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Distinguishing Bovine Fecal Matter on Spinach Leaves Using Field Spectroscopy

机译:场光谱法鉴别菠菜叶上的牛粪便

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Detection of fecal contaminants on leafy greens in the field will allow for decreasing cross-contamination of produce during and post-harvest. Fecal contamination of leafy greens has been associated with Escherichia coli ( E. coli ) O157:H7 outbreaks and foodborne illnesses. In this study, passive field spectroscopy measuring reflectance and fluorescence created by the sun’s light, coupled with numerical normalization techniques, are used to distinguish fecal contaminants on spinach leaves from soil on spinach leaves and uncontaminated spinach leaf portions. A Savitzky-Golay first derivative transformation and a waveband ratio of 710:688 nm as normalizing techniques were assessed. A soft independent modelling of class analogies (SIMCA) procedure with a 216 sample training set successfully predicted all 54 test set sample types using the spectral region of 600–800 nm. The ratio of 710:688 nm along with set thresholds separated all 270 samples by type. Application of these techniques in-field to avoid harvesting of fecal contaminated leafy greens may lead to a reduction in foodborne illnesses as well as reduced produce waste.
机译:在田间绿叶蔬菜上检测粪便中的污染物将有助于减少收获期间和收获后产品的交叉污染。粪便污染绿叶蔬菜已与大肠杆菌O157:H7暴发和食源性疾病有关。在这项研究中,通过测量太阳光产生的反射率和荧光的无源光谱技术,结合数值归一化技术,可以将菠菜叶上的粪便污染物与菠菜叶和未污染的菠菜叶部分的土壤区别开来。评估了Savitzky-Golay一阶导数变换和710:688 nm的波段比作为归一化技术。具有216个样本训练集的类比模拟(SIMCA)程序的软独立建模成功地使用了600–800 nm的光谱区域预测了所有54种测试集样本类型。 710:688 nm的比率以及设置的阈值按类型将所有270个样品分开。在野外应用这些技术以避免收获受粪便污染的绿叶蔬菜,可减少食源性疾病并减少农产品浪费。

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