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We present the first report on reflectometric optical fiber sensing based on mechanically induced cascaded long-period grating (LPG) structure. This method utilizes in-house designed mechanical casings and a bare fiber. When a fiber is sandwiched between the casings and pressed by tightening screws, an LPG is induced due to a photo-elastic effect. By exploiting Fresnel refection at a fiber end, a cascaded LPG structure can be implemented using a single LPG, enabling reflectometric configuration. When this sensor is subjected to external physical processes, the attenuation bands change clearly, and their shift can be used for non-destructive evaluations. We show experimentally that this sensor can measure loads of up to 10 N without producing any permanent change in the fiber properties, indicating its pressure sensing capability. We also show that this sensor can potentially measure strain and temperature. In our experiments, the strain and temperature sensitivities are 9.4 nm/% and 0.045 nm/°C, respectively.

A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were built by using feed-forward neural networks (FNN) and partial least squares regression (PLSR). In addition, it was established that using a regression coefficient on the data can be further compressed by selecting optimal wavelengths (35 for TVB-N and 18 for TBA). The results validated that FNN has higher prediction accuracies than PLSR for both cases using full and selected reflectance spectra. Moreover, our FNN based model has showcased excellent performance even with selected reflectance spectra with rp = 0.978, R2p = 0.981, and RMSEP = 2.292 for TVB-N, and rp = 0.957, R2p = 0.916, and RMSEP = 0.341 for TBA, respectively. This optimal FNN model was then utilized for pixel-wise visualization maps of TVB-N and TBA contents in fillets.