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López-Calabozo R, Liberal Â, Fernandes Â, Revilla I, Ferreira ICFR, Barros L, Vivar-Quintana AM. Determination of Carbohydrate Composition in Lentils Using Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2024; 24:4232. [PMID: 39001010 PMCID: PMC11244296 DOI: 10.3390/s24134232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a significant influence on the techno-functional properties of lentil-derived products. In this study, the use of near-infrared spectroscopy (NIRS) to predict the concentration of total carbohydrate, fibre, starch, total sugars, fructose, sucrose and raffinose was investigated. For this purpose, six different cultivars of macrosperm (n = 37) and microsperm (n = 43) lentils have been analysed, the samples were recorded whole and ground and the suitability of both recording methods were compared. Different spectral and mathematical pre-treatments were evaluated before developing the calibration models using the Modified Partial Least Squares regression method, with a cross-validation and an external validation. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the total sugars and fructose, sucrose, and raffinose. The recording of ground samples allowed for obtaining better models for the calibration of starch content (R > 0.8), total sugars and sucrose (R > 0.93), and raffinose (R > 0.91). The results obtained confirm that there is sufficient information in the NIRS spectral region for the development of predictive models for the quantification of the carbohydrate content in lentils.
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Affiliation(s)
- Rocío López-Calabozo
- Food Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain; (R.L.-C.); (Â.L.); (I.R.)
| | - Ângela Liberal
- Food Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain; (R.L.-C.); (Â.L.); (I.R.)
- Centro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal; (Â.F.); (I.C.F.R.F.); (L.B.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ângela Fernandes
- Centro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal; (Â.F.); (I.C.F.R.F.); (L.B.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Isabel Revilla
- Food Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain; (R.L.-C.); (Â.L.); (I.R.)
| | - Isabel C. F. R. Ferreira
- Centro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal; (Â.F.); (I.C.F.R.F.); (L.B.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Lillian Barros
- Centro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal; (Â.F.); (I.C.F.R.F.); (L.B.)
- Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ana M. Vivar-Quintana
- Food Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain; (R.L.-C.); (Â.L.); (I.R.)
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Singer WM, Lee YC, Shea Z, Vieira CC, Lee D, Li X, Cunicelli M, Kadam SS, Khan MAW, Shannon G, Mian MAR, Nguyen HT, Zhang B. Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed. THE PLANT GENOME 2023; 16:e20415. [PMID: 38084377 DOI: 10.1002/tpg2.20415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/22/2023]
Abstract
Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.
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Affiliation(s)
- William M Singer
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Yi-Chen Lee
- Department of Agriculture, Fort Hays State University, Hays, Kansas, USA
| | - Zachary Shea
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Caio Canella Vieira
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, Arkansas, USA
| | - Dongho Lee
- Fisher Delta Research, Extension, and Education Center, University of Missouri, Portageville, Missouri, USA
| | - Xiaoying Li
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Mia Cunicelli
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, North Carolina, USA
| | - Shaila S Kadam
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | | | - Grover Shannon
- Fisher Delta Research, Extension, and Education Center, University of Missouri, Portageville, Missouri, USA
| | - M A Rouf Mian
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, North Carolina, USA
| | - Henry T Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | - Bo Zhang
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
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Amanah HZ, Tunny SS, Masithoh RE, Choung MG, Kim KH, Kim MS, Baek I, Lee WH, Cho BK. Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques. Foods 2022; 11:foods11020232. [PMID: 35053964 PMCID: PMC8774574 DOI: 10.3390/foods11020232] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/10/2022] [Accepted: 01/14/2022] [Indexed: 12/10/2022] Open
Abstract
The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.
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Affiliation(s)
- Hanim Z. Amanah
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (H.Z.A.); (S.S.T.); (W.-H.L.)
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, Indonesia;
| | - Salma Sultana Tunny
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (H.Z.A.); (S.S.T.); (W.-H.L.)
| | - Rudiati Evi Masithoh
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, Indonesia;
| | - Myoung-Gun Choung
- Department of Herbal Medicine Resource, Dogye Campus, Kangwon National University, Samcheok 25949, Korea;
| | - Kyung-Hwan Kim
- Department of Gene Engineering, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea;
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Wang-Hee Lee
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (H.Z.A.); (S.S.T.); (W.-H.L.)
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (H.Z.A.); (S.S.T.); (W.-H.L.)
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
- Correspondence: ; Tel.: +82-42-8216-715
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Sanaeifar A, Zhang W, Chen H, Zhang D, Li X, He Y. Study on effects of airborne Pb pollution on quality indicators and accumulation in tea plants using Vis-NIR spectroscopy coupled with radial basis function neural network. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 229:113056. [PMID: 34883323 DOI: 10.1016/j.ecoenv.2021.113056] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/12/2021] [Accepted: 12/01/2021] [Indexed: 06/13/2023]
Abstract
Tea plants that have a large leaf area mainly suffer from heavy metal accumulation in the above-ground parts through foliar uptake. With the world rapid industrialization, this pollution in tea is considered a crucial challenge due to its potential health risks. The present study proposes an innovative approach based on visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometrics for the characterization of tea chemical indicators under airborne lead stress, which can be performed fast and in situ. The effects of lead stress on chemical indicators and accumulation in leaves of the two tea varieties at different time intervals and levels of treatment were investigated. In addition, changes in cell structure and leaf stomata were monitored during foliar uptake of aerosol particles by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The spectral variation was able to classify the tea samples into the Pb treatment groups through the linear discriminant analysis (LDA) model. Two machine learning techniques, namely, partial least squares (PLS) and radial basis function neural network (RBFNN), were evaluated and compared for building the quantitative determination models. The RBFNN models combined with correlation-based feature selection (CFS) and PLS data compression methods were used to optimize the prediction performance. The results demonstrated that the PLS-RBFNN as a non-linear model outperformed the PLS model and provided the R-value of 0.944, 0.952, 0.881, 0.937, and 0.930 for prediction of MDA, starch, sucrose, fructose, glucose, respectively. It can be concluded that the proposed approach has strong application potential in monitoring the quality and safety of plants under airborne heavy metal stress.
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Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Haitian Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Dongyi Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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Wang H, Hu L, Zhou P, Ouyang L, Chen B, Li Y, Chen Y, Zhang Y, Zhou J. Simultaneous determination of fructose, glucose and sucrose by solid phase extraction-liquid chromatography-tandem mass spectrometry and its application to source and adulteration analysis of sucrose in tea. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2020.103730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Sanaeifar A, Huang X, Chen M, Zhao Z, Ji Y, Li X, He Y, Zhu Y, Chen X, Yu X. Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy. Food Sci Nutr 2020; 8:5860-5874. [PMID: 33282238 PMCID: PMC7684591 DOI: 10.1002/fsn3.1861] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 11/06/2022] Open
Abstract
Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near-infrared (Vis-NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis-NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis-NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis-NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real-time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes.
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Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xinyao Huang
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Mengyuan Chen
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Zhangfeng Zhao
- College of Mechanical EngineeringZhejiang University of TechnologyHangzhouChina
| | - Yifan Ji
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xiaoli Li
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Yong He
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Yi Zhu
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xi Chen
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xinxin Yu
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
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Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics. SENSORS 2020; 20:s20195451. [PMID: 32977413 PMCID: PMC7582835 DOI: 10.3390/s20195451] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/16/2020] [Accepted: 09/20/2020] [Indexed: 12/24/2022]
Abstract
Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.
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Berhow MA, Singh M, Bowman MJ, Price NPJ, Vaughn SF, Liu SX. Quantitative NIR determination of isoflavone and saponin content of ground soybeans. Food Chem 2020; 317:126373. [PMID: 32087514 DOI: 10.1016/j.foodchem.2020.126373] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 01/31/2020] [Accepted: 02/05/2020] [Indexed: 01/16/2023]
Abstract
Over 3200 discrete soybean samples were obtained from production locations around the United States during the years 2012-2016. Ground samples were scanned on near infrared spectrometers (NIRS) and analyzed by HPLC for total isoflavone and total saponin composition, as well as total carbohydrate composition. Multiple Linear Regression (MLR) analysis of preprocessed spectral data was used to develop optimized models to predict isoflavone content. The selection of a suitable calibration model was based on a high regression coefficient (R2), and lower standard error of calibration (SEC) values. Robust validated predictions were obtained for isoflavones, however less than robust calibrations were obtained for the total saponins. The correlations were not as robust for predicting the carbohydrate composition. NIRS is a suitable, rapid, nondestructive method to determine isoflavone composition in ground soybeans. Useful isoflavone composition predictions for large numbers of soybean samples can be obtained from quickly obtained NIRS scans.
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Affiliation(s)
- Mark A Berhow
- Functional Foods Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States(1).
| | - Mukti Singh
- Functional Foods Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States(1)
| | - Michael J Bowman
- Bioenergy Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States
| | - Neil P J Price
- Renewable Product Technology Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States
| | - Steven F Vaughn
- Functional Foods Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States(1)
| | - Sean X Liu
- Functional Foods Research Unit, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, 1815 N. University St., Peoria, IL 61604, United States(1)
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Using DNA Fingerprinting to Detect the Genetic Relationships in Acacia by Inter-Simple Sequence Repeat Markers. JOURNAL OF PURE AND APPLIED MICROBIOLOGY 2019. [DOI: 10.22207/jpam.13.1.30] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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10
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Lin CA, Ayvaz H, Rodriguez-Saona LE. Application of Portable and Handheld Infrared Spectrometers for Determination of Sucrose Levels in Infant Cereals. FOOD ANAL METHOD 2013. [DOI: 10.1007/s12161-013-9763-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Shekarchizadeh H, Ensafi AA, Kadivar M. Selective determination of sucrose based on electropolymerized molecularly imprinted polymer modified multiwall carbon nanotubes/glassy carbon electrode. MATERIALS SCIENCE & ENGINEERING. C, MATERIALS FOR BIOLOGICAL APPLICATIONS 2013; 33:3553-61. [DOI: 10.1016/j.msec.2013.04.052] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2013] [Accepted: 04/25/2013] [Indexed: 10/26/2022]
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