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.

中文翻译：

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结合四种数学算法，用于区分不同物种和来源的牛肝菌的另一种数据融合策略†

牛肝菌是具有许多有益功能的流行食品的来源，这些蘑菇的内部质量在很大程度上取决于许多因素。开发了基于两个部分（帽和柄）的低级数据融合和两种光谱技术（UV和FTIR）的中级数据融合的附加数据融合策略，以区分来自不同物种和产地的牛肝菌。基于最终获得的数据阵列，包括PLS-DA，k和k在内的四种数学算法-NN，SVM和RF被比较地应用于建立分类模型。选择最佳调试参数后，将开发每个校准模型，然后使用测试集来验证已建立的模型。结果表明，基于遗传算法搜索参数的支持向量机算法在区分不同牛肝菌样品方面具有最好的性能，交叉验证，特异性，敏感性和准确性最高，均为100.00％。我们的研究证明了两种光谱技术用于区分源自不同物种和起源的牛肝菌的可行性。该提议的方法可用作牛肝菌蘑菇质量检测的替代策略。