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.
Key words：Visible/near infrared spectroscopy; Maturity of wine grape; Partial least square method; Soluble solids content
张 旭，张天罡，穆维松，傅泽田，张小栓. 基于Vis/NIR光谱技术的酿酒葡萄成熟期间SSC预测研究[J]. 光谱学与光谱分析, 2021, 41(01): 229-235.
ZHANG Xu, ZHANG Tian-gang, MU Wei-song, FU Ze-tian, ZHANG Xiao-shuan. Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 229-235.
[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.
