鲜食玉米果穗含水率近红外光谱无损检测影响因素

Factors affecting the non-destructive detection of water contents in fresh corn cobs by near-infrared spectroscopy

为了获取鲜食玉米果穗高质量近红外光谱,该研究基于近红外漫反射光谱技术开展试验参数对鲜食玉米果穗光谱特征影响及建模验证探究。根据果穗棒状特征,搭建多维度综合试验装置,采集光源类型、光强大小、探测距离和光源角度共4类不同参数下的900~1700 nm光谱数据,进行卤素灯杯与光纤光源、卤素灯杯功率20与40 W、探测距离10~50 mm和30°、45°及60°卤素灯杯夹角的对比试验,分析光谱差异及曲线分布规律,采用标准差和光谱面积极差指标进行光谱质量评价。进一步开展建模验证试验,针对30°和45°卤素灯杯夹角下的光谱,经多元散射校正(multiplicative scatter correction,MSC)、标准正态变量校正(standard normal variate,SNV)、一阶导数(first derivative,FD)和趋势校正(detrending,DT)预处理后,应用偏最小二乘回归(partial least squares,PLS)和支持向量机(support vector machines SVM)方法建立了含水率预测模型,并对建模性能进行了对比。试验结果表明,卤素灯杯、功率20 W、探测距离40 mm对应所选果穗的光谱响应充分、干扰少,与果穗特征相匹配,曲线采用标准差和光谱面积极差分别为0.83和187.2,综合光谱曲线质量评价和建模性能对比,卤素灯杯夹角45°优于30°。通过SNV预处理后的SVM预测模型具有更好的性能,校正集和预测集决定系数分别为0.943、0.880,均方根误差分别为0.708、0.932,剩余预测偏差为2.956。该研究结果可为基于近红外漫反射光谱技术的鲜食玉米果穗内在品质无损检测提供技术支撑。

This study aims to obtain the high-quality near-infrared(NIR)spectra of fresh corn cobs.A systematic investigation was made to explore the effects of experimental parameters on the spectral features of fresh corn cobs and modeling validation using NIR diffuse reflectance spectroscopy.According to the cob stick-shaped characteristics,the multi-dimensional comprehensive test was carried out to collect 900~1700 nm spectral data under four parameters,namely,light source type,light intensity,detection distance,and light source angle.The halogen lamp cups were used as the fiber optic light sources.Among them,the halogen lamp cups were selected with the power of 20 and 40 W,detection distances of 10 and 50 mm,and halogen lamp cups with angles of 30°,45°,and 60°,in order to analyze the spectral differences and distribution patterns of curves.The spectral differences were determined for the distribution patterns.The standard deviation and spectral area extreme deviation indexes were used to evaluate the spectral quality.Further validation tests were carried out on the model.The spectra were evaluated at 30°and 45°halogen lamp cup angles by multiplicative scatter correction(MSC),standard normal variate(SNV),first derivative(FD),and trend correction.Furthermore,the new model was established to predict the water content using partial least squares(PLS)and support vector machines(SVM).The performance of the model was compared after the derivative(FD)and detrending(DT)pre-processing.The experimental results showed that sufficient spectral response and less interference were achieved in the halogen lamp cup with a power of 20 W and a detection distance of 40 mm.The standard deviation and spectral area polarity of the curve were 0.83 and 187.2,respectively.The better quality of the spectral curve and higher performance of the model were also obtained in the halogen lamp cup with the clamp angle of 45°,compared with 30°.The better performance was found in the SVM prediction model after SNV preprocessing.The coefficients of determination were 0.943 and 0.880,respectively,in the correction and prediction datasets,while the root mean square errors were 0.708 and 0.932,respectively,and the residual prediction deviation was 2.956.The finding can provide technical support to the nondestructive test on the intrinsic quality of fresh corn cobs using near-infrared diffuse reflectance spectroscopy.