基于近红外高光谱成像技术的涩柿SSC含量无损检测

Research on Non-destructive methods for soluble solid content detection of astringent persimmon based on near-infrared hyperspectral technology

对150个涩柿采集900~1 700nm波段的近红外高光谱图像信息,利用蒙特卡罗—无信息变量消除(MC-UVE)和连续投影算法(SPA)对感兴趣区域光谱进行波长优选。通过MC-UVE-SPA优选出924.69,928.05,1 112.72,1 270.91,1 365.3,1 402.42,1 453.06,1 547.69nm 8个特征波长,对应的光谱反射率作为柿子可溶性固性物含量(SSC)检测的偏最小二乘回归(PLSR)检测模型输入,其预测集相关系数rpre=0.942,预测集均方根误差RMSEP=1.009°Brix。结果表明,MC-UVE-SPA可以有效提取与柿子SSC含量相关的特征信息,从而保留较少的波长建立较好的预测模型。

This study collected the near infrared （NIR） hyperspectral images of 150 astringent persimmons, with the spectra are in 900-1700 nm. Monte Carlo-uninformative variable elimination （MC- UVE） algorithm and successive projections algorithm （SPA） were a- dopted to the optimization of wavelengths obtained from the region of interest （ROD. Eight wavelengths were selected by MC UCE-SPA. These feature wavelengths were 924. 69, 928. 05, I 112. 72, 1 270.91, 1 365. 3, 1 402. 42, 1 453. 06 and 1 547. 69 nm, respectively. The spectral reflectance of the 8 feature wavelengths were applied to establish the detective model for the soluble solid content （SSC） of persimmon by partial least squares regression （PLSR） method. The correlation coefficient and root mean square er for of prediction set are rpre= 0.942, RMSEP= 1.009°Brix. The re suhs indicated that MC-UVE-SPA could effectively extract the char acteristic information related to the SSC and develop a better predic-tive model with fewer wavelengths. This work can provide technical support and research basis for the nondestructive detection, grading and processing equipment for persimmon quality.