| [1] |
Zeng X, Lin J, Chen L, Zhang C, Luo S, et al. 2024. A study of polysaccharides content and antioxidant activities in vitro of different varieties of grape. Sichuan University of Arts and Science Journal 34(2):38−43 doi: 10.3969/j.issn.1674-5248.2024.02.006 |
| [2] |
Tian Y, Chen GM, Li JF, Xiang XY, Liu Y, et al. 2018. Present development of grape industry in the world. Chinese Journal of Tropical Agriculture 38(6):96−101+105 doi: 10.12008/j.issn.1009-2196.2018.06.020 |
| [3] |
Chen K, Fang YL, Wu WM, Zhang KK, Wang C, et al. 2019. Analysis and comparison of fruit quality of various shape grape varieties. Sino-Overseas Grapevine & Wine 44(2):7−13 doi: 10.13414/j.cnki.zwpp.2019.02.002 |
| [4] |
Wu H, Cai H, Ren Y, Wang T, Zhou J, et al. 2024. Non-destructive detection of soluble solid content based on visible-near infrared spectroscopy. The Journal of Light Scattering 36(1):44−51 doi: 10.13883/j.issn1004-5929.202401006 |
| [5] |
Ma W, Waffo-Téguo P, Jourdes M, Li H, Teissedre PL. 2018. First evidence of epicatechin vanillate in grape seed and red wine. Food Chemistry 259:304−10 doi: 10.1016/j.foodchem.2018.03.134 |
| [6] |
Rouxinol MI, Martins MR, Murta GC, Mota Barroso J, Rato AE. 2022. Quality assessment of red wine grapes through NIR spectroscopy. Agronomy 12(3):637 doi: 10.3390/agronomy12030637 |
| [7] |
Gomes VM, Fernandes AM, Faia A, Melo-Pinto P. 2017. Comparison of different approaches for the prediction of sugar content in new vintages of whole port wine grape berries using hyperspectral imaging. Computers and Electronics in Agriculture 140:244−254 doi: 10.1016/j.compag.2017.06.009 |
| [8] |
Lee WK, Choi JY, Kim BY, Memon A, Jeon KH. 2020. Changes in aroma pattern of campbell grapes by treatment of phytoncide pads derived from pine bark using GC-MSD electronic nose. Culinary Science & Hospitality Research 26(2):99−109 doi: 10.20878/cshr.2020.26.2.012 |
| [9] |
Wu M, Cai H, Cui X, Wei Z, Ke H. 2020. Fast inspection of fruits using nuclear magnetic resonance spectroscopy. Journal of the Chinese Chemical Society 67(10):1794−99 doi: 10.1002/jccs.201900458 |
| [10] |
Wen J, Xu G, Zhang A, Ma W, Jin G. 2024. Emerging technologies for rapid non-destructive testing of grape quality: A review. Journal of food composition and analysis 133:106446 doi: 10.1016/j.jfca.2024.106446 |
| [11] |
Ncama K, Tesfay SZ, Fawole OA, Opara UL, Magwaza LS. 2018. Non-destructive prediction of'Marsh'grapefruit susceptibility to postharvest rind pitting disorder using reflectance Vis/NIR spectroscopy. Scientia Horticulturae 231:265−71 doi: 10.1016/j.scienta.2017.12.028 |
| [12] |
Zhang X, Zhu Y, Zhao Y, Chen M, Sun Q, et al. 2023. Optimization of nondestructive testing method for soluble solid content of peach based on visible/near infrared spectroscopy. Acta Agriculture Zhejiangensis 35(7):1617−25 |
| [13] |
Munawar AA, Kusumiyati, Wahyuni D. 2019. Near infrared spectroscopic data for rapid and simultaneous prediction of quality attributes in intact mango fruits. Data in Brief 27:104789 doi: 10.1016/j.dib.2019.104789 |
| [14] |
Beghi R, Giovenzana V, Tugnolo A, Guidetti R. 2018. Application of visible/near infrared spectroscopy to quality control of fresh fruits and vegetables in large-scale mass distribution channels: a preliminary test on carrots and tomatoes. Journal of the Science of Food and Agriculture 98(7):2729−34 doi: 10.1002/jsfa.8768 |
| [15] |
dos Santos Costa D, Oliveros Mesa NF, Santos Freire M, Pereira Ramos R, Teruel Mederos BJ. 2019. Development of predictive models for quality and maturation stage attributes of wine grapes using VIS-NIR reflectance spectroscopy. Postharvest Biology and Technology 150(1):166−78 doi: 10.1016/j.postharvbio.2018.12.010 |
| [16] |
Xiao H, Sun K, Tu K, Pan L. 2019. Development and application of a specialized portable visible and near-infrared instrument for grape quality detection. Food Science 40(8):300−5 doi: 10.7506/spkx1002-6630-20171124-305 |
| [17] |
Zhang X, Zhang T, Mu W, Fu Z, Zhang X. 2021. Prediction of soluble solids content for wine grapes during maturing based on visible and near-infrared spectroscopy. Spectroscopy and Spectral Analysis 41(1):229−35 |
| [18] |
Liu W, Zhou X, Ping F, Su Y, Ju Y, et al. 2024. Detection of key indicators of ripening quality in table grapes based on visible-near-infrared spectroscopy. Transactions of the Chinese Society for Agricultural Machinery 55(2):373−83 doi: 10.6041/j.issn.1000-1298.2024.02.037 |
| [19] |
Zhou X, Yang J, Su Y, He K, Fang Y, et al. 2024. Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: introducing a novel quantitative index. Postharvest Biology and Technology 218:113131 doi: 10.1016/j.postharvbio.2024.113131 |
| [20] |
Cai J, Huang C, Ma L, Zhai L, Guo Z. 2024. Hand-held visible/near infrared nondestructive detection system for soluble solid content in mandarin by 1D-CNN. Spectroscopy and Spectral Analysis 43(9):2792−98 |
| [21] |
Zhu W, Huang W, Zhu Q, Fan S. 2024. Development of a handheld yellow nectarine soluble solid content detection device based on Vis/NIR spectroscopy. Transactions of the Chinese Society of Agricultural Engineering 40(1):286−92 doi: 10.11975/j.issn.1002-6819.202306134 |
| [22] |
Puertas G, Vázquez M. 2019. Fraud detection in hen housing system declared on the eggs' label: an accuracy method based on UV-VIS-NIR spectroscopy and chemometrics. Food Chemistry 288:8−14 doi: 10.1016/j.foodchem.2019.02.106 |
| [23] |
Lu B, Liu N, Li H, Yang K, Hu C, et al. 2019. Quantitative determination and characteristic wavelength selection of available nitrogen in coco-peat by NIR spectroscopy. Soil & Tillage Research 191:266−74 doi: 10.1016/j.still.2019.04.015 |
| [24] |
Zhang L, Li Y, Huang W, Ni L, Ge J. 2020. The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 227:117647 doi: 10.1016/j.saa.2019.117647 |
| [25] |
Zheng Y, Cao Y, Yang J, Xie L. 2023. Enhancing model robustness through different optimization methods and 1-D CNN to eliminate the variations in size and detection position for apple SSC determination. Postharvest Biology and Technology 205:112513 doi: 10.1016/j.postharvbio.2023.112513 |
| [26] |
Li Y, Ma B, Li C, Yu G. 2022. Accurate prediction of soluble solid content in dried Hami jujube using SWIR hyperspectral imaging with comparative analysis of models. Computers and Electronics in Agriculture 193:106655 doi: 10.1016/j.compag.2021.106655 |
| [27] |
Yu S, Huan K, Liu X. 2023. Application of quantitative non-destructive determination of protein in wheat based on pretreatment combined with parallel convolutional neural network. Infrared Physics Technology 135:104958 doi: 10.1016/j.infrared.2023.104958 |
| [28] |
Suktanarak S, Teerachaichayut S. 2017. Non-destructive quality assessment of hens'eggs using hyperspectral images. Journal of Food Engineering 215:97−103 doi: 10.1016/j.jfoodeng.2017.07.008 |
| [29] |
Zhang J, Du M, Li Y, Liu Z, Li X, et al. 2020. Research on fresh-keeping technology of table grapes with variable temperature and controlled atmosphere. Food Research and Development 41(5):80−84+103 doi: 10.12161/j.issn.1005-6521.2020.05.013 |
| [30] |
Lin J, Meng Q, Wu Z, Chang H, Ni C, et al. 2024. Fruit soluble solids content non- destructive detection based on visible/near infrared hyperspectral imaging in mango. Journal of Fruit Science 41(1):122−32 doi: 10.13925/j.cnki.gsxb.20230269 |
| [31] |
Yang J, Luo X, Zhang X, Passos D, Xie L, et al. 2022. A deep learning approach to improving spectral analysis of fruitquality under interseason variation. Food Control 140:109108 doi: 10.1016/j.foodcont.2022.109108 |
| [32] |
He K, Jing B, Tang X. 2022. Combination of airflow and multi-point laser ranging technique for the prediction of total volatile basic nitrogen content in beef. Journal of Food Measurement and Characterization 16:3095−105 doi: 10.1007/s11694-022-01388-0 |
| [33] |
Sun T, Xu W, Hu T, Liu M. 2013. Determination of soluble solids content in Nanfeng mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM. Spectroscopy and Spectral Analysis 33:3235−39 doi: 10.3964/j.issn.1000-0593(2013)12-3235-05 |
| [34] |
Li H, Liang Y, Xu Q, Gao D. 2009. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta 648:77−84 doi: 10.1016/j.aca.2009.06.046 |
| [35] |
Siedliska A, Baranowski P, Mazurek W. 2014. Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data. Computers and Electronics in Agriculture 106:66−74 doi: 10.1016/j.compag.2014.05.012 |
| [36] |
Raj R, Walker JP, Vinod V, Pingale R, Naik B, et al. 2021. Leaf water content estimation using top-of-canopy airborne hyperspectral data. International Journal of Applied Earth Observation and Geoinformation 102:102393 doi: 10.1016/j.jag.2021.102393 |