Online detection of blood‐spot eggs based on a novel method of spectral amplitude space conversion

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Abstract To improve the robustness of the online detection system for blood‐spot eggs, a linear converter was established based on Levenberg–Marquardt (LM) algorithm to transform spectral signals in the amplitude space, so that error introduced from the variation of sample, mechanical device, and other factors can be reduced to a certain degree. First, the transmission spectrum in the 500–599 nm bandwidth of each sample was selected. After preprocessed with the moving average filtering, the spectrum was transformed in the space of spectral amplitude using the LM algorithm. Then, the converted spectrum was utilized to establish the backpropagation neural network model. As the result, the classification model using the transformed spectra realized more than 96% accuracy. When the detection system dealt with different batches of samples produced on different dates, the discriminant accuracy using LM converter was significantly higher than that without (p value < .05). Meanwhile, when the detection system ran at different speeds, the model still showed good robustness, and the accuracy was increased to some degree with LM converter. The research indicated the technical superiority of the model built with the proposed spectra converter over the model without and provided a feasible method to detect blood‐spot eggs online for commercial usage. Practical Applications Blood‐spot eggs need to be detected and removed before sale or export. During the task of online detection, the variation of sample batch, conveyor speed, and other factors can introduce error to the predicting outcomes of the classification model. Thus, it is essential for the detection system to stay robust and realize high‐accuracy identification under variable external factors. To this end, a novel linear converter for transforming spectral signals based on Levenberg–Marquardt algorithm was studied in this paper. The results showed that the proposed converter could increase the classification accuracy and robustness of detection system to a certain degree, thus realizing accurate and fast online detection of blood‐spot eggs.

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