基于Vis/NIR光谱技术的酿酒葡萄成熟期间SSC预测研究

Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy

酿酒葡萄成熟度是确定葡萄采收期的重要品质指标,针对酿酒葡萄大田中成熟度检测难度大的问题,利用可见/近红外(Vis/NIR)光谱技术和化学计量学,研究了酿酒葡萄可溶性固形物含量(SSC)与光谱数据之间的内在联系。采用USB2000+光谱仪获取5种酿酒葡萄及其叶片在不同成熟时期的Vis/NIR光谱数据,通过OMNIC 8.0软件提取光谱数据,将化学值与光谱吸收率值通过TQ Analyst8.0软件建立模型。选取信噪比高的450~1000 nm波段,利用PCA剔除异常光谱数据,将一阶导数(FD)、Savitzky-Golay卷积平滑(S-G)、多元散射校正(MSC)、标准正态变换(SNV)分别组合共4种方法用于光谱数据预处理。利用偏最小二乘(PLS)法分别建立了5种葡萄基于酿酒葡萄光谱数据的SSC预测模型,建立了5种葡萄基于冠层叶片光谱数据的SSC预测模型,对比了不同方式预处理后的建模效果,并选择最优预处理方式建模。最后用外部样本分别验证了SSC预测模型。结果表明,采用S-G平滑+FD+MSC的预处理方法时大多数预测模型性能达到最好。5种葡萄浆果校正集和验证集的R分别达到0.93和0.86以上,最高均方根误差分别为0.30和0.48,5种葡萄冠层叶片校正集和验证集的R分别达到0.73和0.65以上,最大均方根误差分别为0.95和0.75。5种葡萄浆果外部试验样本预测值与真实值间的平均RE最高为0.43%。基于酿酒葡萄浆果光谱的SSC预测模型具备良好的预测能力,优于基于酿酒葡萄冠层叶片光谱的SSC预测模型,SSC预测模型能够为酿酒葡萄成熟度评价研究提供理论参考。Vis/NIR光谱技术适用于在酿酒葡萄大田中快速、无损检测SSC。

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 450~1000 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.