Search Results (5)

The short-wavelength mid-infrared spectral range of the 2 μm waveband has the advantages of low transmission loss and broad gain bandwidth, making it a promising candidate for the next optical fiber communication window. It is thus highly desired to develop high-performance silicon photonic components in this waveband. Here, an efficient dual-layer grating coupler was designed on a 220 nm thick silicon-on-insulator based on raised polysilicon to address the low directionality issue. For the fiber tilted at an angle of 10°, the grating coupler’s simulated coupling efficiency reaches 80.3% (−0.95 dB) at a wavelength of 2002 nm. The 1 dB bandwidth is 66 nm. The structure is completely compatible with the standard silicon photonic fabrication process, making it suitable for large volume fabrication. Full article

The objective of this study was to evaluate the performance of near-infrared spectroscopy (NIRS) systems operated in dual band for the non-destructive measurement of the fat, protein, collagen, ash, and Na contents of soy sauce stewed meat (SSSM). Spectra in the waveband ranges of 650–950 nm and 960–1660 nm were acquired from vacuum-packed ready-to-eat samples that were purchased from 97 different brands. Partial least squares regression (PLSR) was employed to develop models predicting the five critical quality parameters. The results showed the best predictions were for the fat (R_p = 0.808; RMSEP = 2.013 g/kg; RPD = 1.666) and protein (R_p = 0.863; RMSEP = 3.372 g/kg; RPD = 1.863) contents, while barely sufficient performances were found for the collagen (R_p = 0.524; RMSEP = 1.970 g/kg; RPD = 0.936), ash (R_p = 0.384; RMSEP = 0.524 g/kg; RPD = 0.953), and Na (R_p = 0.242; RMSEP = 2.097 g/kg; RPD = 1.042) contents of the SSSM. The quality of the content predicted by the spectrum of 960–1660 nm was generally better than that for the 650–950 nm range, which was retained in the further prediction of fat and protein. To simplify the models and make them practical, regression models were established using a few wavelengths selected by the random frog (RF) or regression coefficients (RCs) method. Consequently, ten wavelengths (1048 nm, 1051 nm, 1184 nm, 1191 nm, 1222 nm, 1225 nm, 1228 nm, 1450 nm, 1456 nm, 1510 nm) selected by RF and eight wavelengths (1019 nm, 1097 nm, 1160 nm, 1194 nm, 1245 nm, 1413 nm, 1441 nm, 1489 nm) selected by RCs were individually chosen for the fat and protein contents to build multi-spectral PLSR models. New models led to the best predictive ability of R_p, RMSEP, and RPD of 0.812 and 0.855, 1.930 g/kg and 3.367 g/kg, and 1.737 and 1.866, respectively. These two simplified models both yielded comparable performances to their corresponding full-spectra models, demonstrating the effectiveness of these selected variables. The overall results indicate that NIRS, especially in the spectral range of 960–1660 nm, is a potential tool in the rapid estimation of the fat and protein contents of SSSM, while not providing particularly good prediction statistics for collagen, ash, and Na contents. Full article

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (R_C), root mean square error of calibration (RMSEC), correlation coefficient of prediction (R_P), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control. Full article

The special dispersion and temperature characteristics of diffractive optical element (DOE) make them widely used in optical systems that require both athermalization and achromatic aberrations designs. The multi-layer DOE (MLDOE) can improve the diffraction efficiency of the overall broad waveband, but its diffraction efficiency decreases with changes in ambient temperature. When the ambient temperature changes, the micro-structure heights of MLDOE and the refractive index of the substrate materials change, ultimately affecting its diffraction efficiency, and, further, the optical transform function (OTF). In this paper, the influence of ambient temperature on the diffraction efficiency of MLDOE in a dual-infrared waveband is proposed and discussed, the diffraction efficiency of MLDOE caused by ambient temperature is derived, and a computational imaging method that combines optical design and image restoration is proposed. Finally, a dual-infrared waveband infrared optical system with athermalization and achromatic aberrations corrected based on computational imaging method is designed. Results show that this method can effectively reduce the diffraction efficiency of MLDOE by ambient temperature and improve the imaging quality of hybrid optical systems. Full article

Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems. Full article