Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe
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摘要: 传统外部品质分选目前已经无法满足人们对水果口感品质的需求，对传统外部品质分选线进行糖度分选改造，实现糖度分选，对确保水果的口感品质具有重要意义。分别采集两种不同检测方式下获取的脐橙的近红外漫反射光谱，其中环形发射与接收漫反射方式的光谱能量要比多点发射与接收漫反射方式强，波峰与波谷位置大致相同。近红外漫反射光谱经基线校正、多元散射校正、一阶、二阶导数等方法进行光谱数据预处理，以减少杂散光和噪声的影响。分别建立脐橙在两种不同漫反射检测方式下的糖度偏最小二乘（PLS）模型进行对比分析。实验结果表明，采用基线校正预处理方法获得的结果最优；环形发射与接收漫反射检测方式下的糖度模型预测相关系数为0.81，预测均方根误差为0.46°Brix，多点发射与接收漫反射检测方式下的糖度模型预测相关系数为0.76，预测均方根误差为0.53°Brix。研究表明，应用PLS建模结合近红外漫反射光谱对传统外部品质分选线中的糖度分选线功能进行升级改造是可行的。Abstract: Traditional quality sorting methods have been unable to meet people's increasing demands for fruit flavour and quality. Producers must therefore develop their traditional quality sorting methods to achieve sugar content sorting and ensure favourable flavour and quality. To address this, the near-infrared reflection spectra of navel oranges were collected separately through two different detection methods. The spectral energy of their ring transmission and diffuse reflection had to be stronger than that of the multi-point transmission and diffuse reflections. The positions of their peaks and troughs had to be approximately the same. The near-infrared diffuse reflectance spectra were preprocessed using baseline correction, multivariate scattering correction, first and second derivatives to reduce the influence of stray light and noise. A Partial Least Squares (PLS) model for the sugar content information that was collected through the two different reflection detection methods was established for their comparison and analysis. The experimental results show that the baseline correction preprocessing method produced the best results between the two methods. Its predicted correlation coefficient of sugar under ring transmission and diffuse reflection detection was 0.81 and its root mean square error was 0.46° Brix. The estimated correlation coefficient of the sugar content model using the multi-point transmission and diffuse reflection detection method was 0.76 and its root mean square error was 0.53° Brix. This research shows that it is feasible to use PLS modeling and near-infrared diffuse reflectance spectrum to upgrade the sugar content sorting methodology used on production lines.
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图 2 近红外漫反射在线检测装置示意图
Figure 2. Schematic diagram of online detection device of near infrared diffuse reflection
图 4 光纤在不同出射角度下的光强图
Figure 4. Light intensity charges of optical fiber at different exit angles
图 5 两种不同漫反射式及相应检测光纤探头的发射接收端
Figure 5. Two kinds of diffuse reflection detection modes and corresponding optical fiber probes
图 6 两种光纤探头下参比球光谱的标准偏差
Figure 6. Standard deviations of reference sphere spectra under two different kinds of fiber probes
图 7 两种不同检测方式下的可见近红外漫反射光谱
Figure 7. Visible near-infrared diffuse reflectance spectra with two different kinds of detection modes
图 8 两种不同检测方式下脐橙实果与脐橙果皮的近红外漫反射光谱
Figure 8. Visible near-infrared diffuse reflectance spectra of navel orange and peel with two different kinds of detection modes
图 9 环形发射与接收检测方式下基线校正前后光谱图
Figure 9. Spectra obtained by ring transmission and diffuse reflection mode before and after pretreatment with baseline correction
图 10 多点发射与接收检测方式下基线校正前后光谱对比
Figure 10. Spectra obtained by multi-point transmission and diffuse reflection mode before and after pretreatment with baseline correction
图 11 两种漫反射方式下预测集的糖度模型主成分因子图
Figure 11. Principal component factor charts of prediction set obtained with two kinds of diffuse reflection methods
图 12 两种不同漫反射方式下的回归系数图
Figure 12. Regression coefficients obtained with two different kinds of diffuse reflection methods
图 13 环形发射与接收模式下偏最小二乘模型预测散点图
Figure 13. Predicted scatter plot with partial least squares model using ring transmission and diffuse reflection mode
图 14 多点发射与接收模式下偏最小二乘模型预测散点图
Figure 14. Predicted scatter plot with partial least squares model using multi-point transmission and diffuse reflection mode
表 1 两种不同近红外漫反射光谱检测方式结合不同预处理方法时的脐橙糖度PLS建模结果
Table 1. PLS modeling results of navel orange sugar content using two different near-infrared diffuse reflectance spectroscopy methods combined with different pretreatment methods
检测方式 预处理方法 Rc RMSEC Rp RMSEP PCs 环形发射和接收 无预处理 0.89 0.39 0.82 0.43 12 MSC 0.89 0.41 0.75 0.51 10 Baseline 0.90 0.38 0.81 0.46 11 1st-9 0.83 0.48 0.80 0.46 9 2st-13 0.95 0.26 0.73 0.58 15 SG-5 0.86 0.45 0.77 0.51 15 多点发射和接收 无预处理 0.92 0.31 0.77 0.51 9 MSC 0.91 0.33 0.72 0.59 8 Baseline 0.94 0.28 0.76 0.53 10 1st-3 0.87 0.40 0.79 0.50 6 2st-13 0.84 0.45 0.75 0.53 6 SG-5 0.82 0.48 0.81 0.47 6 
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