Prediction and evaluation method of TVB-N values distribution in pork by hyperspectral imaging  

作　　者：Yuweiyi Tang Xiwei Wang Xiaoyang Xing Zhong Li Maocheng Zhao 

机构地区：[1]College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China [2]National Engineering Research Center of Biomaterials,Nanjing Forestry University,Nanjing 210037,China [3]Taizhou University,Taizhou 225300,Jiangsu,China

出　　处：《International Journal of Agricultural and Biological Engineering》2021年第4期270-276,共7页国际农业与生物工程学报（英文）

基　　金：The authors acknowledge the funding support of Postgraduate Training Innovation Project in Jiangsu Province,China(Grant No.KYCX17_0864).

摘　　要：Hyperspectral imaging makes it possible to map the spatial distribution of quality-indicating attributes over biological targets.However,inconsistencies may arise between these newly available qualitymaps of the same piece of the biological target from different chemometric models.Therefore,such inconsistency of the spatial prediction of the freshness-indicating attribute of total volatile basic nitrogen(TVB-N)over pork loins(Longissimus dorsi)was investigated in this work from the perspective of the accuracy and variation of pixel-wise prediction over the top-surfaces.The partial least square regression(PLSR)-based chemometric modelling was performed on the characteristic spectra of a target extracted from regions-of-interest(ROIs)of both the whole meat and the lean part only,after spectral preprocessing and spatial filtering,and coupled with waveband selection using successive projections algorithm(SPA).Results showed that all PLSR models achieved good and stable predictions of the average TVB-N values of pork loins,despite the differences of data-preprocessing or numbers of wavebands,with R2p being in the range from 0.832 to 0.889 for Meat-ROI and an even higher range from 0.846 to 0.912 if using Lean-ROIs,even with a reduced number of wavebands,accuracy remaining high in the range of R2p from 0.722 to 0.889 for Meat-ROIs and from 0.704 to 0.900 for Lean-ROIs.In contrast,however,devastating differences emerged between the predicted TVB-N distributions over a top-surface.Results showed that excessive variation of pixel-wise predictions rendered utterly useless distribution maps resulted from a direct application of the PLSR or PLSR_SPA models from Meat or Lean ROIs,quantified by root-mean-error(RMSE)of pixel-wise predictions over 21.6 mg/100 g with PLSR,or even the worse with PLSR-SPA,shooting absurdly high about 55 mg/100 g.After appropriate spatial filtering,the excessive between-pixel variation was suppressed to below 8.5 mg/100 g in RMSE,presenting visually better distribution maps,but at a significant loss

关 键 词：PORK TVB-N hyperspectral imaging PREDICTION evaluation DISTRIBUTION PLSR SPA 

分 类 号：TS2[轻工技术与工程—食品科学与工程]