基于全透射近红外光谱的西瓜不同部位可溶性固形物含量在线检测研究

Online Detection of Soluble Solids Content in Different Parts of Watermelons Based on Full Transmission Near Infrared Spectroscopy

可溶性固形物含量(SSC)是评价西瓜果肉品质优劣的关键指标。西瓜SSC在线检测模型的建立,可以实现西瓜品质按其SSC进行在线分级,满足不同人群需求,提高市场竞争力。以160个京美2K西瓜为研究对象,通过实验室自主研发的在线检测设备,采集了西瓜两种姿态的可见近红外全透射光谱数据,分别与西瓜不同部位SSC建立偏最小二乘回归(PLSR)预测模型,探究西瓜SSC在线检测的最佳姿态和检测部位。首先,分别定义西瓜不同部位SSC测量值为瓜蒂糖、中心糖、瓜脐糖和整果糖,在线检测的两种姿态分别定义为T1姿态和T2姿态。其次对比西瓜不同部位SSC,探讨西瓜SSC评价标准。然后去除光谱透射强度值较低且频率较高,包含大量噪声和无用信息的光谱数据,最终选取波长范围(671~1116 nm)的光谱进行分析。采用卷积平滑(SGS)算法分别与多元散射校正(MSC)、单位矢量归一化(UVN)和标准正态变量变换(SNV)这3种算法相结合对两种姿态下的光谱数据进行预处理,随后对应西瓜不同部位SSC分别建立预测模型。通过对比不同模型的预测结果发现:使用SGS和MSC组合对T1姿态采集的光谱数据预处理效果最好,而对于T2姿态的光谱数据使用SGS与UVN结合预处理效果最好;T1姿态明显比T2姿态的光谱数据所建模型的预测效果好;对西瓜瓜蒂糖和整果糖的预测结果较好,瓜脐糖次之,中心糖最差。最后采用竞争性自适应重加权算法(CARS)分别对预测瓜蒂糖和整果糖的模型进行优化。其中,共挑选出81个波长点用于建立预测瓜蒂糖模型,106个波长点用于建立预测整果糖模型,两模型的预测集相关系数分别为0.8810和0.8758,均方根误差分别为0.8667%和0.7589%,不仅模型得到了简化,还提高了模型的预测精度。研究结果表明,西瓜不同姿态和对不同部位SSC预测的差异,会影响西瓜SSC在线检测和品质评价分级结果,应根据用户的实际需求进行模型选取和优化;为此,提出了糖度评价指数,为进一步开发西瓜SSC在线检测设备提供了技术支撑。

Soluble solids content(SSC)is the key indicator to evaluate the quality of watermelon pulp.In order to meet the needs of different groups of people and improve market competitiveness,an online detection model of watermelon SSC is established,which can realize the online grading of watermelon quality according to its SSC.In this paper,the 160 Jingmei2K watermelons are used as the research object,and the visible near-infrared full transmission spectrum data of the two postures of watermelons are collected using the online detection equipment independently developed by our laboratory.The partial least squares regression(PLSR)prediction model is established with the SSC of different parts of the watermelon to explore the best posture and part of online detection of watermelon SSC.Firstly,the SSC measurements of different parts of watermelon were defined as Pedicel Sugar,Central Sugar,Melon Navel Sugar and Average Sugar,and the two postures detected online were defined as T1 posture and T2 posture,respectively.Secondly,comparing the SSC of different parts of watermelon,the evaluation standard of watermelon SSC was discussed.Then,the spectral data with low transmission intensity and high frequency containing much noise and useless information were removed.Finally,the spectrum with a wavelength range(671~1116 nm)was selected for analysis.The Savitzky-Golay smoothing(SGS)algorithm is combined with multiplicative scatter correction(MSC),unit vector normalization(UVN)and standard normal variate transformation(SNV)to preprocess the spectral data under two postures.Then the prediction model is established for the SSC of different parts of watermelon.By comparing the prediction results of different models,it is found that the combination of SGS and MSC has the best preprocessing effect for T1 posture spectral data,while The spectral data of T2 posturehas better performance using SGS combined with UVN preprocessing methods.The prediction effect of the T1 pose is better than that of the T2 posture spectral data.The prediction results of Pedicel Sugar and Average Sugar are better than that of Melon Navel Sugar,and Central Sugar is the worst.Finally,competitive adaptive reweighted sampling(CARS)was used to optimize the prediction models of Pedicel Sugar and Average Sugar.81 and 106 wavelength points were selected to establish the prediction model of Pedicel Sugar and Average Sugar,respectively.The correlation coefficients of the prediction sets of the two models are 0.8810 and 0.8758,and the root mean square errors are 0.8667%and 0.7589%,respectively,simplifying the model and improving the prediction accuracy.The results showed that different postures and SSC prediction of different parts of a watermelon affected the results of online detection and quality evaluation.The model should be selected and optimized according to the actual needs of users.This paper,proposes an evaluation index for the online watermelon SCC detection,which provides a technical basis for further development of watermelon SSC online detection equipment.