苹果内在组分与隐性缺陷的透射光谱在线检测基础研究

Apple plays a critical role for increasing farmers' income and developing green friendly economic in China's modern agricultural. Currently, postharvest commercial processing technologies for apple internal quality detection are urgently needed especially the rapid non-destructive, online detection technology and systems. In view of the difficulty of apple internal quality and hidden defects detection, we proposed a novel idea that simultaneous determination internal quality and hidden defects in apple using online near-infrared (NIR) transmittance spectroscopy. Apple tissue optical transmission mechanism, spectral response characteristics, signal enhancement mechanism and processing methods will be research. The absorption and reduced scattering coefficients will be used in Monte Carlo models to simulate light transport in the fruit tissue. Through experiment test and optical simulation, light absorption and scattering properties in apple tissues will be calculated. The patterns of diffuse reflectance, light penetration depth, and scattering energy distribution will be determined for the apples. Relationships between texture attributes and the optical properties will be identified. The differences of light scattering properties between diseased tissue and intact apple will be investigated, and then identification models will be developed using NIR transmittance spectra combined pattern recognition methods. Transmittance spectra weak detection signals will be effective achieve. An online determination system based on NIR transmittance spectroscopy will be developed and used for signal acquisition related to the internal quality and hidden defects. Combination of system parameters will be optimizing to improve the quality of online transmission spectra. Multivariate calibration methods and pattern recognition methods will be compared and selected to build internal quality quantitative models and hidden defects classification models, respectively. Finally, we will construct mathematical models for simultaneous determination internal quality and hidden defects in apple by online detection system based on NIR transmittance spectroscopy. This project will provide a theoretical foundation methods reference for fruit internal quality rapid, non-destructive, online detection technology and equipment, and will be quite helpful to reduce the apple postharvest economic loss, enhance market competitiveness.

苹果在我国现代农业经济中占有重要地位，现阶段迫切需要产后内部品质商品化处理技术，特别是快速无损、可在线的检测技术和方法。针对苹果内在组分与隐性缺陷检测的难点问题，研究提出全透射光谱技术的苹果多指标同步在线检测思路，探析苹果组织光传输机理、光谱响应规律、信号增强机制与处理方法等科学问题。研究拟通过试验模拟与光学仿真，利用苹果组织内光传输的吸收特性和散射特性解析光的有效穿透深度和能量分布规律；分析苹果组织光学特性参数与内在品质的相关关系；研究苹果病变组织的光散射特性，分析典型隐性缺陷苹果与正常果的光谱响应差异；研究透射光谱的微弱信号增强与消澡方法，设计水果内在组分与隐性缺陷透射光谱在线检测系统，优化系统以提升在线透射光谱质量，优选多元校正方法和模式识别方法构建内在组分定量分析模型和隐性缺陷判别模型，实现苹果多指标同步在线检测。本项目研究为水果内部品质的无损检测技术和装备提供理论依据和方法参考。

针对苹果内在组分和隐性缺陷检测的产业技术难题，研究提出了全透射光谱技术的苹果多指标同步在线检测新思路。研究分析了苹果组织对光的衰减作用以及光在苹果组织散射的分布规律，解析了苹果组织空间分辨的衰减规律，表征了光传输散射特性，通过仪器表征的方法量化描述质地特性，分析了苹果质地的变化规律与分布特征，为苹果品质无损检测的光路设计和检测系统结构设计提供理论依据；提出了苹果可溶性固形物模型的颜色补偿方法，采用特征波长提取和潜变量提取，融合颜色空间参数，建立线性或非线性回归模型，比较了短波近红外和长波近红外两个波段可溶性固形物的颜色补偿模型预测性能，为保证内部品质检测的精度和稳定性，颜色作为补偿因子可以有效提高品质检测的性能；研究解析了典型隐性缺陷苹果与正常果的透射光谱的特性差异，从机理上揭示了透射光谱检测隐性缺陷的可行性和不同缺陷检测的特异性；通过筛选特征光谱区间或特征波长组合，优选模式识别算法建立苹果隐性缺陷的识别方法；设计了基于全透射光谱技术的苹果内在组分与隐性缺陷透射光谱在线检测试验系统，优化系统参数以提升在线透射光谱质量、降低热损伤和机械损伤、提高检测系统适应性。本研究建立了苹果内在组分和内部隐性缺陷的多指标同步检测方法，阐明了苹果组织光传输机理、光谱响应规律、信号增强机制与处理方法等科学问题，为实际在线检测应用提供理论支撑和方法参考。