基于光谱变换算法的不同横径沃柑的光谱与建模对比分析  

Comparative analysis of spectra and modeling of different cross-diameter orah mandarin based on spectral transformation algorithm

作　　者：胡峰 龚中良[1] 文韬[1] 代兴勇 唐子叶 HU Feng;GONG Zhongliang;WEN Tao;DAI Xingyong;TANG Ziye(School of Mechanical and Electrical Engineering,Central South University of Forestry and Technology,Changsha,Hunan 410004,China)

机构地区：[1]中南林业科技大学机电工程学院,湖南长沙410004

出　　处：《食品与机械》2024年第5期113-121,218,共10页Food and Machinery

基　　金：湖南自然科学基金杰出青年基金项目(编号:2023JJ10099)。

摘　　要：目的:减小沃柑横径对糖度预测带来的影响。方法:将132个沃柑按横径分为小等果(65~70 mm)、中等果(70~75 mm)和大等果(75~80 mm)3组。采集全体沃柑的横径光谱后,运用光谱变换算法,综合光谱信息与沃柑横径信息,将不同大小的沃柑光谱转换到同一横径基准下。将修正前的光谱与修正后的光谱分别通过预处理,目标共生距离算法(SPXY)划分,竞争性自适应权重取样法(CARS)筛选特征波长点以及偏最小二乘回归法(PLS)建立修正前后的糖度模型。结果:小等果光谱经过修正,预测集决定系数(R_(P)^(2))由0.790提升至0.821,预测集均方根误差(RMSEP)为0.489降低至0.443;中等果光谱经过修正,R_(P)^(2)由0.801提升为0.845,RMSEP为0.460降低至0.422;大等果光谱修正,R_(P)^(2)由0.820提升至0.863,RMSEP为0.431降低至0.393。结论:光谱修正算法减小了沃柑横径带来的光谱差异,提升了模型的预测精度。Objective:To reduce the influence of transverse diameter of Mandarin orange on the prediction of sugar content.Methods:132 citrus were divided into three groups according to transverse diameter:small(65~70 mm),medium(70~75 mm)and large(75~80 mm).After collecting the transverse diameter spectrum of all citrus,the spectrum information and transverse diameter information of citrus were synthesized by spectral transformation algorithm,and the spectrum of different sizes of citrus was converted to the same transverse diameter datum.The pre-correction and post-correction spectra were respectively preprocessed,divided by target symbiotic distance algorithm(SPXY),screened by competitive adaptive weight sampling(CARS)and selected by partial least squares regression(PLS)to establish the sugar degree model before and after correction.Results:The prediction set determination coefficient(R_(P)^(2))was increased from 0.790 to 0.821,and the prediction set root mean square error(RMSEP)was decreased from 0.489 to 0.443.The middle fruit spectrum was modified,R_(P)^(2) increased from 0.801 to 0.845,RMSEP decreased from 0.460 to 0.422.R_(P)^(2) increased from 0.820 to 0.863 and RMSEP decreased from 0.431 to 0.393.Conclusion:The spectral correction algorithm can reduce the spectral difference caused by the transverse diameter and improve the prediction accuracy of the model.

关 键 词：沃柑 横径 光谱修正 糖度 

分 类 号：O657.3[理学—分析化学] TS255.7[理学—化学]