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Rapid determination of nutrient composition and mineral element content of common vetch (Vicia sativa L.) using near-infrared spectroscopy

  • Tao Wang (State Key Laboratory of Grassland Agroecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University) ;
  • Li Wang (State Key Laboratory of Grassland Agroecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University) ;
  • Tao Guo (State Key Laboratory of Grassland Agroecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University) ;
  • Yanli Shi (State Key Laboratory of Grassland Agroecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University) ;
  • Baocang Liu (Xinjiang Taikun Group Chang Feed Co., Ltd.) ;
  • Fei Li (State Key Laboratory of Grassland Agroecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University)
  • Received : 2024.12.11
  • Accepted : 2025.03.27
  • Published : 2025.09.01

Abstract

Objective: This study aims to establish an accurate and reliable near-infrared spectroscopy to enable rapid, efficient, and non-destructive evaluation of the nutritional quality of common vetches across different regions and varieties. Methods: A total of 190 samples from various regions and varieties were selected for this study, which were divided into a calibration set (4:1 ratio) and validation set. The original spectrum of the calibration set is subjected to 10 different pretreatment techniques in combination with first and second derivatives, while the prediction model was established by combining the measured value of common vetch with the modified partial least squares method. Results: The results indicate that the calibration root-mean-square error and cross-validation root-mean-square error values range from 0.01 to 1.25 and 0.01 to 1.37, respectively. The determination coefficients of cross-validation (R2CV) for phosphorus (P), potassium (K), magnesium (Mg), and iron (Fe) are relatively low at 0.82, 0.86, 0.82, and 0.74, respectively; however, all other indicators have R2CV values above 0.90. The predicted root means square errors (RMSEP) for common vetch indexes range from 0.01 to 1.87, with RMSEP values higher than 1.0 observed for crude protein, neutral detergent fiber, acid detergent fiber, and ash indices, whereas RMSEP values lower than or equal to 1.0 were obtained for other indicators. The measured coefficient of determination (R2p) demonstrates that the R2p values for each nutrient element and mineral element vary from 0.70 to 0.96. The residual prediction deviation (RPD) values for Mg exhibit relatively low levels, while the RPD values for other indicators exceed 2.0. Conclusion: These findings suggest that this study provides a viable approach to evaluate the nutritional composition and mineral element content of different varieties and regions of common vetch.

Keywords

Acknowledgement

The platform and support provided by the Ruminant Research Institute of Grassland Agricultural Science and Technology College of Lanzhou University are greatly appreciated.

References

  1. Larbi A, Hassan S, Kattash G, et al. Annual feed legume yield and quality in dryland environments in north-west Syria: 1. herbage yield and quality. Anim Feed Sci Technol 2010;160:81-9. https://doi.org/10.1016/j.anifeedsci.2010.07.003
  2. Pastor-Cavada E, Juan R, Pastor JE, Alaiz M, Vioque J. Antioxidant activity of seed polyphenols in fifteen wild Lathyrus species from South Spain. LWT-Food Sci Technol 2009;42:705-9. https://doi.org/10.1016/j.lwt.2008.10.006
  3. Remigi P, Zhu J, Young JPW, Masson-Boivin C. Symbiosis within symbiosis: evolving nitrogen-fixing legume symbionts. Trends Microbiol 2016;24:63-75. https://doi.org/10.1016/j.tim.2015.10.007
  4. Huang Y, Li R, Coulter JA, Zhang Z, Nan Z. Comparative grain chemical composition, ruminal degradation in vivo, and intestinal digestibility in vitro of Vicia sativa L. varieties grown on the Tibetan plateau. Animals 2019;9:212. https://doi.org/10.3390/ani9050212
  5. Yolcu H, Gunes A, Turan M. Evaluation of annual legumes and barley as sole crops and intercrop in spring frost conditions for animal feeding II. Mineral composition. J Anim Vet Adv 2009;8:1-6. 
  6. Greveniotis V, Bouloumpasi E, Zotis S, Korkovelos A, Ipsilandis CG. A stability analysis using AMMΙ and GGE biplot approach on forage yield assessment of common vetch in both conventional and low-input cultivation systems. Agriculture 2021;11:567. https://doi.org/10.3390/agriculture11060567
  7. Oten M, Kiremitci S, Erdurmus C. Unveil of superior landraces of common vetch (Vicia sativa l.) for yield and yield components to overcome bottleneck. Fresenius Environ Bull 2018;27:6324-9.
  8. Sanchez PDC, Hashim N, Shamsudin R, Nor MZM. Quality evaluation of sweet potatoes (Ipomoea batatas L.) of different varieties using laser light backscattering imaging technique. Sci Hortic 2020;260:108861. https://doi.org/10.1016/j.scienta.2019.108861
  9. Anderson JV, Wittenberg A, Li H, Berti MT. High throughput phenotyping of Camelina sativa seeds for crude protein, total oil, and fatty acids profile by near infrared spectroscopy. Ind Crops Prod 2019;137:501-7. https://doi.org/10.1016/j.indcrop.2019.04.075
  10. Wang M, Li X, Shen Y, et al. A systematic high-throughput phenotyping assay for sugarcane stalk quality characterization by near-infrared spectroscopy. Plant Methods 2021;17:76. https://doi.org/10.1186/s13007-021-00777-8
  11. Li M, Wang J, Du F, Diallo B, Xie GH. High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using dual-optimized partial least squares calibration models. Biotechnol Biofuels 2017;10:206. https://doi.org/10.1186/s13068-017-0892-z
  12. Association of Official Analytical Chemists (AOAC). Official methods of analysis. 21st ed. AOAC International; 2019.
  13. Jia J, Zhou X, Li Y, Wang M, Liu Z, Dong C. Establishment of a rapid detection model for the sensory quality and components of Yuezhou Longjing tea using near-infrared spectroscopy. LWT-Food Sci Technol 2022;164:113625. https://doi.org/10.1016/j.lwt.2022.113625
  14. Payne CE, Wolfrum EJ. Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy. Biotechnol Biofuels 2015;8:1-14. https://doi.org/10.1186/s13068-015-0222-2
  15. Escuredo O, Seijo-Rodríguez A, Inmaculada González-Martín M, Shantal Rodríguez-Flores M, Carmen Seijo M. Potential of near infrared spectroscopy for predicting the physicochemical properties on potato flesh. Microchem J 2018;141:451-7. https://doi.org/10.1016/j.microc.2018.06.008
  16. He HJ, Wang Y, Zhang M, Wang Y, Ou X, Guo J. Rapid determination of reducing sugar content in sweet potatoes using NIR spectra. J Food Compos Anal 2022:111:104641. https://doi.org/10.1016/j.jfca.2022.104641
  17. Miao X, Miao Y, Gong H, et al. NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples. Spectrochim Acta A Mol Biomol Spectrosc 2021;257:119700. https://doi.org/10.1016/j.saa.2021.119700
  18. Yang Z, Li K, Zhang M, Xin D, Zhang J. Rapid determination of chemical composition and classification of bamboo fractions using visible–near infrared spectroscopy coupled with multivariate data analysis. Biotechnol Biofuels 2016;9:35. https://doi.org/10.1186/s13068-016-0443-z
  19. Santa IM, Nagel S, Taylor JD. Incorporating the pedigree information in multi-environment trial analyses for improving common vetch. Front Plant Sci 2023;14:1166133. https://doi.org/10.3389/fpls.2023.1166133
  20. Huang YF, Gao XL, Nan ZB, Zhang ZX. Potential value of the common vetch (Vicia sativa L.) as an animal feedstuff: a review. J Anim Physiol Anim Nutr 2017;101:807-23. https://doi.org/10.1111/jpn.12617
  21. Tomar M, Bhardwaj R, Kumar M, et al. Development of NIR spectroscopy based prediction models for nutritional profiling of pearl millet (Pennisetum glaucum (L.)) R.Br: a chemometrics approach. LWT-Food Sci Technol 2021;149:111813. https://doi.org/10.1016/j.lwt.2021.111813
  22. Liu J, Jin S, Bao C, Sun Y, Li W. Rapid determination of lignocellulose in corn stover based on near-infrared reflectance spectroscopy and chemometrics methods. Bioresour Technol 2021;321:124449. https://doi.org/10.1016/j.biortech.2020.124449
  23. Ejaz I, He S, Li W, et al. Sorghum grains grading for food, feed, and fuel using NIR spectroscopy. Front Plant Sci 2021;12:720022. https://doi.org/10.3389/fpls.2021.720022
  24. Eum C, Jang D, Kim J, Choi S, Cha K, Chung H. Improving the accuracy of spectroscopic identification of geographical origins of agricultural samples through cooperative combination of near-infrared and laser-induced breakdown spectroscopy. Spectrochim Acta B At Spectrosc 2018;149:281-7. https://doi.org/10.1016/j.sab.2018.09.004
  25. Buratti S, Sinelli N, Bertone E, Venturello A, Casiraghi E, Geobaldo F. Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis. J Sci Food Agric 2015;95:2192-200. https://doi.org/10.1002/jsfa.6933
  26. Sim J, McGoverin C, Oey I, Frew R, Kebede B. Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee. Food Chem 2023:427:136695. https://doi.org/10.1016/j.foodchem.2023.136695
  27. Williams PC. Application of chemometrics to prediction of some wheat quality factors by near-infrared spectroscopy. Cereal Chem 2020;97:958-66. https://doi.org/10.1002/cche.10318
  28. Li M, He S, Wang J, Liu Z, Xie GH. An NIRS-based assay of chemical composition and biomass digestibility for rapid selection of Jerusalem artichoke clones. Biotechnol Biofuels 2018;11:334. https://doi.org/10.1186/s13068-018-1335-1
  29. Zhang A, Hu Z, Hu X, et al. Large-scale screening of diverse barely lignocelluloses for simultaneously upgrading biomass enzymatic saccharification and plant lodging resistance coupled with near-infrared spectroscopic assay. Ind Crops Prod 2023;194:116324 https://doi.org/10.1016/j.indcrop.2023.116324
  30. Patel N, Toledo-Alvarado H, Cecchinato A, Bittante G. Predicting the content of 20 minerals in beef by different portable near-infrared (NIR) spectrometers. Foods 2020;9:1389. https://doi.org/10.3390/foods9101389
  31. Ikoyi AY, Younge BA. Influence of forage particle size and residual moisture on near infrared reflectance spectroscopy (NIRS) calibration accuracy for macro-mineral determination. Anim Feed Sci Technol 2020;270:114674. https://doi.org/10.1016/j.anifeedsci.2020.114674
  32. Fang J, Jin X, Wu L, et al. Prediction models for the content of calcium, boron and potassium in the fruit of 'Huangguan' pears established by using near-infrared spectroscopy. Foods 2022;11:223642. https://doi.org/10.3390/foods11223642
  33. Nuwamanya E, Wembabazi E, Kanaabi M, et al. Development and validation of near-infrared spectroscopy procedures for prediction of cassava root dry matter and amylose contents in Ugandan cassava germplasm. J Sci Food Agric 2024;104:4793-800. https://doi.org/10.1002/jsfa.12966
  34. Carbas B, Machado N, Oppolzer D, et al. Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for the determination of nutritional and antinutritional parameters in common beans. Food Chem 2020;306:125509. https://doi.org/10.1016/j.foodchem.2019.125509
  35. González-Martín I, Hernández-Hierro JM, González-Cabrera JM. Use of NIRS technology with a remote reflectance fibreoptic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa. Anal Bioanal Chem 2007;387:2199-205. https://doi.org/10.1007/s00216-006-1039-4
  36. Kämper W, Trueman SJ, Tahmasbian I, Bai SH. Rapid determination of nutrient concentrations in hass avocado fruit by Vis/NIR hyperspectral imaging of flesh or skin. Remote Sens 2020;12: 3409. https://doi.org/10.3390/rs12203409
  37. Yu Y, Chai Y, Li Z, Ren Z, Dong H, Chen L. Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics. Food Chem 2025;462:141033. https://doi.org/10.1016/j.foodchem.2024.141033
  38. Carbas B, Machado N, Oppolzer D, et al. Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy for the determination of nutritional and antinutritional parameters in common beans. Food Chem 2020;306:125509. https://doi.org/10.1016/j.foodchem.2019.125509
  39. Zaukuu JLZ, Zimmermann E, Acquah BB, Kwofie ED. Novel detection techniques for shrimp powder adulteration using near infrared spectroscopy in tandem chemometric tools and multiple spectral preprocessing. Food Anal Methods 2023;16:819-31. https://doi.org/10.1007/s12161-023-02460-1
  40. Thomson AL, Humphries DJ, Rymer C, Archer JE, Grant NW, Reynolds CK. Assessing the accuracy of current near infra-red reflectance spectroscopy analysis for fresh grassclover mixture silages and development of new equations for this purpose. Anim Feed Sci Technol 2018;239:94-106. https://doi.org/10.1016/j.anifeedsci.2018.03.009
  41. Shi D, Hang J, Neufeld J, Zhao S, House JD. Estimation of crude protein and amino acid contents in whole, ground and defatted ground soybeans by different types of near-infrared (NIR) reflectance spectroscopy. J Food Compos Anal 2022;111:104601. https://doi.org/10.1016/j.jfca.2022.104601
  42. Babos DV, Ramos JFK, Francisco GC, Benites VM, Milori DMBP. Laser-induced breakdown spectroscopy and digital image data fusion for determination of the Al, Ca, Fe, Mg, and P in mineral fertilizer: overcome matrix effects in solid direct analysis. J Opt Soc Am B 2023;40:654-60. https://doi.org/10.1364/JOSAB.482619
  43. González-Martín MI, Escuredo O, Hernández-Jiménez M, et al. Prediction of stable isotopes and fatty acids in subcutaneous fat of Iberian pigs by means of NIR: a comparison between benchtop and portable systems. Talanta 2021;224:121817. https://doi.org/10.1016/j.talanta.2020.121817
  44. Ancin-Murguzur FJ, Tarroux A, Bråthen KA, Bustamante P, Descamps S. Using near-infrared reflectance spectroscopy (NIRS) to estimate carbon and nitrogen stable isotope composition in animal tissues. Ecol Evol 2021;11:10483-8. https://doi.org/10.1002/ece3.7851
  45. Hossain MM, Rahim MA, Moutosi HN, Das L. Evaluation of the growth, storage root yield, proximate composition, and mineral content of colored sweet potato genotypes. J Agric Food Res 2022;8:100289. https://doi.org/10.1016/j.jafr.2022.100289
  46. de Aldana BRV, Criado BG, Ciudad AG, Corona MEP. Estimation of mineral content in natural grasslands by near infrared reflectance spectroscopy. Commun Soil Sci Plant Anal 1995;26:1383-96. https://doi.org/10.1080/00103629509369379
  47. Sepúlveda MÁ, Hidalgo M, Araya J, et al. Near-infrared spectroscopy: alternative method for assessment of stable carbon isotopes in various soil profiles in Chile. Geoderma Reg 2021;25:e00397. https://doi.org/10.1016/j.geodrs.2021.e00397
  48. Wafula EN, Onduso M, Wainaina IN, et al. Antinutrient to mineral molar ratios of raw common beans and their rapid prediction using near-infrared spectroscopy. Food Chem 2022;368:130773. https://doi.org/10.1016/j.foodchem.2021.130773
  49. Lu Y, Jia B, Yoon SC, et al. Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology. Int J Food Microbiol 2024;416:110661. https://doi.org/10.1016/j.ijfoodmicro.2024.110661
  50. Rodríguez-Espinosa ME, Guevara-Oquendo VH, Yang JC, Feng X, Zhang W, Yu P. Processing induced changes in physicochemical structure properties and nutrient metabolism and their association in cool-season faba (CSF: Vicia L.), revealed by vibrational FTIR spectroscopy with chemometrics and nutrition modeling techniques. Crit Rev Food Sci Nutr 2021;61:1099-107. https://doi.org/10.1080/10408398.2020.1754160
  51. Liu GS, Guo HS, Pan T, Wang JH, Cao G. Vis-NIR spectroscopic pattern recognition combined with SG smoothing applied to breed screening of transgenic sugarcane. Guang Pu Xue Yu Guang Pu Fen Xi 2014;34:2701-6. 
  52. Tang C, Jiang B, Ejaz I, et al. High-throughput phenotyping of nutritional quality components in sweet potato roots by nearinfrared spectroscopy and chemometrics methods. Food Chem X 2023;20:100916. https://doi.org/10.1016/j.fochx.2023.100916