Lycopene detection in cherry tomatoes with feature enhancement and data fusion.

Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.

Re-understanding of SERS for General and Standardized Quantitative Analysis.

We proposed two physical concepts, i.e., an intramolecular relative cross section (RCS) and an intermolecular relative scattering ability (RSA), to re-understand and re-describe surface-enhanced Raman scattering (SERS) and established a general SERS quantification theory. Interestingly, RCS and RSA are intrinsic factors and are experimentally measurable to form datasheets of molecules, namely, SERS cards, with which a standard SERS quantification procedure was established. The validity of the theory and quantification procedure was confirmed by experiments. Surprisingly, RCS and RSA are also valid for complex systems being considered as virtual molecules and are experimentally measurable. This simplifies complex systems into analyte-virtual molecule binary systems. With this consideration, trace-level mitoxantrone (a typical cancer drug metabolite) in artificial urine was accurately predicted. The theory, the SERS cards, the standard quantification procedure, and the virtual molecule concept pave a way toward quantitative and standardized SERS spectroscopy in dealing with real-world problems and complex samples.

A Neural Network Computational Spectrometer Trained by a Small Dataset with High-Correlation Optical Filters.

A computational spectrometer is a novel form of spectrometer powerful for portable in situ applications. In the encoding part of the computational spectrometer, filters with highly non-correlated properties are requisite for compressed sensing, which poses severe challenges for optical design and fabrication. In the reconstruction part of the computational spectrometer, conventional iterative reconstruction algorithms are featured with limited efficiency and accuracy, which hinders their application for real-time in situ measurements. This study proposes a neural network computational spectrometer trained by a small dataset with high-correlation optical filters. We aim to change the paradigm by which the accuracy of neural network computational spectrometers depends heavily on the amount of training data and the non-correlation property of optical filters. First, we propose a presumption about a distribution law for the common large training dataset, in which a unique widespread distribution law is shown when calculating the spectrum correlation. Based on that, we extract the original dataset according to the distribution probability and form a small training dataset. Then a fully connected neural network architecture is constructed to perform the reconstruction. After that, a group of thin film filters are introduced to work as the encoding layer. Then the neural network is trained by a small dataset under high-correlation filters and applied in simulation. Finally, the experiment is carried out and the result indicates that the neural network enabled by a small training dataset has performed very well with the thin film filters. This study may provide a reference for computational spectrometers based on high-correlation optical filters.

The alginate dialdehyde crosslinking on curcumin-loaded zein nanofibers for controllable release.

In this study, electrospun zein/alginate dialdehyde (AD) nanofibers were prepared by green crosslinking. The degree of crosslinking could reach 50.72 %, and the diameter of electrospun fibers ranged from 446.2 to 541.8 nm. The generation of AD and the bonding of crosslinking were further confirmed by the changes on characteristic peaks and conformational ratios in the infrared spectroscopy and secondary structure analysis. High concentrations of AD led to improved thermal stabilities, mechanical properties, and hydrophobicity. And the highly crosslinked nanofibers (Z-8) owned the highest elastic modulus (24.92 MPa), tensile strength (0.28 MPa), and elongation at break (8.14 %) among five samples. Moreover, Z-8 possessed a high swelling ratio of 5.45 g/g, and a low weight loss of 6.09 %. The samples could encapsulate curcumin efficiently and show controllable release behaviors based on different AD addition. And the oxidation resistance of nanofibers gradually improved, consistent with the release performances. This study indicated AD crosslinking favored the preparation and application of zein nanofibers, and the oxidized polysaccharide acted as the green crosslinking agent, which provided reference value for the application of polysaccharides in food-related electrospun materials.

Research on the Modulation Transfer Function Detection Method of a Bayer Filter Color Camera.

Bayer filter color cameras are more and more widely used in the field of aerospace remote sensing, but the Bayer filter causes great degradation in image quality; therefore, obtaining a means of achieving the high-precision measurement of the modulation transfer function (MTF) of Bayer filter color cameras is an urgent problem. In order to solve this problem, this paper develops a slanted-edge method via three steps: the detection of the slanted edge, the acquisition and processing of the edge spread function (ESF), and the acquisition and processing of the line spread function (LSF). A combination of the Canny operator and Hough transform is proposed for the detection of the slanted edge, which improves the fitting accuracy and anti-interference ability of the algorithm. Further, the Canny operator is improved by constructing an adaptive filter function and introducing the Otsu method, which can more effectively smooth the image and remove its false edges. A method of processing ESF data by combining cubic spline interpolation and Savitzky-Golay (SG) filtering is proposed, which reduces the effects of noise and the non-uniform sampling of ESF on MTF. A method of LSF processing using Gaussian function fitting is proposed to further reduce the effect of noise on MTF. The improved algorithm is verified by the MTF measurement test applied to a specific type of Bayer filter color space camera. The simulation and test results show that the improved slanted-edge method discussed in this paper has greater precision and a better anti-interference ability, and it can effectively solve the difficult problem associated with MTF detection in Bayer filter color space cameras.

Association of HTRA1 and CFH gene polymorphisms with age-related macular degeneration in Ningbo, China.

BACKGROUND: Age-related macular degeneration (AMD) is one of the major causes of blindness, and the incidence of this disease has been increasing in recent years. OBJECTIVE: To investigate the association between the single nucleotide polymorphisms (SNPs) of the high temperature requirement factor A-1 (HTRA1) and complement factor H (CFH) genes and susceptibility to AMD in Ningbo, China. METHODS: Ninety-eight patients with AMD and seventy-three controls were recruited at the Sixth Hospital of Ningbo from August 2017 to April 2019 in China. Genomic DNA was extracted from the venous blood provided by the hospital, and the genotypes of the AMD susceptibility genes CFH and HTAR1 were detected by polymerase chain reaction and sequenced directly. The SNPs rs11200638 on the HTRA1 gene and rs3753394 on the CFH gene were selected for genotype and association analysis. The correlations between the different genotypes of HTRA1 and CFH and AMD were analysed by the Chi-squared test. RESULTS: All the genotypes adhered to the Hardy-Weinberg equilibrium. There were three genotypes (AA, AG and GG) in HTRA1 (rs11200638). The differences in genotypes and allele frequency between the AMD group and the control group were statistically significant (P < 0.05). The A allele was a risk allele (OR: 4.19, 95% Cl: 2.28 ~ 7.70, P < 0.05), with a frequency of 61.7% in patients versus 43.8% in controls. However, the rs3753394 SNP in CFH was not associated with AMD in our study (P > 0.05). CONCLUSIONS: The rs11200638 SNP of the HTRA1 gene is associated with AMD, and the AA genotype is a risk factor for AMD in the Ningbo population. There is no significant correlation between the rs3753394 SNP of the CFH gene and AMD.

Polyoxometalate-like sub-nanometer molybdenum(vi)-oxo clusters for sensitive, selective and stable H2O2 sensing.

Polyoxometalate-like molybdenum(vi)-oxo clusters ([Mo-oxo]n, n = 1-20) deposited on high surface-area carbon are developed as a biosensor for non-enzymatic electrochemical H2O2 detection. The sensor exhibits excellent electrocatalytic performance with a low detection limit, wide linear range, excellent sensitivity and stability. The composite can be stably deposited on screen-printed electrodes which combine microlitre analyses, long shelf-life and re-usability.

Facet Annotation by Extending CNN with a Matching Strategy.

Most community question answering (CQA) websites manage plenty of question-answer pairs (QAPs) through topic-based organizations, which may not satisfy users' fine-grained search demands. Facets of topics serve as a powerful tool to navigate, refine, and group the QAPs. In this work, we propose FACM, a model to annotate QAPs with facets by extending convolution neural networks (CNNs) with a matching strategy. First, phrase information is incorporated into text representation by CNNs with different kernel sizes. Then, through a matching strategy among QAPs and facet label texts (FaLTs) acquired from Wikipedia, we generate similarity matrices to deal with the facet heterogeneity. Finally, a three-channel CNN is trained for facet label assignment of QAPs. Experiments on three real-world data sets show that FACM outperforms the state-of-the-art methods.