Application of Portable Near-Infrared Spectroscopy for Quantitative Prediction of Protein Content in Torreya grandis Kernels Under Different States
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| 出版年: | Foods vol. 14, no. 11 (2025), p. 1847 |
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| 第一著者: | |
| その他の著者: | , , , , , , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 024 | 7 | |a 10.3390/foods14111847 |2 doi | |
| 035 | |a 3217732199 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Gu Yuqi |u College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China; guyuqi@zafu.edu.cn (Y.G.); 2023612021010@stu.zafu.edu.cn (K.L.); 15968131641@163.com (Y.H.); 20080094@zafu.edu.cn (L.Y.) | |
| 245 | 1 | |a Application of Portable Near-Infrared Spectroscopy for Quantitative Prediction of Protein Content in <i>Torreya grandis</i> Kernels Under Different States | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Protein content is a key quality indicator in nuts, influencing their color, taste, storage, and processing properties. Traditional methods for protein quantification, such as the Kjeldahl nitrogen method, are time-consuming and destructive, highlighting the need for rapid, convenient alternatives. This study explores the feasibility of using portable near-infrared spectroscopy (NIRS) for the quantitative prediction of protein content in Torreya grandis (T. grandis) kernels by comparing different sample states (with shell, without shell, and granules). Spectral data were acquired using a portable NIR spectrometer, and the protein content was determined via the Kjeldahl nitrogen method as a reference. Outlier detection was performed using principal component analysis combined with Mahalanobis distance (PCA-MD) and concentration residual analysis. Various spectral preprocessing techniques and partial least squares regression (PLSR) were applied to develop protein prediction models. The results demonstrated that portable NIRS could effectively predict protein content in T. grandis kernels, with the best performance being achieved using granulated samples. The optimized model (1Der-SNV-PLSR-G) significantly outperformed models based on whole kernels (with or without shell), with determination coefficients for the calibration set (<inline-formula>Rc2</inline-formula>) and prediction set (<inline-formula>Rp2</inline-formula>) of 0.92 and 0.86, respectively, indicating that the sample state critically influenced prediction accuracy. This study confirmed the potential of portable NIRS as a rapid and convenient tool for protein quantification in nuts, offering a practical alternative to conventional methods. The findings also suggested its broader applicability for quality assessment in other nuts and food products, contributing to advancements in food science and agricultural technology. | |
| 651 | 4 | |a China | |
| 653 | |a Outliers (statistics) | ||
| 653 | |a Data acquisition | ||
| 653 | |a Food | ||
| 653 | |a Nuts | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Nitrogen | ||
| 653 | |a Agricultural technology | ||
| 653 | |a Least squares method | ||
| 653 | |a Feasibility studies | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Prediction models | ||
| 653 | |a Infrared spectra | ||
| 653 | |a Infrared spectroscopy | ||
| 653 | |a Proteins | ||
| 653 | |a Nutrient content | ||
| 653 | |a Data analysis | ||
| 653 | |a Quality assessment | ||
| 653 | |a Quality standards | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Near infrared radiation | ||
| 653 | |a Quality control | ||
| 653 | |a Food processing | ||
| 653 | |a Particle size | ||
| 653 | |a Kernels | ||
| 653 | |a Information processing | ||
| 653 | |a Portability | ||
| 653 | |a Torreya grandis | ||
| 700 | 1 | |a Zhong Haosheng |u Zhoushan Special Equipment Inspection Research Institute, Zhoushan 316021, China; 13656800858@163.com | |
| 700 | 1 | |a Wu, Jianhua |u Panzhihua Academy of Agriculture and Forestry Sciences, Panzhihua 617061, China; jhuawu2024@163.com | |
| 700 | 1 | |a Li Kaixuan |u College of Optical, Mechanical and Electrical Engineering, Zhejiang A&amp;F University, Hangzhou 311300, China; guyuqi@zafu.edu.cn (Y.G.); 2023612021010@stu.zafu.edu.cn (K.L.); 15968131641@163.com (Y.H.); 20080094@zafu.edu.cn (L.Y.) | |
| 700 | 1 | |a Huang, Yu |u College of Optical, Mechanical and Electrical Engineering, Zhejiang A&amp;F University, Hangzhou 311300, China; guyuqi@zafu.edu.cn (Y.G.); 2023612021010@stu.zafu.edu.cn (K.L.); 15968131641@163.com (Y.H.); 20080094@zafu.edu.cn (L.Y.) | |
| 700 | 1 | |a Fang Huimin |u School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; fanghuimin@ujs.edu.cn | |
| 700 | 1 | |a Hassan, Muhammad |u U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology, Islamabad 44000, Pakistan; hassan@uspcase.nust.edu.pk | |
| 700 | 1 | |a Yao Lijian |u College of Optical, Mechanical and Electrical Engineering, Zhejiang A&amp;F University, Hangzhou 311300, China; guyuqi@zafu.edu.cn (Y.G.); 2023612021010@stu.zafu.edu.cn (K.L.); 15968131641@163.com (Y.H.); 20080094@zafu.edu.cn (L.Y.) | |
| 700 | 1 | |a Zhao, Chao |u College of Optical, Mechanical and Electrical Engineering, Zhejiang A&amp;F University, Hangzhou 311300, China; guyuqi@zafu.edu.cn (Y.G.); 2023612021010@stu.zafu.edu.cn (K.L.); 15968131641@163.com (Y.H.); 20080094@zafu.edu.cn (L.Y.) | |
| 773 | 0 | |t Foods |g vol. 14, no. 11 (2025), p. 1847 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3217732199/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3217732199/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3217732199/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |