{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:26:59Z","timestamp":1773901619806,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400\u20132500 nm), and two HSI cameras operated in 400\u20131000 nm (VNIR) and 1000\u20132500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400\u2013550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451\u20131600 nm, 1901\u20132050 nm and 2051\u20132200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.<\/jats:p>","DOI":"10.3390\/rs13061128","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T21:42:41Z","timestamp":1615930961000},"page":"1128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7636-0481","authenticated-orcid":false,"given":"Iman","family":"Tahmasbian","sequence":"first","affiliation":[{"name":"Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD 4350, Australia; Scopus affiliation ID: 60028929"}]},{"given":"Natalie K.","family":"Morgan","sequence":"additional","affiliation":[{"name":"School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia"}]},{"given":"Shahla","family":"Hosseini Bai","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6108-2669","authenticated-orcid":false,"given":"Mark W.","family":"Dunlop","sequence":"additional","affiliation":[{"name":"Department of Agriculture and Fisheries, Queensland Government, Toowoomba, QLD 4350, Australia; Scopus affiliation ID: 60028929"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8647-8448","authenticated-orcid":false,"given":"Amy F.","family":"Moss","sequence":"additional","affiliation":[{"name":"School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"key":"ref_1","unstructured":"Wilkinson, S. (2018, January 4\u20137). Big Data for Poultry\u2013What Is Possible?. Proceedings of the 29th Annual Australian Poultry Science Symposium, Sydney, Australia. Available online: https:\/\/poultry-research.sydney.edu.au\/publications\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.5713\/ab.21.0034","article-title":"Precision feeding and precision nutrition: A paradigm shift in broiler feed formulation?","volume":"34","author":"Moss","year":"2021","journal-title":"Anim. Biosci."},{"key":"ref_3","unstructured":"ACMF (2020, February 10). Australian Industry Facts & Figures. Available online: https:\/\/www.chicken.org.au\/facts-and-figures\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8200","DOI":"10.1039\/C4CS00062E","article-title":"Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials","volume":"43","author":"Manley","year":"2014","journal-title":"Chem. Soc. Rev."},{"key":"ref_5","unstructured":"Kleyn, R. (2013). Chicken Nutrition: A Guide for Nutritionists and Poultry Professionals, Context."},{"key":"ref_6","unstructured":"Moss, A., Crowley, T., and Choct, M. (2020, January 16\u201319). Compilation and Assessment of the Variability of Nutrient Specifications for Commonly Used Australian Feed Ingredients. Proceedings the Australian Poultry Science Symposium, Sydney, Australia,."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110893","DOI":"10.1016\/j.lwt.2021.110893","article-title":"An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples","volume":"143","author":"Tahmasbian","year":"2021","journal-title":"LWT"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107781","DOI":"10.1016\/j.foodcont.2020.107781","article-title":"Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch","volume":"123","author":"Khamsopha","year":"2021","journal-title":"Food Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"128473","DOI":"10.1016\/j.foodchem.2020.128473","article-title":"Predicting micronutrients of wheat using hyperspectral imaging","volume":"343","author":"Hu","year":"2021","journal-title":"Food Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105868","DOI":"10.1016\/j.compag.2020.105868","article-title":"Quality estimation of nuts using deep learning classification of hyperspectral imagery","volume":"180","author":"Han","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","unstructured":"Sun, D.-W. (2010). Hyperspectral Imaging for Food Quality Analysis and Control, Elsevier."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1080\/10408398.2010.543495","article-title":"Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A review","volume":"52","author":"Elmasry","year":"2012","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.talanta.2018.11.071","article-title":"Detection of Salmonella from chicken rinsate with visible\/near-infrared hyperspectral microscope imaging compared against RT-PCR","volume":"195","author":"Eady","year":"2019","journal-title":"Talanta"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"513","DOI":"10.13031\/2013.12940","article-title":"Calibration of a pushbroom hyperspectral imaging system for agricultural inspection","volume":"46","author":"Lawrence","year":"2003","journal-title":"Trans. ASAE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"203","DOI":"10.13031\/2013.13096","article-title":"Accuracy and repeatability of protein content measurements for wheat during storage","volume":"19","author":"Casada","year":"2003","journal-title":"Appl. Eng. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.catena.2018.04.023","article-title":"Using laboratory-based hyperspectral imaging method to determine carbon functional group distributions in decomposing forest litterfall","volume":"167","author":"Tahmasbian","year":"2018","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.compag.2018.06.029","article-title":"A non-destructive determination of peroxide values, total nitrogen and mineral nutrients in an edible tree nut using hyperspectral imaging","volume":"151","author":"Bai","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"128615","DOI":"10.1016\/j.foodchem.2020.128615","article-title":"Application of near-infrared hyperspectral (NIR) images combined with multivariate image analysis in the differentiation of two mycotoxicogenic Fusarium species associated with maize","volume":"344","author":"Simeone","year":"2021","journal-title":"Food Chem."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Song, X., Li, Z., Xu, X., Feng, H., and Zhao, C. (2021). Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040581"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, X., Fan, L., Li, Z., Meng, Y., Feng, H., Yang, H., and Xu, B. (2021). Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV. Remote Sens., 13.","DOI":"10.3390\/rs13030340"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intellig. Lab. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses","volume":"5","author":"Wold","year":"1984","journal-title":"SIAM J. Sci. Stat. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1002\/cem.1180020306","article-title":"PLS regression methods","volume":"2","year":"1988","journal-title":"J. Chemom."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1007\/s11368-017-1751-z","article-title":"The potential of hyperspectral images and partial least square regression for predicting total carbon, total nitrogen and their isotope composition in forest litterfall samples","volume":"17","author":"Tahmasbian","year":"2017","journal-title":"J. Soils Sed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.aca.2012.01.004","article-title":"Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging","volume":"719","author":"Barbin","year":"2012","journal-title":"Anal. Chim. Acta"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"K\u00e4mper, W., Trueman, S.J., Tahmasbian, I., and Bai, S.H. (2020). Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis\/NIR Hyperspectral Imaging of Flesh or Skin. Remote Sens., 12.","DOI":"10.3390\/rs12203409"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11368-019-02418-z","article-title":"Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection","volume":"20","author":"Malmir","year":"2020","journal-title":"J. Soils Sed."},{"key":"ref_29","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the International Joint Conference on Artificial Intelligence (Ijcai), Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geoderma.2018.12.049","article-title":"Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique","volume":"340","author":"Malmir","year":"2019","journal-title":"Geoderma"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shen, L., Gao, M., Yan, J., Li, Z.-L., Leng, P., Yang, Q., and Duan, S.-B. (2020). Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method. Remote Sens., 12.","DOI":"10.3390\/rs12071206"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.trac.2010.05.006","article-title":"Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy","volume":"29","author":"Palagos","year":"2010","journal-title":"Trends Anal. Chem."},{"key":"ref_33","unstructured":"Sillero, A.M., Pierna, J.A.F., Sinnaeve, G., Dardenne, P., and Baeten, V. (2018). Quantification of protein in wheat using near infrared hyperspectral imaging: Performance comparison with conventional near infrared spectroscopy. J. Near Infrared Spectrosc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"126241","DOI":"10.1016\/j.eja.2021.126241","article-title":"An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives","volume":"124","author":"Fu","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.jfca.2011.01.006","article-title":"Nutritional composition and antioxidant properties of Canarium odontophyllum Miq.(dabai) fruits","volume":"24","author":"Chew","year":"2011","journal-title":"J. Food Compos. Anal."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bai, S.H., Darby, I., Nevenimo, T., Hannet, G., Hannet, D., Poienou, M., Grant, E., Brooks, P., Walton, D., and Randall, B.J.P.o. (2017). Effects of roasting on kernel peroxide value, free fatty acid, fatty acid composition and crude protein content. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0184279"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1039\/b605386f","article-title":"Hyperspectral NIR imaging for calibration and prediction: A comparison between image and spectrometer data for studying organic and biological samples","volume":"131","author":"Burger","year":"2006","journal-title":"Analyst"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.compag.2018.06.042","article-title":"Optimal sample selection for measurement of soil organic carbon using on-line vis-NIR spectroscopy","volume":"151","author":"Nawar","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Walvoort","year":"2006","journal-title":"Geoderma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.still.2004.12.006","article-title":"Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions","volume":"85","author":"Cozzolino","year":"2006","journal-title":"Soil Tillage Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.geoderma.2018.06.008","article-title":"Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions","volume":"330","author":"Tahmasbian","year":"2018","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Sun, D.-W. (2009). Infrared Spectroscopy for Food Quality Analysis and Control, Academic Press."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jcs.2012.04.011","article-title":"Analysis of wheat grain development using NIR spectroscopy","volume":"56","author":"Gergely","year":"2012","journal-title":"J. Cereal Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2019.03.016","article-title":"Development and performance test of an in-situ soil total nitrogen-soil moisture detector based on near-infrared spectroscopy","volume":"160","author":"Zhou","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100129","DOI":"10.1016\/j.japr.2020.100129","article-title":"Raw material nutrient variability has substantial impact on the potential profitability of chicken meat production","volume":"30","author":"Moss","year":"2021","journal-title":"J. Appl. Poult. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1016\/j.biortech.2006.07.039","article-title":"Feed formulations to reduce N excretion and ammonia emission from poultry manure","volume":"98","author":"Nahm","year":"2007","journal-title":"Bioresour. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1093\/japr\/13.4.684","article-title":"Implications of ammonia production and emissions from commercial poultry facilities: A review","volume":"13","author":"Ritz","year":"2004","journal-title":"J. Appl. Poult. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100137","DOI":"10.1016\/j.japr.2020.100137","article-title":"Alternatives to formulate laying hen diets beyond the traditional least-cost model","volume":"30","author":"Moss","year":"2021","journal-title":"J. Appl. Poult. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:36:36Z","timestamp":1760160996000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,16]]},"references-count":50,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061128"],"URL":"https:\/\/doi.org\/10.3390\/rs13061128","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,16]]}}}