Department of Food Engineering, Canakkale Onsekiz Mart University, Canakkale, Turkey.
J Sci Food Agric. 2020 Mar 30;100(5):1980-1989. doi: 10.1002/jsfa.10211. Epub 2020 Feb 5.
In this study, an infrared-based prediction method was developed for easy, fast and non-destructive detection of pesticide residue levels measured by reference analysis in strawberry (Fragaria × ananassa Duch, cv. Albion) samples using near-infrared spectroscopy and demonstrating its potential alternative or complementary use instead of traditional pesticide determination methods. Strawberries of Albion variety, which were supplied directly from greenhouses, were used as the study material. A total of 60 batch sample groups, each consisting of eight strawberries, was formed, and each group was treated with a commercial pesticide at different concentrations (26.7% boscalid + 6.7% pyraclostrobin) and varying residual levels were obtained in strawberry batches. The strawberry samples with pesticide residuals were used both to collect near-infrared spectra and to determine reference pesticide levels, applying QuEChERS (quick, easy, cheap, rugged, safe) extraction, followed by liquid chromatographic-mass spectrometric analysis.
Partial least squares regression (PLSR) models were developed for boscalid and pyraclostrobin active substances. During model development, the samples were randomly divided into two groups as calibration (n = 48) and validation (n = 12) sets. A calibration model was developed for each active substance, and then the models were validated using cross-validation and external sets. Performance evaluation of the PLSR models was evaluated based on the residual predictive deviation (RPD) of each model. An RPD of 2.28 was obtained for boscalid, while it was 2.31 for pyraclostrobin. These results indicate that the developed models have reasonable predictive power. © 2019 Society of Chemical Industry.
本研究旨在开发一种基于近红外光谱的预测方法，用于快速、无损地检测草莓（Fragaria×ananassa Duch，cv.Albion）样品中通过参考分析测量的农药残留水平，该方法使用近红外光谱，且可替代或补充传统的农药检测方法。本研究以阿尔比恩品种的草莓为研究材料，该品种草莓直接由温室供应。共形成 60 批样本组，每组 8 个草莓，每个组用不同浓度（26.7% 咯菌腈+6.7% 吡唑醚菌酯）的商业农药处理，草莓批次中获得不同的残留水平。使用含有农药残留的草莓样本采集近红外光谱并测定参考农药水平，应用 QuEChERS（快速、简单、廉价、耐用、安全）提取，然后进行液相色谱-质谱分析。
为咯菌腈和吡唑醚菌酯活性物质开发了偏最小二乘回归（PLSR）模型。在模型开发过程中，将样本随机分为两组，一组为校准（n=48），另一组为验证（n=12）。为每种活性物质开发了一个校准模型，然后使用交叉验证和外部集验证模型。基于每个模型的剩余预测偏差（RPD）对 PLSR 模型的性能进行评估。咯菌腈的 RPD 为 2.28，吡唑醚菌酯的 RPD 为 2.31。这些结果表明，所开发的模型具有合理的预测能力。©2019 化学工业协会。
