摘要
香葱是一种保质期很短的重要调味食品,水分与叶绿素是评估香葱采后品质的重要指标。本文旨在使用无损检测技术获取香葱在采后不同存储条件下的水分及叶绿素分布情况。实验采用高光谱成像技术获取431~962 nm波段的香葱反射光谱数据,通过卷积平滑(SG)、多元散射校正(MSC)、标准正态变异(SNV)三种预处理方法对原始光谱进行相应转换,并分别建立水分和叶绿素含量预测模型,比较模型预测精度后,选用降噪效果最好的MSC作为光谱预处理方法。随后使用竞争自适应加权采样算法分别选出11个和20个特征波段用于水分与叶绿素含量的预测。基于优选特征波段,利用偏最小二乘回归算法和支持向量机回归算法建立水分和叶绿素含量的预测模型。所建水分与叶绿素含量预测模型的最高预测决定系数分别达到0.9046和0.9143。最后根据所建模型取得不同存储条件下香葱水分及叶绿素含量分布图。综上,高光谱成像技术可用于快速无损检测香葱水分及叶绿素分布情况。本研究为后续便携式果蔬水分及叶绿素分布检测仪器的开发提供了理论依据。
Green onions are important flavoring food with a limited shelf life.Moisture and chlorophyll content are two important parameters for the post-harvest quality assessment of green onions.The aim of this paper was to obtain moisture and chlorophyll distribution of green onion under different postharvest storage conditions by means of a hyperspectral imaging(HSI)technique.The HSI was used to obtain the reflectance spectral data for green onions at 431~962 nm band.The original spectrum was transformed by three pretreatment methods of convolutional smoothing(SG),multiple scattering correction(MSC),and standard normal variation(SNV)to convert the original spectrum accordingly,and established the prediction model of moisture and chlorophyll content respectively.After comparing the prediction accuracy of the model,the MSC was found to have the best noise reduction effect was selected as the final spectral pretreatment method.Then a competitive adaptive weighted sampling method was used to select 11 and 20 optimal wavelengths for moisture and chlorophyll content predictions,respectively.Based on the selected wavelengths,partial least squares regression and support vector machine regression algorithms were used to establish the prediction model for moisture and chlorophyll contents.The prediction models based on the optimal wavelengths for moisture and chlorophyll content yielded 0.9046 and 0.9143,respectively.Finally,distribution maps of the moisture and chlorophyll content of green onions under the different storage conditions were obtained.In summary,the hyperspectral imaging might be used to rapidly detect the distribution of moisture and chlorophyll in green onion.This study would provide a theoretical basis for the subsequent development of portable measuring instruments for moisture and chlorophyll distribution in fruits and vegetables.
作者
任怡
王成全
Bonah Ernest
Joshua Harrington Aheto
王锋
黄星奕
REN Yi;WANG Chengquan;Bonah Ernest;Joshua Harrington Aheto;WANG Feng;HUANG Xingyi(School of Food and Biological Engineering,Jiangsu University,Zhenjiang 212013,China;School of Smart Agriculture,Suzhou Polytechnic Institute of Agriculture,Suzhou 215008,China;Laboratory Services Department,Food and Drugs Authority,Accra 00233,Ghana)
出处
《食品工业科技》
CAS
北大核心
2021年第10期267-274,共8页
Science and Technology of Food Industry
基金
国家重点研发计划项目(2017YFD0400102)
江苏大学基金(19JDG025)
江苏省研究生科研与实践创新计划项目(KYCX19_1631)
青年教师科研能力提升计划资助项目(SNQ201806)。
关键词
香葱
储藏
可视化
无损检测
高光谱成像
green onion
storage
visualization
non-destructive detection
hyperspectral imaging
