|
|
|
|
|
|
Research Progress of Universal Model of Near-Infrared Spectroscopy in Agricultural Products and Foods Detection |
LI Ming1*, HAN Dong-hai2*, LU Ding-qiang1, LU Xiao-xiang1, CHAI Chun-xiang1, LIU Wen3, SUN Ke-xuan1 |
1. School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, China
2. College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
3. School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
|
|
|
Abstract China has a large population, and the demand for agricultural products and food is great and diverse. Moreover, the quality and safety of agricultural products and foods are closely related to people’s daily life. Therefore, it is the development needs of contemporary society to use nondestructive, rapid, environmentally friendly and high-through put testing methods to detect the quality of agricultural products and foods. The traditional detection and analysis methods have some disadvantages, such as time and labor consumption, the tested samples cannot be sold again after testing and the defective products missing detection. As a rapid and nondestructive detection method, near-infrared spectroscopy (NIR) has been paid more and more attention by some scholars and related industry personnel. However, most NIR analysis methods only build mathematical models for single material. For a large number and variety of agricultural products and foods, such as different regions, different years, different temperatures, different processing methods, different components and even different varieties, this relatively traditional NIR analysis method will undoubtedly increase the workload of preliminary modeling. With the development of computer technology, spectrometer hardware, stoichiometry and internet technology, relevant scholars have begun researching and developing a universal NIR model to solve this problem. That is to establish a near-infrared universal model, which can detect the same index or multiple indexes of various materials. Compared with the traditional NIR model, the universal model has the advantages of low modeling cost and small workload, which makes the application and promotion of NIR spectroscopy technology in agricultural products and foods field of great significance. This paper reviews the research on the universal model of NIR in detecting agricultural products and foods. By comparing the traditional model modeling method with the universal model modeling method, the methods used in the three modeling steps of sample information acquisition, model establishment and sample information prediction in building the universal model are summarized. At the same time, the main points of modeling of NIR universal model in agricultural products and foods detection are summarized. Currently, the research of the NIR universal model in agricultural products and food quality detection is still in the development stage. In this paper, some suggestions on the development and research of the universal model are proposed, and the development trend of the NIR universal model in agricultural products and foods detection has further prospected.
|
Received: 2022-03-27
Accepted: 2022-06-15
|
|
Corresponding Authors:
LI Ming, HAN Dong-hai
E-mail: liming@tjcu.edu.cn;handh@cau.edu.cn
|
|
[1] WANG Jian-wei, TAO Fei(王建伟,陶 飞). Anhui Agricultural Science Bulletin(安徽农学通报), 2021, 27(17): 155.
[2] Norris K H, Hart J R. Principles & Methods of Measuring Moisture Content in Liquides & Solids, 1997, 4(1): 19.
[3] Birth G S, Dull G G, Renfroe W T, et al. JAmerSocHortSci, 1985, 110(2): 297.
[4] TIAN Hua(田 华). Science and Technology of Food Industry(食品工业科技), 2021, 42(18): 41.
[5] LIN Fang, WU Li-hua, LIU Man-man, et al(林 房, 吴丽华, 刘曼曼,等). Liquor Making(酿酒), 2020, 47(1): 12.
[6] LI Chang-bin, XIAO Zhong-shan(李长滨, 肖忠闪). Modern Animal Husbandry(现代牧业), 2020, 4(2): 51.
[7] YAN Yan-lu, CHEN Bin, ZHU Da-zhou(严衍禄, 陈 斌, 朱大洲). Near Infrared Spectroscopy-Principles, Technologys and Applications(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京:中国轻工业出版社), 2013.
[8] WANG Yan-ni, SUN Qi, XIAO Zhi-ming, et al(王燕妮, 孙 琦, 肖志明, 等). Quality and Safety of Agro-Products(农产品质量与安全), 2016, (6): 55.
[9] Kawano, Sumio. Journal of Near Infrared Spectroscopy, 2014, 22(5): 367.
[10] Fan S, Zhang B, Li J, et al. Biosystems Engineering, 2016, 143: 9.
[11] Stone R W K A. Technometrics, 1969, 11(1): 137.
[12] Wang J, Wang J, Chen Z, et al. Postharvest Biology and Technology, 2017, 129: 143.
[13] Galvão R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736.
[14] MA Hui, FENG Xue-jing, CHEN Ming, et al(马 卉, 冯雪静, 陈 明, 等). Chinese Journal of Modern Applied Pharmacy(中国现代应用药学), 2021, 38(23): 2932.
[15] CHANG Dong, ZHANG Sen, WANG Feng-xiang, et al(常 冬, 张 森, 王凤香, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2017, 32(8): 136.
[16] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press (北京:化学工业出版社), 2011.
[17] Liu R, Qi S, Han D. Journal of Near Infrared Spectroscopy, 2015, 23(5): 301.
[18] PENG Dan, LI Lin-qing, LIU Ya-li, et al(彭 丹, 李林青, 刘亚丽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(6): 1828.
[19] Saranwong S, Thanapase W, Suttiwijitpukdee N, et al. Journal of Near Infrared Spectroscopy, 2010, 18(4): 271.
[20] Li X, Wu Z, Xin F, et al. Pharmacognosy Magazine, 2017, 13(49): 193.
[21] Zhang D, Xu L, Wang Q, et al. Food Analytical Methods, 2018, 12: 1.
[22] Li M, Han D, Liu W. Biosystems Engineering, 2019, 188: 31.
[23] ZHOU Ting, LIU Miao-miao, MAO Fei, et al(周 婷, 刘苗苗, 毛 飞, 等). Journal of Food Safety & Quality(食品安全质量检测学报), 2020, 11(11): 3460.
[24] ZHANG De-hu(张德虎). Jiangsu Agricultural Sciences(江苏农业科学), 2020, 48(16): 235.
[25] CHU Xiao-li(褚小立). Practical Manual of Near Infrared Spectral Analysis Techniques(近红外光谱分析技术实用手册). Beijing: China Machine Press(北京:机械工业出版社), 2016.
[26] Powiardowski W, Szulc J, Gozdecka G. Czech Journal of Food Sciences, 2020, 38(2): 131.
[27] Elfadl E, Reinbrechta C, Claupein W, et al. International Journal of Plant Production, 2010, 4:259.
[28] Torres I, Pérez-Marín D, María-José D, et al. Biosystems Engineering, 2017, 153: 140.
[29] Pérez-Marín D, Garrido-Varo A, Guerrero J E. Talanta, 2007, 72(1): 28.
[30] Huang Y, Dong W, Alireza S, et al. Computers and Electronics in Agriculture, 2020, 173: 1.
[31] ZHANG Jin, CAI Wen-sheng, SHAO Xue-guang(张 进, 蔡文生, 邵学广). Progress in Chemistry (化学进展), 2017, 29(8): 902.
[32] Wongsaipun S, Theanjumpol P, Kittiwachana S. Food Analytical Methods, 2021, 14(5): 997.
[33] Boulet J C, Roger J M. Chemometrics & Intelligent Laboratory Systems, 2012, 117: 61.
[34] LI Xiao-yu, ZHONG Xiong-bin, LIU Shan-mei, et al(李小昱, 钟雄斌, 刘善梅, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2014, (9): 216.
[35] GUO Zhi-ming, WANG Jun-yi, SONG Ye, et al(郭志明, 王郡艺, 宋 烨, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(22): 271. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[4] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[6] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[7] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[8] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[9] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[10] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[12] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[13] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[14] |
MU Da1, 2, WANG Qi-shu1, 2*, CUI Zong-yu1, 2, REN Jiao-jiao1, 2, ZHANG Dan-dan1, 2, LI Li-juan1, 2, XIN Yin-jie1, 2, ZHOU Tong-yu3. Study on Interference Phenomenon in Terahertz Time Domain
Spectroscopy Nondestructive Testing of Glass Fiber Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3031-3040. |
[15] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
|
|
|
|