颜色是农产品的重要品质指标, 马铃薯内部颜色直接影响其加工产品的感官品质, 数字化无损快速检测马铃薯内部颜色指标, 并对黄芯薯和白芯薯进行快速分类, 对我国马铃薯主食化产业的进一步推进以及预制菜产业的快速崛起均具有重要意义。 本研究以马铃薯内部颜色的快速无损数字化表征为目的, 利用实验室自行创制的基于微型光谱仪的便携式马铃薯多品质无损检测装置和基于离散光谱传感器的掌上式马铃薯多品质无损检测装置, 选用不同产地26个品种共209个马铃薯样本, 分别采集其连续光谱和离散光谱数据, 并利用色差仪测定马铃薯样本的内部颜色参数 L^*、 a^*、 b^*标准值。 首先, 基于色差仪测定的209个马铃薯 L^*、 a^*、 b^*标准值, 建立黄白芯薯SVM分类判别模型, 确定了黄白芯马铃薯判别阈值平面, 作为下一步无损快速判别黄白芯薯判别依据。 其次, 基于便携式连续光谱装置采集的209个马铃薯样品的连续光谱, 经SNV预处理再结合随机蛙跳算法(RF)筛选200个特征波长建立了马铃薯 L^*、 a^*、 b^*偏最小二乘回归(PLSR)预测模型, 其验证集 L^*、 a^*、 b^*均方根误差分别为1.023 5、 0.052 5、 1.042 7; 基于掌上式离散光谱装置采集的209个马铃薯样品的离散光谱, 经SNV预处理后建立了马铃薯 L^*、 a^*、 b^*的PLSR预测模型, 其验证集 L^*、 a^*、 b^*均方根误差分别为1.278 8、 0.081 6、 1.407 1。 结果表明, 基于两个装置建立的马铃薯内部颜色参数预测模型均可以满足马铃薯内部颜色现场数字化快速无损检测需求。 最后, 选取未参与建模的26个品种共52个样本, 分别对两个装置马铃薯内部颜色 L^*、 a^*、 b^*预测值进行了外部验证, 基于便携式连续光谱装置的马铃薯 L^*、 a^*、 b^*的最大残差绝对值分别为2.617 3、 0.141 3、 2.779 1, 残差平均值分别为0.857 7、 0.049 0以及0.697 2; 基于掌上式离散光谱装置的马铃薯 L^*、 a^*、 b^*最大残差绝对值分别为3.262 8、 0.203 4、 3.519 5, 残差平均值分别为1.093 0、 0.066 7、 1.268 8。 基于两个装置对52个马铃薯样本的 L^*、 a^*、 b^*预测值, 依据前面建立的黄白芯马铃薯判别阈值平面, 进行了马铃薯黄白芯无损快速判别, 结果显示, 基于便携式连续光谱装置的判别正确率为92.31%, 基于掌上式离散光谱装置的判别正确率为86.54%。 该技术可以实现马铃薯内部颜色数字化无损检测及黄白芯薯的现场实时快速判别。 对马铃薯种植、 加工、 销售等产业链提供技术支持。

Color is an important quality indicator for agricultural products. The internal color of potatoes directly affects the sensory quality of their processed products. The rapid and non-destructive digital detection of the internal color of potatoes, as well as the quick classification of yellow-core and white-core potatoes, is of significant importance for advancing China's staple potato industry and the rapid rise of the prepared food industry. This study aims to achieve rapid, non-destructive digital characterization of the internal color of potatoes. It utilizes two self-developed, portable, multi-quality non-destructive detection devices based on a mini-spectrometer and a discrete spectral sensor: one is a laboratory-based, portable multi-quality non-destructive detection device for potatoes, and the other is a handheld, multi-quality non-destructive detection device for potatoes. A total of 209 potato samples from 26 different varieties, grown in various regions, were selected. Continuous spectral and discrete spectral data were collected, and the internal color parameters L^*, a^* and b^* of the potato samples were measured using a colorimeter. First, based on the L^*, a^* and b^* values measured by the colorimeter, an SVM classification model was established to distinguish between yellow-core and white-core potatoes. The threshold plane for distinguishing between yellow- and white-core potatoes was determined, which will serve as the basis for the next step in the non-destructive and rapid identification of yellow- and white-core potatoes. Secondly, based on the continuous spectral data collected by the portable continuous spectral device from the 209 potato samples, SNV preprocessing combined with the Random Frog Jump (RF) algorithm was used to select 200 characteristic wavelengths to establish a PLSR (Partial Least Squares Regression) prediction model for the L^*, a^* and b^* parameters of potatoes. The root mean square errors (RMSE) for the validation set of L^*, a^* and b^* were 1.278 8, 0.081 6, and 1.407 1, respectively. Similarly, for the discrete spectral data collected by the handheld spectral device, after SNV preprocessing, PLSR prediction models for the L^*, a^* and b^* parameters of potatoes were also established. The RMSE for the validation set of L^*, a^* and b^* were 1.278 8, 0.081 6, and 1.407 1, respectively. The results showed that the prediction models for the internal color parameters of potatoes established with both devices can meet the demand for rapid, non-destructive, digital detection of potato internal color in the field. Finally, 52 potato samples from 26 varieties, which were not involved in model training, were selected for external validation of the L^*, a^* and b^* predicted values using both devices. For the portable continuous spectral device, the maximum absolute residuals for L^*, a^* and b^* were 2.617 3, 0.141 3, and 2.779 1, respectively, and the mean residuals were 0.857 7, 0.049 0, and 0.697 2, respectively. For the handheld discrete spectral device, the maximum absolute residuals for L^*, a^* and b^* were 3.262 8, 0.203 4, and 3.519 5, respectively, and the mean residuals were 1.093 0, 0.066 7, and 1.268 8, respectively. Based on the L^*, a^* and b^* predicted values from both devices, rapid non-destructive identification of yellow and white-core potatoes was performed using the previously established classification threshold plane. The classification accuracy for yellow and white-core potatoes was 92.31% for the portable continuous spectral device and 86.54% for the handheld discrete spectral device. This technology enables the rapid, non-destructive, and real-time digital detection of potato internal color, as well as the quick classification of yellow- and white-core potatoes in the field. It provides technical support for the entire potato industry chain, including planting, processing, and sales.