马铃薯近红外光谱无损检测影响因素研究

为提高马铃薯近红外光谱快速无损检测的准确性和稳定性,对比了光纤光源、卤素灯杯光源和环形光源三种光源条件下的光谱,结果显示环形光源的光谱噪声最小,辐照强度与均匀度最好。该工作对光源功率、光源距马铃薯表面距离以及光纤距马铃薯表面检测点距离进行研究,通过三因素三水平响应面试验,评价不同因素水平条件下对马铃薯可溶性固形物含量的光谱模型预测效果,得到最优参数组合为光源功率238.33 W,光纤探头距样品表面距离8.17 mm,光源距样品表面距离370 mm,并建立可溶性固形物定量预测模型。在最优参数条件下,预测偏最小二乘回归模型(PLSR)的相关系数优化到0.867,均方根误差0.149°Brix。为进一步消除设备及环境噪声,通过不同预处理算法降低噪声的干扰,结果显示标准变量排序法去噪效果最好,PLSR预测相关系数可达0.914,预测均方根误差降低到0.132°Brix,既能有效去噪又有较好的预测效果。试验结果表明,响应面试验优化检测环境和条件能有效提高马铃薯品质检测的预测精度,为近红外光谱马铃薯无损检测环境搭建和设备选型提供技术参考。

Research on the Factors Influencing the Non-Destructive Detection of Potatoes by Near-Infrared Spectroscopy

To enhance the stability as well as accuracy of near-infrared rapid nondestructive testing, this paper compares the spectral effects of three different types of light sources in the conditions of fiber optic light source, halogen cup light source and ring light source, and the results show that the ring light source has the lowest spectral noise, moderate irradiation intensity and the best uniformity. Based on these studies, the power of the light source, the distance from the optical source to the potato surface, and the distance from the optical fiber to the detection point on the potato surface are investigated in this study. The spectral model’s prediction effect on potatoes’ soluble solids content under different factors was evaluated by a three-factor, three-level response surface test. The optimal parameters were light source power 238.33 W, distance from fiber optic probe to sample surface 8.17 mm, distance from the light source to sample surface 370 mm, R_P=0.867, and RMSE_P=0.149°Brix of the PLSR model for soluble solids prediction. To further eliminate equipment and environmental noise, interference of noise was reduced by different preprocessing algorithms. The outcome showed that the algorithm with the standard variable ranking approach had the best noise removal effect, with R_P=0.914 and RMSE_P=0.132°Brix, which obtained a better prediction result while effectively removing noise. The test results show that optimizing the testing environment and conditions through response surface testing can effectively improve the prediction accuracy of potato quality testing and provide technical guidance for the construction and device selection of near-infrared online the nondestructive detection environment for potatoes.