|
|
|
|
|
|
| Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy |
| ZHANG Xu1, ZHANG Tian-gang2, MU Wei-song1, FU Ze-tian2,3, ZHANG Xiao-shuan2,3* |
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. College of Engineering, China Agricultural University, Beijing 100083, China
3. Beijing Laboratory of Food Quality and Safety, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract The maturity of wine grape is an important quality index to determine the harvest time of grape. Aiming at the problem that the maturity of wine grapeis difficult to be detected in the field, the internal relationship between SSC and spectral data of wine grape was studied by Vis/NIR spectroscopy and chemometrics. The Vis/NIR spectral data of five varieties of grape and their leaves in different mature periods were obtained by USB2000+spectrometer. The spectral data were extracted by OMNIC 8.0 software, and the chemical values and spectral absorption values were modeled by TQ analyst 8.0 software. The wave band 450~1 000 nm which had high signal-to-noise ratio was selected, and PCA was adopted to eliminate the abnormal spectral data. The first derivative (FD), Savitzky-Golay smoothing (S-G), multiple scattering correction (MSC) and standard normal variate (SNV) were combined into four methods to preprocess the spectral data. Based on the spectral data of five varieties of grape berry and the spectral data of five varieties of grape leaf, the prediction models of SSC were established by PLS. The model effects with different pretreatment methods were compared, and the optimal pretreatment method was selected for modeling. Finally, the prediction models of SSC were verified by external samples. The results show that the performance of most prediction models is the best when S-G smoothing+FD+MSC preprocessing method is applied. The correlation coefficient of calibration sets and validation sets of grape berries were above 0.93 and 0.86, respectively, and the maximum root means square error is 0.30 and 0.48, respectively. The correlation coefficient of calibration sets and validation sets of grape leaves were above 0.73 and 0.65, respectively, the maximum root mean square error is 0.95 and 0.75, respectively. The highest average relative error between the predicted value and the real value of grape berry samples was 0.43%. The SSC prediction model built by the spectra of grape berry has a good predictive ability, which is superior to the SSC prediction model built by the spectra of the grape leaf. The prediction model of SSC can provide a theoretical reference for the study of grape maturity evaluation. Therefore, Vis/NIR spectroscopy is suitable for rapid and non-destructive detection of solid soluble content in the wine grape field.
|
|
Received: 2019-12-24
Accepted: 2020-05-10
|
|
|
|
Corresponding Authors:
ZHANG Xiao-shuan
E-mail: zhxshuan@cau.edu.cn
|
|
[1] LIU Xu, CHEN Min, WU Xuan, et al(刘 旭，陈 敏，武 轩，等). Food Science(食品科学), 2016, 37(22): 230.
[2] CHEN Chen, LU Xiao-xiang, ZHANG Peng, et al(陈 辰，鲁晓翔，张 鹏，等). Food Science(食品科学), 2015, 36(20): 109.
[3] Ignat T, Schmilovitch Z, Feföldi J, et al. Biosystems Engineering, 2013, 114(4): 414.
[4] Dong X, Dong J, Li Y, et al. Computers and Electronics in Agriculture, 2019, 156: 669.
[5] ZHANG Jing-jing, LIU Gui-shan, REN Ying-chun, et al(张晶晶，刘贵珊，任迎春，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(6): 1909.
[6] Qiao L, Tang X, Dong J. Food Chemistry, 2017, 237: 1179.
[7] Ouyang Q, Liu Y, Chen Q, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2017, 180: 91.
[8] ZHENG Xiao-chun, LI Yong-yu, PENG Yan-kun, et al(郑晓春，李永玉，彭彦昆，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(S1): 332.
[9] Jie D, Xie L, Rao X, et al. Postharvest Biology and Technology, 2014, 90: 1.
[10] Li M, Han D, Liu W. Biosystems Engineering, 2019, 188: 31. |
| [1] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
| [2] |
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. |
| [3] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
| [4] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [5] |
LI Xiong1, 2, LIU Yan-de1, 2*, WANG Guan-tian1, JIANG Xiao-gang1, 2. Grapefruit Light Energy Decay Law and Analysis of the Effect of
Transmission Depth on Model Accuracy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2574-2580. |
| [6] |
LUO Dong-jie, WANG Meng, ZHANG Xiao-shuan, XIAO Xin-qing*. Vis/NIR Based Spectral Sensing for SSC of Table Grapes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2146-2152. |
| [7] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
| [8] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
| [9] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
| [10] |
YAN Zhong-wei1, 2, 3, TIAN Xi2, 3, ZHANG Yi-fei2, 3, LI Lian-jie2, 3, LIU San-qing1, 2, 3, HUANG Wen-qian2, 3*. Online Detection of Soluble Solids Content in Different Parts of
Watermelons Based on Full Transmission Near Infrared
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1800-1808. |
| [11] |
WANG Dong1, 2, FENG Hai-zhi3, LI Long3, HAN Ping1, 2*. Compare of the Quantitative Models of SSC in Tomato by Two Types of NIR Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1351-1357. |
| [12] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
| [13] |
XU Wei-xin, XIA Jing-jing, WEI Yun, CHEN Yue-yao, MAO Xin-ran, MIN Shun-geng*, XIONG Yan-mei*. Rapid Determination of Oxytetracycline Hydrochloride Illegally Added in Cattle Premix by ATR-FTIR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 842-847. |
| [14] |
LI Zi-yi1, LI Rui-lan1, LI Can-lin1, WANG Ke-ru2, FAN Jiu-yu3, GU Rui1*. Identification of Tibetan Medicine Zhaxun by Infrared Spectroscopy
Combined With Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 526-532. |
| [15] |
HAN Min-jie, WANG Xiang-you, XU Ying-chao*, CUI Ying-jun, LÜ Dan-yang. Research on the Factors Influencing the Non-Destructive Detection of
Potatoes by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 37-42. |
|
|
|
|
