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Zhu L, Liu F, Du Q, Shi T, Deng J, Li H, Cai F, Meng Z, Chen Q, Zhang J, Huang J. Variation Analysis of Starch Properties in Tartary Buckwheat and Construction of Near-Infrared Models for Rapid Non-Destructive Detection. PLANTS (BASEL, SWITZERLAND) 2024; 13:2155. [PMID: 39124273 PMCID: PMC11314173 DOI: 10.3390/plants13152155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Revised: 07/27/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
Due to the requirements for quality testing and breeding Tartary buckwheat (Fagopyrum tartaricum Gaerth), it is necessary to find a method for the rapid detection of starch content in Tartary buckwheat. To obtain samples with a continuously distributed chemical value, stable Tartary buckwheat recombinant inbred lines were used. After scanning the near-infrared spectra of whole grains, we employed conventional methods to analyze the contents of Tartary buckwheat. The results showed that the contents of total starch, amylose, amylopectin, and resistant starch were 532.1-741.5 mg/g, 176.8-280.2 mg/g, 318.8-497.0 mg/g, and 45.1-105.2 mg/g, respectively. The prediction model for the different starch contents in Tartary buckwheat was established using near-infrared spectroscopy (NIRS) in combination with chemometrics. The Kennard-Stone algorithm was used to split the training set and the test set. Six different methods were used to preprocess the spectra in the wavenumber range of 4000-12,000 cm-1. The Competitive Adaptive Reweighted Sampling algorithm was then used to extract the characteristic spectra, and the prediction model was built using the partial least squares method. Through a comprehensive analysis of each parameter of the model, the best model for the prediction of each nutrient was determined. The correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp) of the best models for total starch and amylose were greater than 0.95, and the Rc and Rp of the best models for amylopectin and resistant starch were also greater than 0.93. The results showed that the NIRS-based prediction model fulfilled the requirement for the rapid determination of Tartary buckwheat starch, thus providing an effective technical approach for the rapid and non-destructive testing of starch content in the food science and agricultural industry.
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Affiliation(s)
- Liwei Zhu
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Fei Liu
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Qianxi Du
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Taoxiong Shi
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Jiao Deng
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Hongyou Li
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Fang Cai
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Ziye Meng
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Qingfu Chen
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
| | - Jieqiong Zhang
- Guizhou Provincial Agricultural Technology Extension Station, Guiyang 550001, China;
| | - Juan Huang
- Research Center of Buckwheat Industry Technology, College of Life Science, Guizhou Normal University, Guiyang 550025, China; (L.Z.); (F.L.); (Q.D.); (T.S.); (J.D.); (H.L.); (F.C.); (Z.M.); (Q.C.)
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Taradolsirithitikul P, Sirisomboon P, Dachoupakan Sirisomboon C. Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2017; 97:1260-1266. [PMID: 27324609 DOI: 10.1002/jsfa.7859] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 04/27/2016] [Accepted: 06/13/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND Ochratoxin A (OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner. RESULTS PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient (r) of 0.814, a standard error of prediction (SEP and bias of 1.965 µg kg-1 and 0.358 µg kg-1 , respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non-OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%. CONCLUSION The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry.
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Affiliation(s)
| | - Panmanas Sirisomboon
- Curriculum of Agricultural Engineering, Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand
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Xie L, Tang S, Chen N, Luo J, Jiao G, Shao G, Wei X, Hu P. Optimisation of near-infrared reflectance model in measuring protein and amylose content of rice flour. Food Chem 2014; 142:92-100. [DOI: 10.1016/j.foodchem.2013.07.030] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2012] [Revised: 02/05/2013] [Accepted: 07/07/2013] [Indexed: 10/26/2022]
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Dachoupakan Sirisomboon C, Putthang R, Sirisomboon P. Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice. Food Control 2013. [DOI: 10.1016/j.foodcont.2013.02.034] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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