香蕉贮藏中腐败基准确定与高光谱信息表征及腐败预警模型构建

doi:10.3964/j.issn.1000-0593(2020)12-3871-07

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摘要：为了实现香蕉在贮藏过程中的腐败预警，对不同贮藏时间的香蕉进行高光谱数据采集。应用Savitzky-Golar（SG）平滑对原始光谱进行降噪处理，以获得少噪声干扰的光谱数据。提出一种基于Wilks Λ统计量结合主成分信息融合的高光谱特征波长提取方法。该方法可描述为：先对降噪后的高光谱数据进行主成分分析（PCA），然后对获取的主成分数据分别构建Wilks Λ统计量，进而可选出Wilks Λ值最小的主成分变量（第3主成分，PC3），绘制出该主成分对应于原始变量（各波长）下的组合权重系数图，将权重系数图中的波峰、波谷所对应的波长变量提取出来即为该法提取出的特征波长。按此方法共提取出9个特征波长。同时，对香蕉的色差数据进行分析，通过分析测试样本的L^*，a^*，b^*，ΔE值随贮藏时间的变化趋势，找到数据异常点（拐点）所在位置，并结合实际感官情况初步确定了腐败基准为第6个贮藏日。为了进一步说明所确定的腐败基准的合理性，应用特征光谱数据做出不同贮藏时间样本平均光谱反射值曲线，对比发现在不同特征波长下光谱反射值均在贮藏第6天达到最小值，与色差指标分析结果一致，进一步证明了腐败基准选取的正确性。所以，可用第6个贮藏日的特征光谱信息来表征基准信息，以生成腐败基准的特征光谱表征向量及其对应的协方差矩阵。最后，以表征腐败基准的光谱特征向量为基点，采用马氏距离（MD）构建了香蕉贮藏过程中基于高光谱特征波长信息的腐败预警模型，并对模型进行了验证。结果表明：随着香蕉贮藏时间的延长，该预警模型给出的测试样本与腐败基准的距离越来越近，吻合香蕉腐败的实际进程。因此，所提出的高光谱特征波长提取方法、腐败基准的确定及其表征方法以及腐败预警高光谱模型是合适的，能有效地预警香蕉腐败。

关键词：香蕉；色差；高光谱；Wilks Λ统计量；马氏距离；预警模型

Abstract：In order to realize the spoilage warning of bananas during storage, hyperspectral data of bananas with different storage time was collected. The original spectrum was smoothed by Savitzky-Golar (SG) so as to obtain the spectral data with less noise interference. And a method of extracting the feature wavelength from hyperspectral information based on Wilks Λ statistic combined with principal component information fusion is proposed. The method could be described as follows: firstly, principal component analysis (PCA) was applied to the hyperspectral data after SG treatment, then the corresponding Wilks Λ statistics value was calculated for the each obtained principal component, and then the principal component variable with the lowest Wilks Λ value (the third principal component, PC3) could be selected. And the variation curve of combination weight coefficient corresponding to the original variable (each wavelength) of the PC3 was plotted. The wavelengths variable corresponding to the wave peaks and valleys in the weight coefficient curve were picked to be as the feature wavelengths. According to this method, totaling 9 feature wavelengths were extracted. At the same time, the color difference data of the banana was analyzed. By analyzing the trend of the L^*, a^*, b^* and ΔE values of the test samples with the storage time, the position of the abnormal data point (inflection point) was found. Combined with the actual sensory situation, the spoilage benchmark was initially determined as the sixth storage day. In order to further illustrate the rationality of the given spoilage benchmark, the feature spectral data was used to make the average spectral reflectance curves of different storage time samples. It was found that the spectral reflectance values reached the minimum value at the 6th day of storage for the different feature wavelengths, which was consistent with analysis results based on the color difference index, it was further determined that the spoilage benchmark was indeed the sixth storage day. Thus, the benchmark information could be characterized by the feature spectral data of the sixth storage day, and then the feature spectral representation vector and corresponding covariance matrix of the spoilage benchmark could be generated. Finally, took the hyperspectral feature vector characterizing the spoilage benchmark as a reference point, a spoilage early warning model based on feature wavelength spectrum information during banana storage was established by Mahalanobis distance (MD) and was verified effectively. The results showed that the MD between the test samples and spoilage benchmark given by the early warning model was getting closer and closer with the extension of banana storage time, which was consistent with the actual process of banana spoilage. Therefore, the proposed hyperspectral feature wavelength extraction method, determination method of spoilage benchmark, the representation method and the hyperspectral warning model are suitable and effective for early warning of banana spoilage.

Key words：Banana; Color difference; Hyperspectral; Wilks Λ statistic; Mahalanobis distance; Early warning model

收稿日期: 2019-11-07 修订日期: 2020-03-22

中图分类号:

TS205.9

基金资助: 国家重点研发计划项目（2017YFC1600802）资助

通讯作者: 殷勇 E-mail: yinyong@haust.edu.cn

作者简介: 薛书凝，1996年生，河南科技大学食品与生物工程学院硕士研究生 e-mail：1653456810@qq.com

引用本文:

薛书凝，殷勇，于慧春，袁云霞，马帅帅. 香蕉贮藏中腐败基准确定与高光谱信息表征及腐败预警模型构建[J]. 光谱学与光谱分析, 2020, 40(12): 3871-3877.
XUE Shu-ning, YIN Yong, YU Hui-chun, YUAN Yun-xia, MA Shuai-shuai. Determination of Spoilage Benchmark and Its Hyperspectral Information Representation Method as Well as Construction of Hyperspectral Based Spoilage Early Warning Model During Banana Storage. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3871-3877.

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