Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid‐infrared (FT‐MIR) spectrometry combined with multivariate statistical analysis,Food Science & Nutrition

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Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid‐infrared (FT‐MIR) spectrometry combined with multivariate statistical analysis
Food Science & Nutrition ( IF 3.5 ) Pub Date : 2020-01-14 , DOI: 10.1002/fsn3.1313
Xiu-Ping Li _{1,

2} , Jieqing Li ₁ , Tao Li ₃ , Honggao Liu ₁ , Yuanzhong Wang _{1,

2}

Affiliation

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time‐consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid‐infrared (FT‐MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t‐distributed stochastic neighbor embedding based on mid‐level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS‐DA models was mid‐level data fusion^q (100%) > mid‐level data fusion^e (97.06%) = mid‐level data fusion^v (97.06%) = stipes (97.06%) > low‐level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid‐search support vector machine models was low‐level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support vector machine was low‐level data fusion (100%) > caps (97.06%) > stipes (88.24%). The mid‐level data fusion^q and low‐level data fusion had best discrimination accuracy (100%) allowing each mushroom classed into its real species, which could be used for accurate discrimination of samples. B. edulis mushrooms had highest total polyphenol, with 14.76 mg/g dw and 17.33 in caps and stipes mg/g dw, respectively. The phenols were easier to accumulate in the caps in Leccinum rugosiceps (1.03) and B. tomentipes (1.19), and the opposite phenomenon is observed in B. edulis (0.85) and B. umbriniporus (0.95). The correlation coefficient and residual predictive deviation of best prediction model were 86.76% and 2.40%, respectively, indicating that that there is good relevance between FT‐MIR and total polyphenol content, which could be used to predict roughly polyphenols content in mushrooms.

中文翻译：

傅里叶变换中红外（FT-MIR）光谱法结合多元统计分析牛肝菌的物种判别和总多酚预测

高原特产农产品——野生牛肝菌，既是美味佳肴，又具有巨大的药用价值。由于品种之间的品质差异，加上销售过程中的造假行为，使得劣质或有毒样品流入市场，给消费者带来健康风险，也扰乱了食用菌市场。传统的分析方法费时费力。因此，本研究的目的是开发一种使用傅里叶变换中红外（FT-MIR）光谱测定和数据融合策略的方法，以快速准确地进行物种区分并预测四种牛肝菌中的总多酚含量。基于中级数据融合的 t 分布随机邻域嵌入表明已识别出两种美味牛肝菌 (Boletus edulis)和B. umbriniporus 。 PLS-DA模型正确率顺序为中级数据融合^q （100%）>中级数据融合^e （97.06%）=中级数据融合^v （97.06%）=柄（97.06%）>低级数据融合（94.12%）>上限（91.18%）。网格搜索支持向量机模型正确率顺序为低级数据融合（100%）> caps（94.12%）> stipes（91.18%），粒子群优化支持向量机模型正确率顺序为低级数据融合（100%）> caps（94.12%）> stipes（91.18%）。数据融合（100%）>帽（97.06%）>菌柄（88.24%）。中层数据融合^q和低层数据融合具有最好的判别准确率（100%），可以将每个蘑菇分类到其真实物种中，可用于样本的准确判别。蘑菇的总多酚含量最高，菌盖和菌柄中的多酚含量分别为 14.76 毫克/克干重和 17.33 毫克/克干重。 Leccinum rugosiceps (1.03) 和B. tomentipes (1.19) 中酚类化合物更容易积聚在菌盖中，而在B. edulis (0.85) 和B. umbriniporus (0.95) 中观察到相反的现象。最佳预测模型的相关系数和残差预测偏差分别为86.76%和2.40%，表明FT-MIR与总多酚含量有较好的相关性，可用于粗略预测蘑菇中多酚含量。

更新日期：2020-01-14

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