State Key Laboratory Breeding Base of Systematic Research, Development and Utilization of Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
Food Funct. 2018 Nov 14;9(11):5903-5911. doi: 10.1039/c8fo01376d.
Porcini are a source of popular food products with many beneficial functions and the internal quality of these mushrooms is largely determined by many factors. An additional data fusion strategy based on low-level data fusion for two portions (cap and stipe) and mid-level data fusion for two spectroscopic techniques (UV and FTIR) was developed to discriminate porcini mushrooms from different species and origins. Based on a finally obtained data array, four mathematical algorithms including PLS-DA, k-NN, SVM and RF were comparatively applied to build classification models. Each calibrated model was developed after selecting the best debug parameters and then a test set was used to validate the established model. The results showed that the SVM algorithm based on a GA procedure searching for parameters had the best performance for discriminating different porcini samples with the highest cross-validation, specificity, sensitivity and accuracy of 100.00%. Our study proved the feasibility of two spectroscopic techniques for the discrimination of porcini mushrooms originated from different species and origins. This proposed method can be used as an alternative strategy for the quality detection of porcini mushrooms.
牛肝菌是一种受欢迎的食品，具有许多有益的功能，这些蘑菇的内部质量在很大程度上取决于许多因素。本研究提出了一种基于低水平数据融合（两部分，菌盖和菌柄）和中水平数据融合（两种光谱技术，UV 和 FTIR）的附加数据融合策略，用于区分不同种类和来源的牛肝菌。基于最终获得的数据数组，应用了包括 PLS-DA、k-NN、SVM 和 RF 在内的四种数学算法来构建分类模型。每个校准模型都是在选择最佳调试参数后开发的，然后使用测试集来验证建立的模型。结果表明，基于 GA 程序搜索参数的 SVM 算法在区分不同牛肝菌样本方面表现最佳，交叉验证、特异性、敏感性和准确性的最高值分别为 100.00%。本研究证明了两种光谱技术用于区分不同种类和来源的牛肝菌的可行性。该方法可以作为牛肝菌质量检测的替代策略。
