{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:10:38Z","timestamp":1788844238005,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>Moisture content is an important indicator of grain storage safety, processing quality, and circulation efficiency. However, the performance of near-infrared spectroscopy (NIRS) models is affected by differences in grain crops, sample morphologies, preprocessing strategies, feature selection methods, and regression models, and the adaptation relationships among these factors remain insufficiently understood. In this study, soybean, maize, and wheat samples with whole-grain and powder morphologies were investigated to reveal the method adaptation patterns of NIRS-based moisture prediction under consistent experimental conditions. Spectra in the range of 900\u20131700 nm were collected, and 54 analytical pathways were established by combining six preprocessing strategies, three feature selection algorithms (SPA, CARS, and UVE), and three regression models (PLSR, SVR, and RF). The results showed that representative optimal pathways achieved Rp values above 0.9730 for powder samples and above 0.9587 for whole-grain samples. Different grain crops and sample morphologies exhibited distinct analytical pathway preferences, indicating that appropriate analytical strategies should be selected according to specific detection objects. Spectral variation analysis further demonstrated higher spectral variability in whole-grain samples than in powder samples. This study provides insights into the selection of suitable NIRS analytical strategies for grain moisture prediction and quality assessment.<\/jats:p>","DOI":"10.3390\/foods15173093","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"3093","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Method Adaptation Patterns and a Selection Framework for Near-Infrared Spectroscopy-Based Moisture Prediction of Major Grain Crops"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-0141","authenticated-orcid":false,"given":"Chenxiao","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglong","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Song","sequence":"additional","affiliation":[{"name":"College of Physics, Jilin University, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7103-1870","authenticated-orcid":false,"given":"Daping","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Dai, L., Huang, Z., Gong, C., Chen, J., Xie, J., and Qu, M. (2025). Corn Seed Quality Detection Based on Spectroscopy and Its Imaging Technology: A Review. Agriculture, 15.","DOI":"10.3390\/agriculture15040390"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112390","DOI":"10.1016\/j.jfoodeng.2024.112390","article-title":"Density-Independent Wheat Moisture Content Detection Based on DMLA","volume":"388","author":"Li","year":"2025","journal-title":"J. Food Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Li, G., Huang, Z., He, X., Zhang, Z., Li, Z., and Du, H. (2024). Research on Non-Destructive and Rapid Detection Technology of Foxtail Millet Moisture Content Based on Capacitance Method and Logistic-SSA-ELM Modelling. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1354290"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s40538-025-00747-5","article-title":"Research Progress in Near-Infrared Spectroscopy for Detecting the Quality of Potato Crops","volume":"12","author":"Ren","year":"2025","journal-title":"Chem. Biol. Technol. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s44211-022-00106-6","article-title":"Application of Near-Infrared Spectroscopy to Agriculture and Forestry","volume":"38","author":"Tsuchikawa","year":"2022","journal-title":"Anal. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111482","DOI":"10.1016\/j.foodcont.2025.111482","article-title":"From Grain to Gain: Bridging Conventional Methods with Chemometric Innovations in Cereal Quality Analysis through Near-Infrared Spectroscopy (NIRS)","volume":"178","author":"Tomar","year":"2025","journal-title":"Food Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1080\/05704928.2018.1425214","article-title":"Near-Infrared Spectroscopy and Hyperspectral Imaging for Non-Destructive Quality Assessment of Cereal Grains","volume":"53","author":"Caporaso","year":"2018","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"119666","DOI":"10.1016\/j.saa.2021.119666","article-title":"Application of Long-Wave Near-Infrared Hyperspectral Imaging for Determination of Moisture Content of Single Maize Seed","volume":"254","author":"Wang","year":"2021","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Z.L., Fan, S.X., Wu, J.Z., Zhang, C., and Li, H.W. (2023). Development of a General Prediction Model of Moisture Content in Maize Seeds Based on LW-NIR Hyperspectral Imaging. Agriculture, 13.","DOI":"10.3390\/agriculture13020359"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100955","DOI":"10.1016\/j.afres.2025.100955","article-title":"Rapid and Non-Destructive Monitoring of the Drying Process of Glutinous Rice Using Visible-Near Infrared Hyperspectral Imaging","volume":"5","author":"Jimoh","year":"2025","journal-title":"Appl. Food Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, P., and Liu, J. (2025). Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds. Agriculture, 15.","DOI":"10.3390\/agriculture15131341"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liang, Y., Li, Z., Shi, J., Zhang, N., Qin, Z., Du, L., Zhai, X., Shen, T., Zhang, R., and Zou, X. (2025). Advances in Hyperspectral Imaging Technology for Grain Quality and Safety Detection: A Review. Foods, 14.","DOI":"10.3390\/foods14172977"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2569166","DOI":"10.1080\/14735903.2025.2569166","article-title":"Optical Imaging for Food Grain Quality Evaluation\u2014Recent Advances and Future Perspectives","volume":"23","author":"Ageh","year":"2025","journal-title":"Int. J. Agric. Sustain."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140062","DOI":"10.1016\/j.foodchem.2024.140062","article-title":"Optimizing Feature Selection with Gradient Boosting Machines in PLS Regression for Predicting Moisture and Protein in Multi-Country Corn Kernels via NIR Spectroscopy","volume":"456","author":"Zheng","year":"2024","journal-title":"Food Chem."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, C., Li, X., Xing, L., Lv, M., He, H., Pan, L., and Ou, X. (2025). Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour. Foods, 14.","DOI":"10.3390\/foods14132393"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.15302\/J-FASE-2019255","article-title":"Factors Influencing Near Infrared Spectroscopy Analysis of Agro-Products: A Review","volume":"6","author":"Xu","year":"2019","journal-title":"Front. Agric. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105134","DOI":"10.1016\/j.jfca.2023.105134","article-title":"Rapid Determination of Protein, Starch and Moisture Content in Wheat Powder by Near-Infrared Hyperspectral Imaging","volume":"117","author":"Zhang","year":"2023","journal-title":"J. Food Compos. Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112759","DOI":"10.1016\/j.isci.2025.112759","article-title":"A Review on Spectral Data Preprocessing Techniques for Machine Learning and Quantitative Analysis","volume":"28","author":"Yan","year":"2025","journal-title":"iScience"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1111\/pbr.13265","article-title":"Phenomic Prediction Can Be Improved by Optimization of NIRS Preprocessing","volume":"144","author":"Braun","year":"2025","journal-title":"Plant Breed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"116045","DOI":"10.1016\/j.trac.2020.116045","article-title":"New Data Preprocessing Trends Based on Ensemble of Multiple Preprocessing Techniques","volume":"132","author":"Mishra","year":"2020","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104969","DOI":"10.1016\/j.infrared.2023.104969","article-title":"Rapid Determination of Rice Protein Content Using Near-Infrared Spectroscopy Coupled with Feature Wavelength Selection","volume":"135","author":"Liu","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103937","DOI":"10.1016\/j.infrared.2021.103937","article-title":"Variable Selection in Near-Infrared Spectra: Application to Quantitative Non-Destructive Determination of Protein Content in Wheat","volume":"119","author":"Huan","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., Taha, M.F., Wei, X., He, Y., Qiu, Z., and Liu, Y. (2020). Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.575810"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ren, J., Xiong, Y., Chen, X., and Hao, Y. (2024). Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS. Sensors, 24.","DOI":"10.20944\/preprints202408.0024.v1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2581384","DOI":"10.1080\/10942912.2025.2581384","article-title":"Accurate Prediction of Kiwifruit Soluble Solids Content Using an Optimized 1D-CNN Model with Near-Infrared Spectroscopy","volume":"28","author":"Geng","year":"2025","journal-title":"Int. J. Food Prop."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"123933","DOI":"10.1016\/j.saa.2024.123933","article-title":"Wavelength Selection Method for Near-Infrared Spectroscopy Based on Max-Relevance Min-Redundancy","volume":"310","author":"Ma","year":"2024","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1002\/fsn3.652","article-title":"Determination of Soybean Routine Quality Parameters Using Near-Infrared Spectroscopy","volume":"6","author":"Zhu","year":"2018","journal-title":"Food Sci. Nutr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"141053","DOI":"10.1016\/j.foodchem.2024.141053","article-title":"Fusion of Convolutional Neural Network with XGBoost Feature Extraction for Predicting Multi-Constituents in Corn Using Near Infrared Spectroscopy","volume":"463","author":"Zou","year":"2025","journal-title":"Food Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104120","DOI":"10.1016\/j.infrared.2022.104120","article-title":"Comparative Study of Different Wavelength Selection Methods in the Transfer of Crop Kernel Qualitive Near-Infrared Models","volume":"123","author":"Xu","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xiang, J., Huang, Y., Guan, S., Shang, Y., Bao, L., Yan, X., Hassan, M., Xu, L., and Zhao, C. (2023). A Sustainable Way to Determine the Water Content in Torreya grandis Kernels Based on Near-Infrared Spectroscopy. Sustainability, 15.","DOI":"10.3390\/su151612423"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1177\/0967033519891594","article-title":"A Partial Least Squares-Based Approach to Assess the Light Penetration Depth in Wheat Flour by Near Infrared Hyperspectral Imaging","volume":"28","author":"Laborde","year":"2020","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jones, S.B., Sheng, W., and Or, D. (2022). Dielectric Measurement of Agricultural Grain Moisture\u2014Theory and Applications. Sensors, 22.","DOI":"10.3390\/s22062083"}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/17\/3093\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T04:26:43Z","timestamp":1788841603000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/15\/17\/3093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":32,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["foods15173093"],"URL":"https:\/\/doi.org\/10.3390\/foods15173093","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}