College of Electronic Engineering, Guangxi Normal University, Guilin 541004, China.
UCD School of Biosystems and Food Engineering, University College of Dublin (UCD), Belfield Dublin 4, Ireland.
Molecules. 2020 Oct 28;25(21):4987. doi: 10.3390/molecules25214987.
Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.
不同品种和产地的核桃通常导致不同的营养价值，最终价格也存在很大差异。传统的分析技术存在一些不可避免的局限性，例如，化学分析通常既耗时又费力。因此，本工作旨在应用傅里叶变换中红外光谱结合机器学习算法，快速准确地对来自四个省份的十种不同品种的核桃进行分类。使用五种机器学习分类器开发了三种类型的模型，以（1）区分四个产地；（2）识别来自同一产地的品种；以及（3）对来自四个产地的所有 10 个品种进行分类。在建模之前，使用小波变换算法对光谱进行平滑和去噪。结果表明，同种起源品种的识别效果最好（即某些起源的准确率为 100%），其次是四个不同起源的分类（即准确率为 96.97%），而对所有 10 个品种的区分效果最差（即准确率为 87.88%）。我们的结果表明，对于某些分类器而言，使用全光谱范围（700-4350cm）不如使用最佳光谱变量子集效果好。此外，研究还表明，反向传播神经网络（BPNN）的模型性能最佳，而随机森林（RF）的效果最差。因此，本研究表明，基于傅里叶变换中红外光谱结合机器学习算法可以有效地实现核桃的认证和产地溯源。
