生物散斑是一种利用激光在物体内部的折射与反射来反映其内部信息的光学无损检测技术。生物散斑成像设备和图像处理算法不断改进,应用领域也在逐步扩大;但由于干扰因素的存在,建模精度仍是研究者关注的重点。本文较为详尽地整理了散斑图像处理算法,并对生物散斑技术在水果品质检测中的应用进行调研,归纳主要的成像设备,提出改进意见,旨在为后续研究提供启发。

Nondestructive evaluation of melon quality is in great need of comprehensive study. Soluble solids content (SSC) and firmness are the two indicators of melon internal quality that mostly affect consumer acceptance. To provide guidance for fruit classification, internal quality standards was preliminarily established through sensory test, as: Melon with SSC over 12° Brix, firmness 4–5.5 kgf · cm^-2 were considered as satisfactory class sample; and SSC over 10° Brix, firmness 3.5–6.5 kgf · cm^-2 as average class sample. The near infrared (NIR) nondestructive detection program was set as spectra collected from the stylar-end, Brix expressed by the average SSC of inner and outer mesocarp, each cultivar of melon was detected with its own optimum integration time, and the second derivative algorithm was used to equalize them. Using wavelength selected by genetic algorithms (GA), a robust SSC model of mix-cultivar melon was established, the root mean standard error of cross-validation (RMSECV) was 0.99 and the ratio performance deviation (RPD) nearly reached 3.0, which almost could meet the accuracy requirement of 1.5° Brix. Firmness model of mix-cultivar melon was acceptable but inferior.

利用成像光谱可同时获得水果的成像信息和光谱信息, 实现水果外部和内部品质的高精度、 无损伤、 无污染的大面积瞬态检测。 为了获得水果的成像光谱, 设计了一种以自主研发的凸面光栅作为核心元件的小型成像光谱仪, 它具有成像质量好、 体积小、 重量轻等优点, 光谱分辨率在578 nm波长处达2.1 nm, 光谱谱线弯曲和色畸变均小于0.6%。 对该小型成像光谱仪进行了检测和苹果成像光谱测量实验, 结果表明: 该成像光谱仪满足设计要求且可以快速的获得苹果的高精度成像光谱, 从而进行水果的品质检测。