Ping Fengjiao, Yang Jihong, Zhou Xuejian, Su Yuan, Ju Yanlun, Fang Yulin, Bai Xuebing, Liu Wenzheng
College of Enology, Northwest A&F University, Yangling 712100, China.
Shaanxi Engineering Research Center for Viti-Viniculture, Yangling 712100, China.
Foods. 2023 Jun 14;12(12):2364. doi: 10.3390/foods12122364.
Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness () and Chroma () and soluble solids (SSC) content and decreasing values in color of lightness (), yellowness/blueness () and Hue angle (*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration (RCal2) and prediction (RPre2) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.
成熟度显著影响水果的商业价值和销量。为了监测葡萄在成熟过程中品质参数的变化，本研究采用了一种快速无损的可见-近红外光谱（Vis-NIR）技术方法。首先，探究了四个不同成熟阶段葡萄的理化性质。数据表明，随着成熟度的提高，葡萄的红度/绿度（）、色度（）和可溶性固形物（SSC）含量增加，而明度（）、黄度/蓝度（）、色相角（*）、硬度和总酸（TA）含量降低。基于这些结果，建立了葡萄中SSC和TA的光谱预测模型。通过竞争性自适应加权算法（CARS）选择有效波长，并应用六种常见的预处理方法对光谱数据进行预处理。基于有效波长和全光谱，采用偏最小二乘回归（PLSR）建立模型。利用全光谱数据和一阶导数预处理建立的预测PLSR模型在SSC和TA方面均提供了最佳的性能参数值。对于SSC，该模型校准集（RCal2）和预测集（RPre2）的决定系数分别为0.97和0.93，校准集（RMSEC）和预测集（RMSEP）的均方根误差分别为0.62和1.27；RPD等于4.09。对于TA，RCal2、RPre2、RMSEC、RMSEP和RPD的最佳值分别为0.97、0.94、0.88、1.96和4.55。结果表明，可见-近红外光谱是快速无损检测葡萄中SSC和TA的有效工具。
