CAJ | 학술논문

为了实现番茄可溶性固形物含量（soluble solids content，SSC）的有效检测，提出高光谱漫透射成像检测方法，对比该成像方式下不同姿态（果脐端面姿态BS、赤道圆周3姿态C1、C2、C3以及组合姿态C1C2C3）的检测效果。首先对采集的不同姿态光谱图像，通过剪裁消除图像边缘噪声。针对圆周赤道面姿态C1、C2和C3，进行了拼接处理，获得组合姿态图像 C1C2C3。其后对以上5种姿态图像进行单波段背景分割，获取目标区域，并统计不同姿态下番茄漫透射平均光谱。最后利用漫透射光谱结合偏最小二乘回归（partial least squares，PLS）方法，对番茄SSC分别在450～720、720～990、450～990 nm 3个波段进行定量分析。结果表明，组合姿态C1C2C3在3个波段区域上整体检测效果优于单个姿态的检测效果，其模型验证集均方根误差（root mean squared error of prediction，RMSEP）分别为0.299%、0.133%、0.151%；相关系数rp分别为0.42，0.89，0.90。说明利用高光谱漫透射成像，获取组合姿态光谱图像，可以有效检测番茄SSC。
위료실현번가가용성고형물함량（soluble solids content，SSC）적유효검측，제출고광보만투사성상검측방법，대비해성상방식하불동자태（과제단면자태BS、적도원주3자태C1、C2、C3이급조합자태C1C2C3）적검측효과。수선대채집적불동자태광보도상，통과전재소제도상변연조성。침대원주적도면자태C1、C2화C3，진행료병접처리，획득조합자태도상 C1C2C3。기후대이상5충자태도상진행단파단배경분할，획취목표구역，병통계불동자태하번가만투사평균광보。최후이용만투사광보결합편최소이승회귀（partial least squares，PLS）방법，대번가SSC분별재450～720、720～990、450～990 nm 3개파단진행정량분석。결과표명，조합자태C1C2C3재3개파단구역상정체검측효과우우단개자태적검측효과，기모형험증집균방근오차（root mean squared error of prediction，RMSEP）분별위0.299%、0.133%、0.151%；상관계수rp분별위0.42，0.89，0.90。설명이용고광보만투사성상，획취조합자태광보도상，가이유효검측번가SSC。
Soluble solid content (SSC) is one of the most important indexes for quality evaluation of tomato products. Near infrared (NIR) spectroscopy and hyperspectral reflectance imaging have been widely used in quality evaluation of fruits and vegetables including tomatoes. But they have many disadvantages for inspection of SSC in tomato. For example, NIR spectroscopic assessments cannot get the spatial variability of sample materials. Although hyperspectral reflectance imaging can obtain both spatial and spectral information of tomatoes, it's almost impossible to avoid a serious influence of high specula patches on tomatoes. Diffuse transmittance is one kind of transmittance mode. Compared with transmittance, the influence of shape, size, and core of fruit can be reduced through adjusting the lighting angle in diffuse transmittance systems. So diffuse transmittance is more suitable to assess the components of fruits and vegetables. Hyperspectral imaging technique in a diffuse transmittance mode was used to measure the SSC of tomato. First, a hyperspectral imaging platform with diffuse transmittance illumination was set up, and then hyperspectral diffuse transmittance images of tomatoes were captured in different positions including BS, C1, C2, and C3. All images were resized to eliminate boundary noise. The position C1C2C3 was achieved through mosaicing images of position C1, C2, and C3. Then background segmentation on a single wavelength was operated on the images to extract regions of interest (ROIs). Afterwards, the mean diffuse transmittance spectra of tomatoes in each position were calculated and preprocessed using normalization, standard normal variate (SNV), and a quadratic linear removed baseline. Finally, partial least squares regression (PLSR) was used to establish predicting models among the SSC of tomatoes and mean diffuse transmittance spectra in different positions on three different wavebands (450~720 nm, 720~990 nm, and 450~990nm). The results indicated that the prediction precision of integrated position C1C2C3 was much better than that of the other positions on the above three wavebands. RMSEP of the C1C2C3 model on the three wavebands were 0.299%, 0.133% and 0.151%, and the correlation coefficients (rp) were 0.42, 0.89 and 0.90 respectively.