School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Jan 5;206:378-383. doi: 10.1016/j.saa.2018.07.049. Epub 2018 Jul 17.
In order to identify the moldy tea leaves in a fast and nondestructive way, a method involving wavelet coupled with k-nearest neighbor (WKNN) was proposed to select effective characteristic wavelengths in this paper. The hyperspectral imaging of 300 dried tea samples with 3 different mildew degrees (contrast check, mild moldy and severe moldy) were obtained using hyperspectral data acquisition device. Besides, food microbiological examination results showed that mold count and total numbers of colony increased with the increase of storage time, temperature and humidity. Roughness penalty smoothing (RPS) algorithm was used to preprocess the raw spectra. Afterwards, WKNN was applied to select the optimal wavelengths of spectral data by using db4, db6, sym5, sym7 as wavelet basis functions, respectively. In addition, five layers of wavelet decomposition were adopted based on different wavelet basis functions. Linear discriminant analysis (LDA) algorithm was used to build the classification models based on preprocessed spectra feature in characteristic wavelengths. The results showed that four optimal prediction models were optimal decomposition level in each wavelet basis function. In addition, the best performance model among all LDA models achieved an identification rate of 100% in the calibration set and 98.33% in the prediction set, in which db4 was used as wavelet basis function and the optimal wavelet decomposition level was 2. WKNN algorithm can effectively achieve the best wavelet decomposition layer and the best wavelengths. WKNN algorithm combined with NIR hyperspectral imaging technology can realize the effective wavelength extraction and classification of dried tea with different mildew degrees.
为了快速无损地识别霉变茶叶，本文提出了一种基于小波结合 K 最近邻（WKNN）的方法来选择有效的特征波长。使用高光谱数据采集设备获取了 300 个不同霉变程度（对照检查、轻度霉变和重度霉变）的干燥茶叶样本的高光谱图像。此外，食品微生物检验结果表明，霉菌数量和菌落总数随储存时间、温度和湿度的增加而增加。采用 Roughness Penalty Smoothing（RPS）算法对原始光谱进行预处理。然后，分别使用 db4、db6、sym5 和 sym7 作为小波基函数，应用 WKNN 选择光谱数据的最佳波长。此外，基于不同的小波基函数采用了五层小波分解。基于预处理光谱特征的特征波长，采用线性判别分析（LDA）算法建立分类模型。结果表明，在每个小波基函数中，四个最优预测模型都是最优的分解水平。此外，所有 LDA 模型中性能最好的模型在标定集和预测集中的识别率均达到 100%，其中 db4 作为小波基函数，最优小波分解水平为 2。WKNN 算法可以有效地实现最佳小波分解层和最佳波长。WKNN 算法结合近红外高光谱成像技术，可实现不同霉变程度的干燥茶叶的有效波长提取和分类。
