基于木质素含量的香梨品质快速无损分级方法研究

The project is based on the near infrared spectroscopy technology（NIR）, with the key index parameters of lignin content and sugar, which influence the internal quality of fragrant pear, to explore the nondestructive technology of graded fragrant pear’s internal quality recognition and optimal storage quality. A stable and accurate quantitative detection model of lignin and sugar is built by the combination of near infrared spectroscopy and chemometrics principle, with the optimal spectrum detection mode, characteristic wavelength selection, spectral preprocessing methods and multifarious aspects of data modeling methods. And according to the influence of pear’s maturity on the typical postharvest quality of deterioration, the high precision separation system is established on difference recognition of fragrant pear’s internal key quality and optimal storage maturity, to delay the deterioration of pear’s quality and commercial reduction after harvest, and improve its added value of commercialization. Then, with the platform of fragrant pear grading system, hierarchical quality analysis system of pear is built up to promote the development of fruit’s quality fast non-contact inspection technology of our country.

本项目针对影响香梨内在品质的木质素含量及糖度等关键指标参数测定，以近红外光谱分析技术为基础，开展不同等级香梨内在品质差异的识别分级和适贮（适宜采摘时间）品质无损检测技术的研发。将近红外光谱技术与化学计量学原理结合，通过确定适宜在线检测的光谱采集模式、关键特征波长、光谱预处理方法以及模型算法等，建立稳定的、准确的香梨木质素及糖度的定量检测模型。并根据成熟度对梨典型品质采后劣变的影响，形成香梨内在关键品质差异的识别及适贮成熟度（适宜采摘时间）的高精度分选体系，延缓采后品质劣变和商品性下降，提升梨果采后商品化附加值。进而以香梨的分级体系为平台，构建完善的梨果品质分析分级体系，促进我国水果品质非接触快检技术的发展进步。

针对香梨品质分选分级技术缺位这一问题，项目以近红外光谱技术为基础，进行了香梨内部关键品质差异识别的新方法研发。项目开展了香梨光谱数据采集模式研究，结合样品特性、光谱质量、模型效果比较透射、透反射、漫反射方式的优劣，从而确定了最佳采集方法；结合木质素、糖、酸、果品物理特性等参数进行内在品质关联，优化了木质素的检测方法（高效液相色谱）和关键流程（提取温度、时间、料液比），统计分析了青头果、粗皮果、突顶果、宿萼果和脱萼果等5类不同品质的香梨指标差异，确定了以硬度、蔗糖含量、草酸含量、柠檬酸含量、木质素结构氧化产物等指标构建的两级分类方法；研究了数据的降维、预处理、波段区域筛选、建模方法等因素对模型的影响，以最佳的信息提取方法、模型建立方法构建了有效的数学模型。本项目取得的科研成果为后续果品分选分级设备的开发奠定了理论基础，为水果品质快速无损识别提供新参数、新方法。项目资助发表核心论文两篇，专利一项，培养研究生三名，其中一名已经取得硕士学位。项目投入直接经费25万元，各项支出基本与预算相符。