The adulteration of extra virgin olive oil (EVOO) is a big problem in food safety. The present paper uses Raman spectra to characterize different kinds of vegetable oils in the region 800–1800 cm⁻¹. Bayesian framework is applied to find the best parameters for the least squares support vector machines (LS-SVM), and an adulteration prediction model is established by using the optimal parameters and the Raman spectral data of EVOO for the training of LS-SVM without any classification process. The results show that the root mean square error of prediction (RMSEP) and the coefficient of determination (R²) of the algorithm based on Bayesian framework LS-SVM (Bay-LS-SVM) are 0.0509 and 0.9976, respectively. Compared with the commonly used chemometric tool, partial least squares regression (PLS), the proposed algorithm shows higher accuracy and computational efficiency. The method based on Bay-LS-SVM and Raman spectroscopy is also easy to operate, non-destructive and ‘lipid sensitive’, and it is considered to be suitable for online detection of adulterated olive oil.

We investigate a high-sensitivity surface plasmon resonance (SPR) biosensor consisting of a Au layer, four-layer MoS₂, and monolayer graphene. The numerical simulations, by the transfer matrix method (TMM), demonstrate the sensor has a maximum sensitivity of 282°/RIU, which is approximately 2 times greater than the conventional Au-based SPR sensor. The finite difference time domain (FDTD) indicates that the presence of MoS₂ film generates a strong surface electric field and enhances the sensitivity of the proposed SPR sensor. In addition, the influence of the number of MoS₂ layers on the sensitivity of the proposed sensor is investigated by simulations and experiments. In the experiment, MoS₂ and graphene films are transferred on the Au-based substrate by the PMMA-based wet transfer method, and the fabricated samples are characterized by Raman spectroscopy. Furthermore, the fabricated sensors with the Kretschmann configuration are used to detect okadaic acid (OA). The okadaic acid–bovine serum albumin bioconjugate (OA-BSA) is immobilized on the graphene layer of the sensors to develop a competitive inhibition immunoassay. The results show that the sensor has a very low limit of detection (LOD) of 1.18 ng/mL for OA, which is about 22.6 times lower than that of a conventional Au biosensor. We believe that such a high-sensitivity SPR biosensor has potential applications for clinical diagnosis and immunoassays.

Surface plasmon resonance biosensor technology has enjoyed rapid development in the past two decades and undergone a boom in recent years. The growing demand for analytical applications has brought a variety of these instruments on to the market, and these sensors are nearly essential in the biochemical analysis laboratory, with wide applications in the life sciences, environmental monitoring, food safety, drug screening, and clinical diagnosis. After examining the last three years of literature on surface plasmon resonance biosensor instruments, in this article excellent studies are reviewed that have practical significance for progress in the instrumentation. Among the studies on improving sensor performance, the focus has been on metal thin film modification and data processing; regarding innovative design, the combinations of biosensing techniques with microfluidic chips and electrochemical with surface-enhanced Raman scattering are discussed. Instrument miniaturization has also been considered, and some personal perspectives are presented. In addition, various applications of these sensors are in biochemistry, medical diagnostics, food safety, and environmental monitoring. Finally, conclusions on the developments to date are summarized and future development trends are proposed.

Surface plasmon resonance (SPR) biosensors consisting of alternate layers of silver (Ag) and TiO₂ thin film have been proposed as a high sensitivity biosensor. The structure not only prevents the Ag film from oxidation, but also enhances the field inside the structure, thereby improving the performance of the sensor. Genetic algorithm (GA) was used to optimize the proposed structure and its maximum angular sensitivity was 384°/RIU (refractive index unit) at the refractive index environment of 1.3425, which is about 3.12 times that of the conventional Ag-based biosensor. A detailed discussion, based on the finite difference time domain (FDTD) method, revealed that an enhanced evanescent field at the top layer–analyte region results in the ultra-sensitivity characteristic. We expect that the proposed structure can be a suitable biosensor for chemical detection, clinical diagnostics, and biological examination.

Digital holographic microscopy is becoming increasingly useful for the analysis of marine plankton. In this study, we investigate autofocusing and image fusion in digital holographic microscopy. We propose an area metric autofocusing method and an improved wavelet-based image fusion method. In the area metric autofocusing method, a hologram image is initially segmented into several plankton regions for focus plane detection, and an area metric is then applied to these regions. In the improved wavelet-based image fusion method, a marked map is introduced for labeling each plankton region with the order of refocus plane images that accounts for the most pixels. The results indicate that the area metric autofocusing method applied to each plankton region provides a higher depth resolution accuracy than a number of general autofocusing methods, and the mean accuracy increases by approximately 33%. The improved wavelet-based image fusion method can fuse more than nine reconstructed plane images at a time and effectively eliminate fringes and speckle noise, and the fused image is much clearer than that of a general wavelet-based method, a sparse decomposition method, and a pulse-coupled neural networks method. This work has practical value for plankton imaging using digital holographic microscopy.