A combination of near-infrared hyperspectral imaging with two-dimensional correlation analysis for monitoring the content of biogenic amines in mutton

The study investigated the feasibility of using a combination of near-infrared hyperspectral imaging (NIR-HSI) with two-dimensional correlation (2DCOS) analysis for rapid and non-destructive determination of the content of biogenic amines in mutton during refrigerated storage. Total contents of biogenic amines (TBA) were used as the perturbation. By analysing the synchronous and asynchronous two-dimensional correlation spectra, sensitive variables that were closely related to TBA contents were obtained. The results showed that the wavelengths in the spectra range of 1002–1335 nm were the research area for the detection of TBA contents in mutton. The least-squares support vector machines (LSSVM) model based on effective wavelengths selected by competitive adaptive reweighted sampling (CARS) from 2DCOS analysis showed excellent results, with correlation coefficient in prediction (Rp) of 0.91, root mean square error in prediction (RMSEP) of 1.67 mg kg⁻¹ and the ratio of performance deviation (RPD) of 2.76. The research demonstrated that the combination of NIR-HSI and 2DCOS could be used as an effective method for monitoring the content of biogenic amines in mutton.     

基于深度学习的高光谱影像分类方法研究

针对高光谱影像分类方法精度不足的问题,提出一种基于空间-频谱变换（Spectral-Spatial Transformer,SST）网络的高光谱影像分类方法。首先,将高光谱影像预处理为一维特征向量。然后,设计了具有光谱-空间注意力模块和池化残差模块的SST高光谱影像分类网络。本文所提出的分类方法在Indian Pines数据集和Pavia University数据集上的总体分类精度分别为98.67%和99.87%,表明此方法具有较高的分类精度,为高光谱影像分类及应用提供了一种新方案。     

基于改进WOA-Elman神经网络的高光谱牛奶蛋白质快速无损检测

目的：解决现有牛奶蛋白质检测方法存在的精度低、效率低和人工依赖性强等问题。方法：基于高光谱成像系统,提出一种将改进的鲸鱼算法与Elman神经网络相结合用于牛奶蛋白质含量快速无损检测。通过混沌映射、自适应收敛因子、自适应权重优化鲸鱼算法,提高搜索精度,优化后对Elman神经网络参数（权重和阈值）进行寻优。通过试验分析所提无损检测方法的性能。结果：与常规检测方法相比,试验方法在牛奶蛋白质无损检测的多个性能指标方面均为最优,决定系数为0.997 3,均方根误差为0.000 3,检测时间为1.56 s。结论：试验方法具有较高的检测精度和检测效率。     

Analytical‐Based Methodologies for Examining the In Vitro Absorption,Distribution, Metabolism,and Elimination (ADME) of Silver Nanoparticles

The clinical applications of silver nanoparticles (AgNPs) remain limited due to the lack of well‐established methodologies for studying their nanokinetics. Hereby, the primary goal is to adapt a suite of analytical‐based methodologies for examining the in vitro absorption, distribution, metabolism, and elimination of AgNPs. Vero 76 and HEK 293 cells are exposed to ≈10‐nm spherical AgNPs⁺ and AgNPs^? at relevant concentrations (0–300 µg mL^?1) and times (4–48 h). Absorption: Inductively coupled plasma optical emission spectroscopy (ICP‐OES) demonstrates that the two AgNP formulations are not bioequivalent. For example, different bioavailabilities (C _maximum < 20.7 ± 4% and 6.82 ± 0.4%), absorption times (T _maximum > 48 and ≈24 h), and absorption rate laws (first‐ and zeroth‐order at 300 µg mL^?1) are determined in Vero 76 for AgNPs⁺ and AgNPs^?, respectively. Distribution: Raman and CytoViva hyperspectral imaging show different cellular localizations for AgNPs⁺ and AgNPs^?. Metabolism: Cloud point extraction (CPE)‐tangential flow filtration (TFF) reveal that ≤ 11% ± 4% of the administered, sublethal AgNPs release Ag⁺ and contribute to the observed cytotoxicity. Elimination: ICP‐OES‐CPE suggests that AgNPs are cleared via exocytosis.     

基于小波变换和形状-增益矢量量化的3维图像压缩

数据压缩是高光谱图像处理应用中的一个关键问题。为了对高光谱图像进行有效压缩，在2维小波变换的基础上，提出了一种分组矢量量化的高光谱图像有损压缩方案。该方案首先按照谱段类型对高光谱图像进行分组，然后对每个谱段分别进行2维小波变换，最后变换系数再使用一种Kronecker-Product形状-增益矢量量化方法来进行量化编码。计算机仿真结果证明．该算法在取得高压缩率的同时，不仅能很好地保持数据的谱特征，并能降低运算量。     

Recent Developments and Applications of Hyperspectral Imaging for Quality Evaluation of Agricultural Products: A Review

Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious, and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. HSI allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of HSI are also reported.     

重庆万盛采矿区内主要农作物的光谱测量和分析

矿山开采中产生的固体废弃物和废液会使矿区土壤中富集重金属元素,进而影响矿区内的农作物生长。选择重庆市万盛区矿区作为研究区,采集红薯和南瓜的实测高光谱数据和土壤样本的重金属含量数据。通过对土壤重金属含量和农作物的红边位置偏移进行相关分析,发现不同土壤重金属含量对研究区内主要农作物的影响是不同的。可以看出,在研究区内的两种主要农作物中,红薯对矿区土壤中的重金属Cr、Mn和Cd比较敏感,而南瓜则对以上三种土壤重金属具有一定的吸收和抵抗作用。该结果可以为万盛区矿区内农作物种植的选择提供理论依据。     

SSCDenseNet:一种空-谱卷积稠密网络的高光谱图像分类算法

基于深度学习的高光谱遥感图像地物分类是目前研究的热点.但由于其参数规模大以及结构复杂,深度网络通常需要大量训练样本和较长训练时间,如何在小规模样本下建立深度学习监督分类模型是需要解决的关键问题.本文提出了一种小规模样本下高光谱图像分类的空-谱卷积稠密网络算法,称为SSCDenseNet,其包含三种新颖的架构策略：（1）空-谱分离卷积,即采取光谱维一维卷积和空间维二维卷积的分离卷积结构构成隐层单元,并通过多个隐层单元堆叠构造深度网络;（2）隐层单元中使用批归一化,减少数据协方差漂移及加速网络训练;（3）隐层单元间构建稠密连接,缓解梯度消失问题并实现特征复用.通过Indian Pines、Pavia University与Salinas数据集进行综合测评,表明该方法优于若干最新深度学习方法,特别在小规模样本下具有优异的分类性能.     

CMOS成像器件性能测试方法的研究

针对CMOS成像器件结构的特殊性,发展并提出了像元光子转移技术法测量增益和读出噪声。同时对CMOS器件的线性度、满阱电荷、暗流、不均匀性和量子效率等性能的测试方法进行了研究。最后基于2k×2kCMOS芯片进行了性能测试实验,实验结果也验证了该测试方法的可行性和可靠性。     

一种新的基于目标检测的波段选择方法

针对现有的高光谱遥感波段选择方法应用于目标检测时效果不高的问题,提出了基于目标检测的波段选择方法两步波段选择（Two Step Band Selection,TSBS）方法。该方法首先应用典型波段选择方法对高光谱波段进行初选;然后根据波段逐一累积后不同波段组合的目标检测效果,对初选波段进行再次选择,最终得到目标检测效果更好的波段组合。该方法不仅继承了典型波段选择方法的优点,而且直接基于目标检测的效果选择波段,应用针对性强,方法简单易行。实验结果表明:TSBS方法在高光谱数据波段选择方面具有较好的普适性,能够在实现数据降维的同时,有效改善目标检测的效果。