我国马铃薯采后储运销售过程中黑心病发病率较高, 内部品质也参差不齐, 检测分选技术滞后, 严重制约了马铃薯主食化产业发展进程。 马铃薯黑心病及淀粉含量等内部品质的同时在线无损检测, 对推进我国马铃薯主食化战略具有重要意义。 基于可见/近红外漫透射光谱原理, 利用实验室自行搭建的无损在线检测系统(检测速度约为每秒4个), 以马铃薯黑心病和淀粉含量为内部品质检测指标, 进行了黑心病和淀粉含量同时在线无损检测研究。 先将121个健康马铃薯和116个黑心马铃薯600～1 000 nm波段范围的原始光谱分别进行了平均处理, 发现600～900 nm波段内黑心马铃薯样品的吸光度数值明显高于健康马铃薯样品, 而且黑心组织影响健康马铃薯在663 nm附近叶绿素的特征吸收峰和760 nm附近水的特征吸收峰, 强度明显高于黑心马铃薯。 基于健康马铃薯和黑心马铃薯原始光谱建立了马铃薯黑心病偏最小二乘判别模型(PLS-DA)。 同时对121个健康马铃薯光谱分别采用SG卷积平滑(SG-Smoothing)、 标准正态变换(SNV)、 多元散射校正(MSC)、 一阶导数(FD)、 SG平滑结合一阶导数(SG+FD)等不同预处理方法, 并结合竞争性自适应加权重采样CARS算法筛选特征波长后, 建立了淀粉含量(SC)偏最小二乘(PLS)定量预测模型。 结果表明: 黑心马铃薯偏最小二乘定性判别模型校正集和验证集判别正确率分别为97.74%和98.33%, 总判别正确率97.89%; 原始光谱经SG平滑加一阶导数预处理, 再结合CARS算法筛选特征波长建的马铃薯淀粉含量偏最小二乘定量预测模型结果最优, 其校正集和预测集相关系数分别为0.928和0.908, 均方根误差分别为0.556%和0.633%。 最后, 将所建模型植入在线检测系统, 利用50个未参与建模的样品进行了外部验证。 马铃薯黑心病的判别正确率为96%, 淀粉预测值与标准理化值相关系数为0.893, 均方根误差为: 0.713%。 说明基于马铃薯漫透射光谱可以实现马铃薯黑心病及其他内部品质同时在线无损检测, 为马铃薯采后品质检测分选以至推进马铃薯主食化产业发展提供了一定技术参考。

The incidence of black-heart disease in post-harvest storage and transportation of potato in China is high, the internal quality is also uneven, and the detection and sorting technology lags behind, which seriously restricts the development of potatoes’ staple food industry. Simultaneous online non-destructive testing of internal quality such as potatoes’ black-heart disease and starch content is of great significance for promoting the strategy of main potato diet in China. Based on the principle of visible/near-infrared diffuse transmission spectroscopy, this study uses a non-destructive on-line detection system built by the laboratory (detection speed is about 4/s), and carries out black heart disease with potato black-heart disease and starch content as internal quality test indicators. Simultaneous non-destructive testing with starch content. The original spectra of 121 healthy potatoes and 116 black-heart potatoes in the 600～1 000 nm band were averaged. The absorbance values of black potato samples in the 600～900 nm band were significantly higher than those of healthy potato samples, and the influence of black heart tissue was observed. The characteristic absorption peak of chlorophyll near 663 nm and the characteristic absorption peak of water near 760 nm of healthy potato were significantly higher than that of black heart potato. Partial Least Squares Discriminant Analysis (PLS-DA) was established based on the original spectrum of healthy potato and black heart potato. At the same time, SG-Smoothing, Standard Normal Transformation (SNV), Multiple Scattering Correction (MSC), First Derivative (FD), SG Smoothing and First Derivative (SG+FD) and other pretreatment methods were applied to the 121 healthy potato spectra. And combined with CARS algorithm to screen the characteristic wavelength, established a Starch Content (SC) Partial Least Squares (PLS) quantitative prediction model. The results showed that the correctness rate of the correction set and verification set of the PLS-DA model of the black-heart potatoes was 97.74% and 98.33%, respectively, and the total discriminant correct rate was 97.89%. The original spectrum was preprocessed by SG smoothing plus first derivative, and then combined with CARS. The PLS model of potato starch content was optimized by algorithm screening. The correlation coefficients of the calibration set and prediction set were 0.928 and 0.908, respectively, and the root means square error was 0.556% and 0.633%, respectively. Finally, the model was built into an online inspection system and externally verified using 50 samples that were not modeled. The correct rate of potato black heart disease was 96%, the correlation coefficient between the starch predicted value and the standard physical and chemical value was 0.893, and the root means square error was 0.713%. It is indicated that potato black-heart disease and other internal quality can be simultaneously detected by on-line non-destructive testing based on potatoes’ diffuse transmission spectroscopy, which provides a technical reference for potatoes’ post-harvest qualities testing and promotion of potatoes’ staple food industry.