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Applying near infrared spectroscopy to the detection of fruit fly eggs and larvae in intact fruit

机译:应用近红外光谱检测完整水果中的果蝇卵和幼虫

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摘要

The objective of this work was to investigate the potential use of near infrared (NIR) spectroscopy for non-destructive detection of fruit fly eggs and larvae in intact fruit. Mangoes, the major export fruit of Thailand, were used as model samples. The NIR spectra acquired under interactance mode in the short wavelength region of 700 nm to 1100 nm provided the best classification results, compared with spectra taken under the reflectance mode in the long wavelength region from 1100 nm to 2500 nm. The dominant factor in correct classification was the incubation period after infestation. The best classification was achieved using spectra of green mangoes obtained 48h after infestation, with an error rate of 4.2% (two out of 48) for infested fruit and 0% for the 48 control fruit. Comparing regression coefficient plots of various partial least squares discriminant analysis (PLS-DA) models, it was determined that the most important classification wavelength was near 730 nm, coinciding with a unique peak previously observed in the spectra of dried fruit fly larvae. A universal calibration developed from two mango cultivars produced similar error rates. The results justify development of an automatic classification and sorting system based on NIR imaging technology.
机译:这项工作的目的是研究将近红外(NIR)光谱技术用于无损检测完整水果中的果蝇卵和幼虫的潜在用途。芒果是泰国的主要出口水果,被用作模型样品。与在1100nm至2500nm的长波长区域中的反射模式下获得的光谱相比,在700nm至1100nm的短波长区域中在相互作用模式下获得的NIR光谱提供了最佳的分类结果。正确分类的主要因素是侵染后的潜伏期。使用侵染后48小时获得的绿色芒果光谱可实现最佳分类,受侵染的水果的错误率是4.2%(48个中的两个),而对照48个水果的错误率是0%。比较各种偏最小二乘判别分析(PLS-DA)模型的回归系数图,可以确定最重要的分类波长在730 nm附近,这与干果蝇幼虫光谱中先前观察到的唯一峰一致。由两个芒果品种开发的通用标定产生相似的错误率。结果证明了开发基于近红外成像技术的自动分类和分类系统的合理性。

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