SPXY算法的西瓜可溶性固形物近红外光谱检测

可溶性固形物(SSC)是一种综合参数，主要包括糖、酸、纤维素、矿物质等成分，对评价果实成熟度和品质具有重要意义，影响果实口感、风味及货架期。西瓜可溶性固形物含量的无损快速检测对西瓜成熟度的确定、贮藏及运输过程中西瓜内部品质监控具有十分重要的意义，有助于提高西瓜生产效益和市场竞争力。在西瓜可溶性固形物含量的快速无损近红外光谱检测中，近红外漫透射的方式所需光源的能量大，同时大功率透射会对水果的内部品质产生影响；采用近红外漫反射方式的研究较少，但漫反射采集所需的能量小，有助于实现仪器小型便携化，成本低，同时避免透射引起的水果品质变化。以小型西瓜为研究对象，利用JDSU便携式近红外光谱仪采集西瓜样品瓜梗、瓜脐、赤道部位的近红外反射光谱，在976，1 186和1 453 nm附近有明显的吸收，利用偏最小二乘回归定量分析方法建立西瓜可溶性固形物的近红外光谱无损预测模型。首先，采用光谱-理化值共生距离(SPXY)算法对西瓜不同检测部位的样品集进行划分，以可溶性固形物含量为y变量，光谱为x变量，利用两种变量同时计算样品间距离，以保证最大程度表征样本分布，有效地覆盖多维向量空间，增加样本间的差异性和代表性，提高模型稳定性。将西瓜样品划分为51个校正集和15个预测集，校正集样本的SSC含量涵盖了预测集样本的SSC含量范围，且变异系数均小于9%，样品集划分合理，有助于建立稳健可靠的预测模型。其次，对比分析西瓜瓜梗、瓜脐、赤道检测部位的近红外反射光谱与可溶性固形物含量之间的定量模型的预测精度，结果得出西瓜赤道部位的反射光谱与可溶性固形物含量相关性较高，预测效果较好，预测集相关系数为0.629，预测集均方根误差为0.49%。对于不同检测部位获取的光谱信息所建立的近红外光谱SSC预测模型的精度问题，一方面与光谱的采集方式有关，另一方面与西瓜的产地、品种、成熟期等因素引起的其性状上的差异有关。在模型建立过程中根据实际情况确定西瓜的检测部位。最后，为提高西瓜赤道部位近红外反射光谱与可溶性固形物含量之间的预测模型精度，采用光谱预处理方法进行优化，结果得出经标准归一化预处理后，建立的偏最小二乘回归预测模型效果最佳，预测集相关系数为0.864，预测集均方根误差为0.33%，模型相关性较好，预测精度得到了很大提升。研究结果表明，近红外反射光谱检测小型西瓜赤道部位能很好预测其可溶性固形物含量，为实际生产中近红外光谱无损快速检测西瓜可溶性固形物含量及小型便携式仪器研发提供了技术储备。

The NIR Detection Research of Soluble Solid Content in Watermelon Based on SPXY Algorithm

Soluble solid content (SSC), including sugar, acid, fibrin and mineral components, is a comprehensive index for evaluating the fruit maturity and quality, which can affect the taste, flavor and shelf life. Non-destructive and rapid detection of SSC in watermelon is very important for determining the maturity and monitoring the internal quality during storage and transportation, and is helpful to improve production efficiency and market competitiveness of watermelon. For the rapid and non-destructive near infrared (NIR)-based detection of the watermelon SSC, many researchers have used near infrared diffuse transmission method, which requires high light energy and high power transmission, and high power transmission will affect the internal quality. In contrast, the number of researches on near infrared diffuse reflectance method are relatively smaller. It has the advantages of low light energy and low cost, which is in favor of miniaturization and portability of the instruments, and will avoid the fruit quality changes caused by high power transmission. In this study, the greenhouse watermelon was used as the research object, and the near infrared reflectance spectra were collected in the watermelon stem, navel and equator at near 976, 1 186 and 1 453 nm by using JDSU portable near infrared spectrometer. The models between watermelon SSC and near infrared reflectance spectroscopy were established by using partial least square regression (PLSR). Firstly, the sample collection of different parts in the watermelon was divided based on the joint x-y distances (SPXY) method, with SSC as y variables and spectral as x variables. The samples distances were calculated by using x and y variables, and the watermelon samples were divided into 51 calibration sets and 15 prediction sets. The SSC of the calibration sets has a wide distribution range, which covers that of the prediction sets, and can increase the diversity and representativeness of samples and help to build a stable and reliable prediction model. Secondly, the prediction accuracy of quantitative models between the near infrared reflectance spectroscopy and SSC in different detection positions was investigated, and higher correlation and better prediction performance was found in the equator position with prediction correlation coefficient of 0.629 and root mean standard error of prediction of 0.49%. The accuracy of the models between SSC and near infrared spectra information in different watermelon positions was related with the spectrum collection ways and the differences in growing area, variety and maturity. Therefore, the determination of the detection position in the watermelon should be based on the actual situation in the model-building process. Finally, in order to improve the prediction accuracy of the models built for the watermelon equator, the spectra should be pre-processed with the model built for the watermelon equator, and then normalize the results, based on which we can obtain the best prediction model of PLSR. The prediction correlation coefficient was 0.864 and the root mean standard error of prediction was 0.33%, showing higher correlation and improved prediction accuracy. In conclusion, the results indicated that the SSC of the greenhouse watermelon can be accurately predicted based on detecting the equator position by near infrared reflectance spectroscopy. Therefore, it has the potential for improving the rapid and non-destructive testing technology and developing small and portable equipment to detect watermelon SSC by near infrared spectroscopy.