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The nondestructive assessment of nitrogen status in strawberry was performed to analyze its growth and provide guidance for precise management of N fertilizer using Fourier transform near-infrared reflectance (FT-NIR) spectroscopy with leaf spectral reflectance. The leaf soil plant analysis development (SPAD) value is considered as an indicator that indirectly reflects the nitrogen status in strawberry. However, the variation of cultivars leads to differences in the cell structure and light scattering and/or reflection effects of strawberry leaves during strawberry N status assessment, which means, leaf SPAD threshold value and the specific N demand will vary with different cultivars. As a result, accurate measurement of SPAD values and N status in strawberries with multiple cultivars is still challenging. In this study, the individual-cultivar, hybrid-cultivar, and multi-cultivar models were developed for the determination of SPAD values, and the performance of the models in lessening the impact of cultivar variation was studied and compared. The individual-cultivar model was constructed on the basis of a single cultivar of strawberry leaf. The hybridcultivar model was developed by merging the spectrum reflectance data and SPAD values of all studied leaf samples, and a multi-cultivar model was built in combination with cultivar identification, individual-cultivar models, and model search strategy. The results indicated that the multi-cultivar model was superior to the other two models for SPAD value estimation of strawberry leaves from different cultivars, with the overall Rp and RMSEP values of 0.966 and 0.468, respectively. We demonstrate that the leaf N content of strawberry is profoundly affected by cultivar variation, and establishing a multi-cultivar model is useful in monitoring the nitrogen status and guiding N fertilization recommendations for different strawberry cultivars.

Compared with using a single characteristic parameter of electrochemical impedance spectroscopy (EIS) to classify the freshness of fish samples from different origins, more characteristic parameters could bring higher accuracy as well as complexity, subjectivity, and uncertainty. In order to eliminate the disadvantages of the multiparameter model, a data fusion method based on model similarity (DFMS) was proposed in this study. The similarity relation between the freshness models based on EIS characteristic parameters and physicochemical indicator was analyzed and quantified accordingly, and then, the weighting factors of the fusion model were determined. The classification accuracy rate of fish freshness based on DFMS was 9.2∼15% greater than that of a single EIS characteristic parameter. The novel dimensionless fusion parameter method proposed in this article might provide a simple yet effective indicator for EIS-based food quality evaluation.

Fluorescence analysis method has high sensitivity, but the difference between photoelectric converters will lead to inaccurate data. To solve the problem of the separation of effective and unwanted light in the process of fluorescence conversion and the noise of small-scale variable amplification. This paper designs a method of modulating the light source makes the fluorescent light be a kind of differential signal that is easily extracted by the photoelectric conversion circuit and amplified, and then uses digital devices FPGA and ADC to achieve signal acquisition and processing, and finally it is presented in the form of a dark room box. In this paper, a novel detection structure is used as a discussion point, and a low-power, high-precision, low-cost signal processing system has been introduced in detail, and mainly used in 500 The fluorescence in the wavelength range of -600 nm was tested. The mathematical relationship between different fluorescence intensities and the post-conversion variables was established. In the subsequent product development, the detection of different wavelengths of light can be achieved by replacing the excitation light source and the corresponding filter, providing a simple circuit processing method for designing photoelectric probes, biomedical detection, and chemical applications.

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