Zhang Zheng-Yong
State Key Laboratory of Dairy Biotechnology, Shanghai Engineering Research Center of Dairy Biotechnology, Dairy Research Institute, Bright Dairy & Food Co., Ltd. Shanghai 200436 The People's Republic of China.
School of Management Science and Engineering, Nanjing University of Finance and Economics Nanjing Jiangsu 210023 The People's Republic of China
RSC Adv. 2020 Aug 11;10(50):29682-29687. doi: 10.1039/d0ra06318e. eCollection 2020 Aug 10.
At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335-354 cm, 435-454 cm, 485-540 cm, 820-915 cm, 1155-1185 cm, 1300-1414 cm, and 1415-1520 cm under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control.
目前，用于乳制品的实用且快速的识别技术仍然匮乏。以不同品牌的巴氏杀菌乳为例，它们外观均为乳白色，拉曼光谱非常相似，因此直接用肉眼识别是不可行的。在当前工作中，展示了一种基于拉曼光谱和支持向量机（SVM）算法相结合的清晰特征提取与融合策略。结果表明，基于原始拉曼全光谱数据，乳制品的平均识别准确率为58%，基于单个光谱区间时可达近70%。数据归一化处理有效提高了识别准确率。基于归一化拉曼全光谱数据，乳制品的平均识别准确率达到91%，基于归一化单个光谱区间时接近85%。多光谱特征区域的融合产生了高精度和高运行效率。基于SVM算法进行筛选和优化后，在实验条件下确定最佳光谱特征区间为335 - 354厘米、435 - 454厘米、485 - 540厘米、820 - 915厘米、1155 - 1185厘米、1300 - 1414厘米和1415 - 1520厘米，此处平均识别准确率达到93%。所开发的方案具有特征提取与融合清晰、识别时间短的优点，为食品质量控制提供了技术参考。
