On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy
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摘要: 为了实现不同产地苹果糖度的快速在线无损检测，减少产地差异对近红外光谱检测模型的影响，建立了不同产地苹果糖度的在线检测通用模型。首先，采用水果动态在线检测设备采集了包括栖霞、洛川与会宁3个产地的红富士苹果的漫透射光谱。其次，采用偏最小二乘算法（PLS），结合无信息变量消除（UVE）方法，筛选出58个特征变量，建立了苹果糖度的UVE-PLS通用模型，该模型对个体产地预测集及总预测集的均方根误差分别为0.50~0.74°Brix与0.63°Brix，较原始个体模型分别提高了23.2%~44.4%与35.7%。最后，提出了一个新的外部验证样本集对模型性能进行评价，其残留预测偏差为2.33，预测值在±1.0°Brix和±1.5°Brix误差范围内的占比分别为85%与100%。实验结果表明：建立多个产地苹果糖度的在线检测通用模型，能够提高其他产地样本糖度的预测稳健性，并且采用合适的波长筛选方法能够简化模型。开发不同产地水果内部品质通用模型在波长有限的光谱设备中具有良好的应用潜力。Abstract: In order to realize fast, on-line, non-destructive testing of the Soluble Solids Content (SSC) of apples from different origins, and to reduce the effect of origin variability on NIR models, a universal model for predicting the SSC of apples from different origins is established. Firstly, the diffuse transmission spectra of Fuji apples from Qixia, Luochuan and Huining are collected with fruit dynamic online detection equipment. Then, 58 characteristic variables are selected and a UVE-PLS universal model for predicting the SSC of apples is established using the Partial Least Squares (PLS) algorithm combined with Uninformative Variable Elimination (UVE). The root mean square errors of single-origin prediction sets and the total-origin prediction set are 0.50~0.74° Brix and 0.63° Brix, respectively, which increase by 23.2%~44.4% and 35.7% respectively compared to the original individual model. Finally, a new external sample set is used to assess the performance of the model, showing a residual prediction deviation of 2.33 and ratios of the predicted values within the error range of ±1.0° Brix and ±1.5° Brix of 85% and 100%, respectively. Experimental results indicate that the establishment of a universal model for on-line detection of the SSC of apples, including those from multiple origins can improve the robustness of predicting the SSC of the samples from other origins. The results also show that an appropriate wavelength screening method can simplify the model. The development of a common model for the internal quality of fruit from different origins has strong potential for applications in wavelength-limited spectroscopy equipment.
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Key words:
- online detection /
- near infrared spectroscopy /
- soluble solids /
- partial least squares
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图 4 经过MSC的苹果光谱的前3个主成分得分图
Figure 4. The first three PC score plot of apple spectra after MSC processing
图 6 UVE-PLS通用模型糖度测量值与预测值的散点图
Figure 6. Scatter plots of measured values versus predicted values for SSC by using UVE-PLS universal model
图 7 糖度测量值与预测值的散点图
Figure 7. Scatter plot of measured value versus predicted value of SSC for a new external sample set
表 1 样本集糖度含量统计
Table 1. Statistical values of the SSC（°Brix）for sample sets
产地 校正集 预测集 数量 范围 平均值 标准差 数量 范围 平均值 标准差 1 129 8.8~16.6 12.93 1.43 43 9.3~15.5 12.47 1.24 2 135 8.5~16.4 13 1.28 41 8.9~15.1 12.93 1.23 3 127 10.1~18.2 14.97 1.21 40 11.7~17.6 15.03 1.17 总 391 8.5~18.2 13.62 1.61 124 8.9~17.6 13.45 1.64 
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表 2 单个产地的PLS建模结果
Table 2. Results of PLS modeling for single origin
产地 LVs 校正集 预测集 RPD $R_{\rm{C}}^2$ RMSEC（°Brix） $R_{\rm{P}}^2 $ RMSEP（°Brix） 1 9 0.92 0.40 0.90 0.41 3.02 2 10 0.89 0.42 0.85 0.47 2.62 3 11 0.86 0.46 0.80 0.51 2.29
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表 3 不同产地红富士苹果的预测结果
Table 3. Prediction results of Fuji apples from different origins
产地 预测集 1 2 3 总 $R_{\rm{P}}^2 $ RMSEP（°Brix） $R_{\rm{P}}^2 $ RMSEP（°Brix） $R_{\rm{P}}^2 $ RMSEP（°Brix） $R_{\rm{P}}^2 $ RMSEP（°Brix） 1 / / 0.54 0.82 0.73 1.24 0.42 1.30 2 0.54 0.90 / / 0.67 1.34 0.67 0.98 3 0.73 1.44 0.72 1.25 / / 0.68 1.27
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表 4 苹果糖度通用模型预测结果
Table 4. Results of SSC of apples predicted by universal modeling
模型 变量数 LVs $R_{\rm{C}}^2 $ RMSEC（°Brix） $R_{\rm{P}}^2 $ RMSEP（°Brix） RPD Ori-PLS 400 12 0.84 0.64 0.85 0.63 2.60 UVE-PLS 58 8 0.82 0.68 0.85 0.63 2.60
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表 5 UVE-PLS糖度通用模型的实际性能
Table 5. The practical performance of UVE-PLS universal model for SSC
RMSEP/(°Brix) RPD 栖霞 洛川 会宁 总 总 0.67 0.51 0.72 0.64 2.33
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