Indonesia
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Fitzgerald S, Marple E, Mahadevan-Jansen A. Performance assessment of probe-based Raman spectroscopy systems for biomedical analysis. BIOMEDICAL OPTICS EXPRESS 2023; 14:3597-3609. [PMID: 37497480 PMCID: PMC10368060 DOI: 10.1364/boe.494289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 07/28/2023]
Abstract
We present a methodology for evaluating the performance of probe-based Raman spectroscopy systems for biomedical analysis. This procedure uses a biological standard sample and data analysis approach to circumvent many of the issues related to accurately measuring and comparing the signal quality of Raman spectra between systems. Dairy milk is selected as the biological standard due to its similarity to tissue spectral properties and because its homogeneity eliminates the dependence of probe orientation on the measured spectrum. A spectral dataset is first collected from milk for each system configuration, followed by a model-based correction step to remove photobleaching artifacts and accurately calculate SNR. Results demonstrate that the proposed strategy, unlike current methods, produces an experimental SNR that agrees with the theoretical value. Four preconfigured imaging spectrographs that share similar manufacturer specifications were compared, showing that their capabilities to detect biological Raman spectra widely differ in terms of throughput and stray light rejection. While the methodology is used to compare spectrographs in this case, it can be adapted for other purposes, such as optimizing the design of a custom-built Raman spectrometer, evaluating inter-probe variability, or examining how altering system subcomponents affects signal quality.
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Affiliation(s)
- Sean Fitzgerald
- Vanderbilt Biophotonics Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Eric Marple
- EmVision LCC, 1471 F Road, Loxahatchee, FL 33470, USA
| | - Anita Mahadevan-Jansen
- Vanderbilt Biophotonics Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
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6
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An Easy-to-Use and Cheap Analytical Approach Based on NIR and Chemometrics for Tomato and Sweet Pepper Authentication by Non-volatile Profile. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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7
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Luo S, Yan C, Chen D. Preliminary study on coffee type identification and coffee mixture analysis by light emitting diode induced fluorescence spectroscopy. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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8
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Hu Q, Sellers C, Kwon JSI, Wu HJ. Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification. DIGITAL CHEMICAL ENGINEERING 2022; 3:100020. [PMID: 36874955 PMCID: PMC9983029 DOI: 10.1016/j.dche.2022.100020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.
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Affiliation(s)
- Qiang Hu
- The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA
| | - Chase Sellers
- The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA
| | - Joseph Sang-Il Kwon
- The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA.,Texas A&M Energy Institute, Texas A&M University, College Staticn TX 77845, USA
| | - Hung-Jen Wu
- The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA
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9
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Kolašinac S, Pećinar I, Danojević D, Stevanović ZD. Raman spectroscopy coupled with chemometric modeling approaches for authentication of different paprika varieties at physiological maturity. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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10
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Robert JV, de Gois JS, Rocha RB, Luna AS. Direct solid sample analysis using synchronous fluorescence spectroscopy coupled with chemometric tools for the geographical discrimination of coffee samples. Food Chem 2022; 371:131063. [PMID: 34555703 DOI: 10.1016/j.foodchem.2021.131063] [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: 03/28/2021] [Revised: 08/08/2021] [Accepted: 09/03/2021] [Indexed: 12/13/2022]
Abstract
This work aims the study chemometric methods for the classification of the origin of coffee samples. Samples of finely pulverized coffee grains were analyzed by synchronous molecular fluorescence spectroscopy to carry out the classification. The spectral data of the samples were obtained in triplicate in two offsets: 10 nm (with emission wavelengths from 240 nm to 600 nm) and 40 nm (from 240 nm to 560 nm), all with 1 nm resolution. Different strategies were performed using the spectra obtained with the offsets of 10 nm and 40 nm and fused data at mid-level (10 nm + 40 nm). The performances of linear and nonlinear methods were compared, the best results were obtained from the raw data from the fusion at low-level of the 10 nm and 40 nm offset spectra with the Pareto optimization criterion.
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Affiliation(s)
- Jarbas Verissimo Robert
- Rio de Janeiro State University, Chemical Engineering Graduate Program, Rio de Janeiro 20550-013, RJ, Brazil
| | - Jefferson S de Gois
- Rio de Janeiro State University, Chemical Engineering Graduate Program, Rio de Janeiro 20550-013, RJ, Brazil.
| | - Rodrigo Barros Rocha
- Centro de Pesquisa Agroflorestal de Rondônia, Empresa Brasileira de Pesquisa Agropecuária (Embrapa), Porto Velho, RO 76815-800, Brazil
| | - Aderval S Luna
- Rio de Janeiro State University, Chemical Engineering Graduate Program, Rio de Janeiro 20550-013, RJ, Brazil
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Abstract
This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.
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Manuel MNB, da Silva AC, Lopes GS, Ribeiro LPD. One-class classification of special agroforestry Brazilian coffee using NIR spectrometry and chemometric tools. Food Chem 2021; 366:130480. [PMID: 34284192 DOI: 10.1016/j.foodchem.2021.130480] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 05/11/2021] [Accepted: 06/24/2021] [Indexed: 02/01/2023]
Abstract
The near-infrared spectrometry combined with the one-class classification method was applied as quality control of the agroforestry-grown specialty coffee. A total of 34 samples were analyzed in this study. Spectral data were obtained using a NIR portable and different pre-treatment strategies for baseline correction were evaluated. Unsupervised pattern recognition (PCA and HCA) techniques were performed. The construction of the classification model was carried out using the dd-SIMCA algorithm with 19 samples acquired directly from producers that are recognized for the best quality control of the specialty type coffee. In order to test the model, 15 samples of non-specialty type, obtained in local markets, were evaluated. The classification model with the highest correct classification rate (CCR) scored 100% and 87% in the validation and test groups, respectively. The results demonstrated that the application of this strategy was successful in verifying the authenticity of specialty type agroforestry-grown coffee samples.
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Affiliation(s)
- Monis Neves Baptista Manuel
- Núcleo Avançado de Tecnologias Analíticas (NATA), Universidade da Integração Internacional da Lusofonia Afro-brasileira (Unilab), Brazil
| | - Adenilton Camilo da Silva
- Laboratório de Estudos em Química Aplicada (LEQA), Departamento de Química Analítica e Físico-Química, Universidade Federal do Ceará (UFC), Brazil
| | - Gisele Simone Lopes
- Laboratório de Estudos em Química Aplicada (LEQA), Departamento de Química Analítica e Físico-Química, Universidade Federal do Ceará (UFC), Brazil
| | - Lívia Paulia Dias Ribeiro
- Núcleo Avançado de Tecnologias Analíticas (NATA), Universidade da Integração Internacional da Lusofonia Afro-brasileira (Unilab), Brazil.
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13
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Wang J, Chen Q, Belwal T, Lin X, Luo Z. Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy. Compr Rev Food Sci Food Saf 2021; 20:2476-2507. [DOI: 10.1111/1541-4337.12741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/08/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Jingjing Wang
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang People's Republic of China
| | - Tarun Belwal
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
- Ningbo Research Institute Zhejiang University Ningbo People's Republic of China
- Fuli Institute of Food Science Hangzhou People's Republic of China
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15
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Feng CH, Otani C. Terahertz spectroscopy technology as an innovative technique for food: Current state-of-the-Art research advances. Crit Rev Food Sci Nutr 2020; 61:2523-2543. [PMID: 32584169 DOI: 10.1080/10408398.2020.1779649] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
With the dramatic development of source and detector components, terahertz (THz) spectroscopy technology has recently shown a renaissance in various fields such as medical, material, biosensing and pharmaceutical industry. As a rapid and noninvasive technology, it has been extensively exploited to evaluate food quality and ensure food safety. In this review, the principles and processes of THz spectroscopy are first discussed. The current state-of-the-art applications of THz and imaging technologies focused on foodstuffs are then discussed. The advantages and challenges are also covered. This review offers detailed information for recent efforts dedicated to THz for monitoring the quality and safety of various food commodities and the feasibility of its widespread application. THz technology, as an emerging and unique method, is potentially applied for detecting food processing and maintaining quality and safety.
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Affiliation(s)
- Chao-Hui Feng
- RIKEN Centre for Advanced Photonics, RIKEN, Sendai, Japan
| | - Chiko Otani
- RIKEN Centre for Advanced Photonics, RIKEN, Sendai, Japan.,Department of Physics, Tohoku University, Sendai, Miyagi, Japan
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16
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Shen T, Yu H, Wang YZ. Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization. Molecules 2020; 25:molecules25061442. [PMID: 32210010 PMCID: PMC7144467 DOI: 10.3390/molecules25061442] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 03/15/2020] [Accepted: 03/20/2020] [Indexed: 01/09/2023] Open
Abstract
Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm-1) and Fourier transform mid-infrared (MIR: 4000-600 cm-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.
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Affiliation(s)
- Tao Shen
- Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China;
- The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresources in China (Yunnan) and Southeast Asia, Yunnan University, Kunming 650091, China
- College of Chemistry, Biological and Environment, Yuxi Normal University, Yu’xi 653100, China
| | - Hong Yu
- Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China;
- The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresources in China (Yunnan) and Southeast Asia, Yunnan University, Kunming 650091, China
- Correspondence: ; Tel.: +86-1370-067-6633
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China;
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Review of Analytical Methods to Detect Adulteration in Coffee. J AOAC Int 2020; 103:295-305. [DOI: 10.1093/jaocint/qsz019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 12/11/2022]
Abstract
Abstract
As one of the most consumed beverages in the world, coffee plays many major socioeconomical roles in various regions. Because of the wide coffee varieties available in the marketplaces, and the substantial price gaps between them (e.g., Arabica versus Robusta; speciality versus commodity coffees), coffees are susceptible to intentional or accidental adulteration. Therefore, there is a sustaining interest from the producers and regulatory agents to develop protocols to detect fraudulent practices. In general, strategies to authenticate coffee are based on targeted chemical profile analyses to determine specific markers of adulterants, or nontargeted analyses based on the “fingerprinting” concept. This paper reviews the literature related to chemometric approaches to discriminate coffees based on nuclear magnetic resonance spectroscopy, chromatography, infrared/Raman spectroscopy, and array sensors/indicators. In terms of chemical profiling, the paper focuses on the detection of diterpenes, homostachydrine, phenolic acids, carbohydrates, fatty acids, triacylglycerols, and deoxyribonucleic acid. Finally, the prospects of coffee authentication are discussed.
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Martins PMM, Batista NN, Miguel MGDCP, Simão JBP, Soares JR, Schwan RF. Coffee growing altitude influences the microbiota, chemical compounds and the quality of fermented coffees. Food Res Int 2019; 129:108872. [PMID: 32036899 DOI: 10.1016/j.foodres.2019.108872] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 01/25/2023]
Abstract
The objective of this work was to evaluate the influence of different altitudes on the epiphytic microbiota of coffee beans and on sensorial and chemical quality of coffees grown at 800, 1000, 1200, and 1400 m in Serra do Caparaó, Espírito Santo, Brazil. For microbiological analysis, the population counts of mesophilic bacteria, lactic acid bacteria (LAB), and yeasts were performed from the surface plating. The isolates were grouped and identified from the Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and sequencing of the ribosomal region was used. The chemical composition of the green grains was evaluated by Raman spectroscopy, and the sensory analysis of the roasted grains was performed using temporal dominance of sensations (TDS). During fermentation, there was a decrease in the LAB in pulped coffee from 800 and 1000 m altitude, while an increase was observed at 1200 and 1400 m. In natural coffee, there was an increase of LAB population at all altitudes. The highest diversity of mesophilic bacteria and yeast were identified in natural 1400 m and 1000 m, respectively. However pulped coffee treatments it was at 1200 m and 800 m. The chlorogenic acid and fatty acids in the green bean changed with altitude variation and processing. The floral attribute was detected only at altitude 1400 m. Caramel, chocolate and almond attributes were most frequently detected in coffees at different altitudes and processing. Therefore, pulped coffee processing was most suitable at low altitude while at high altitudes, both processes can be conducted to obtain a beverage with unusual sensory profile.
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Affiliation(s)
| | - Nádia Nara Batista
- Food Sciences Department, Federal University of Lavras, CEP 37200-000 Lavras, MG, Brazil
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Wang YY, Li JQ, Liu HG, Wang YZ. Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species. Molecules 2019; 24:molecules24122210. [PMID: 31200472 PMCID: PMC6631843 DOI: 10.3390/molecules24122210] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 06/06/2019] [Accepted: 06/11/2019] [Indexed: 12/03/2022] Open
Abstract
Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.
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Affiliation(s)
- Yuan-Yuan Wang
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China.
| | - Jie-Qing Li
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China.
| | - Hong-Gao Liu
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China.
| | - Yuan-Zhong Wang
- College of Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China.
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