基于电子舌分析建立西瓜品质特性的预测模型

Models establishment of watermelon qualities based on electronic tongue analysis

摘要

为了探索电子舌技术快速检测西瓜内部品质的方法，以 L600 西瓜为试验材料，利用电子舌技术对 200 个西瓜的味觉成分进行检测，用传统检测法测定可溶性固形物、可滴定酸、水分、维生素 C 和总酚含量，糖酸比及 pH，采用偏最小二乘法、随机森林、支持向量机和 K-近邻算法建立品质的预测模型。结果表明，在生长过程中，西瓜的可溶性固形物含量、糖酸比、维生素 C 含量和总酚含量呈升高趋势，pH 呈先升高后下降的变化趋势，可滴定酸和水分含量呈逐渐下降趋势。在模型预测结果中，西瓜可溶性固形物含量、糖酸比、pH 和水分含量等指标的模型预测效果优于可滴定酸、维生素 C 和总酚含量。随机森林算法对西瓜可溶性固形物含量、可滴定酸含量、糖酸比和水分含量的预测 R_P²分别为 0.884、0.798、0.891 和 0.875，分别比偏最小二乘法提高了 16.9%、19.1%、21.6%和 13.6%；偏最小二乘法对 pH 的预测 R_P²为 0.881，比支持向量机算法提高了 31.7%；K-近邻算法对维生素 C 和总酚含量的预测 R_P²为 0.731 和 0.753，分别比随机森林算法提高了 1.67%和 24.5%。以上结果表明应用电子舌技术预测西瓜的内部品质是可行的。

Abstract

In order to explore the E-tongue technology for rapid detection of internal quality of watermelon, the taste components of 200 watermelons were examined using the E-tongue technology with L600 watermelon as the test material, soluble solids, titratable acid, moisture, vitamin C and total phenol content, sugar-acid ratio and pH were determined by traditional detection methods, and partial least squares, random forest, support vector machine and K-nearest neighbour algorithms were used to establish quality prediction model. The results showed that soluble solids content, sugar-acid ratio, vitamin C content and total phenol content of watermelon tended to increase, pH tended to increase and then decrease, and titratable acid and water content tended to decrease during the growth process. In the model prediction results, the model prediction of the qualities of watermelon soluble solids content, sugar-acid ratio, pH and moisture content were better than titratable acid, vitamin C and total phenol content. The R _P ² of the random forest algorithm for the prediction of watermelon soluble solids content, titratable acid content, sugar-acid ratio and moisture content were 0.884, 0.798, 0.891 and 0.875, which were 16.9%, 19.1%, 21.6% and 13.6% higher than those of the partial least square algorithm, respectively; and the R _P ² of the partial least square algorithm for pH was 0.881, which was 31.7% higher than that of the support vector machine algorithm; and the K-nearest neighbour algorithm predicted vitamin C and total phenol content with R _P ² of 0.731 and 0.753, which were 1.67% and 24.5% higher than the random forest algorithm, respectively. The results indicated that it is feasible to apply the electronic tongue technique to predict the internal quality of watermelon.

关键词

西瓜 / 电子舌 / 品质预测

Key words

Watermelon / Electronic tongue / Quality prediction

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基金资助

国家现代农业产业技术体系(CARS-25)

国家自然科学基金(32172237)

北京市农林科学院协同创新中心资助项目(KJCX20240402)