基于近红外光谱技术设计了“蒙阴蜜桃”内部品质在线无损检测分级系统。基于该系统在移动速度5个/s下,建立对黄桃的可溶性固形物含量(solid soluble contents,SSC)和硬度在线检测模型。采用SGS(savitzky-golay smooth)、SG-DER (savitzky-golay derivative)及等多种光谱预处理方法对光谱图进行处理,基于偏最小二乘法(partial least squares,PLS),建立不同区间段黄桃的SSC和硬度模型。结果表明,在600~750 nm和750~900 nm下,采用SG-DER预处理方法,建立SSC模型性能最好,其校正集和验证集相关系数分别为0.919和0.863,均方根误差分别为0.735%和0.764%;采用SGS处理光谱,建立硬度模型性能最好,其校正集和验证集相关系数分别为0.832和0.746,方根误差分别为0.774 N和0.785 N。后采用遗传算法(genetic algorithm, GA)和连续投影算法(successive projections algorithm,SPA)筛选600~750 nm和750~900 nm特征变量,采用SG-DER处理光谱,建立SSC的预测模型;采用SGS处理光谱,建立硬度预测模型。从建模效果来看,SPA和GA都可以有效减少建模所用变量数、提高黄桃在线SSC和硬度检测模型的预测能力和运算速度,而采用SPA-PLS建立SSC和硬度模型优于GA-PLS,其SSC预测集相关系数和预测均方根误差分别为0.916、0.721%,其硬度预测集相关系数和预测均方根误差分别为0.811、0.742 N。研究表明,采用近红外漫透射光谱技术能够很好地实现黄桃SSC和硬度的在线无损检测。
An online, non-destructive internal quality inspection/grading system was designed for Mengyin peaches using near-infrared spectroscopy. Online models to detect soluble solid content (SSC) and firmness of Mengyin peaches were established in keeping with the sorting system’s at a speed of 5 fruits/second. Spectra were pre-processed using methods including Savitzky-Golay smoothing (SGS) and Savitzky-Golay derivative (SG-DER) calculations. SSC and firmness prediction models were established for different wavelength ranges using partial least squares (PLS) fitting. The results showed that, in the construction of SSC prediction model, SG-DER pre-processing was optimum in the range of 600-750 nm and 750-900 nm wavelength. Correlation coefficients of calibration and validation sets were 0.919 and 0.863, and root mean square errors were 0.735 and 0.764%, respectively. SGS used for pre-processing was suitable in firmness prediction model construction. And the correlation coefficients of calibration and validation sets were 0.832 and 0.746, and root mean square errors were 0.774 N and 0.785 N, respectively. Furthermore, a genetic algorithm (GA) and a successive projections algorithm (SPA) were used to screen characteristic variables in the 600-750 nm and 750-900 nm wavelength ranges, and SSC and firmness prediction models were established with the respective use of SG-DER and SGS for spectral pre-preprocessing. The results revealed that both SPA and GA could effectively reduce the number of variables used in model construction. And they also could enhance the predictive ability and computation speed of online SSC and firmness prediction models. SSC and firmness prediction models established with SPA-PLS were better than those GA-PLS did. The correlation coefficient and root mean square error of the SSC prediction set were 0.916% and 0.721 %, respectively. Moreover, the correlation coefficient and root mean square error of the firmness prediction set were 0.811 and 0.742 N, respectively. Thus, on-line non-destructive detection of SSC and firmness of Mengyin peaches could be realized with near-infrared diffuse-transmission spectroscopy.
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