水果糖度近红外光谱在线检测装置

采用短波近红外光谱仪,进行机械传送、光谱采集处理、自动控制等系统的设计和集成,研制水果糖度近红外光谱在线检测装置。在550~850nm范围,采用偏最小二乘法,建立了苹果糖度近红外光谱在线检测数学模型。经比较,标准正交校正和一阶导数处理后的光谱建立的数学模型预测效果最优,模型的相关系数为0.78,模型预测均方根误差为0.67°Brix。实验表明:水果糖度近红外光谱在线检测装置可准确地检测苹果糖度含量。     

Comparison of functional and structural properties of native and industrial process-modified proteins from long-grain indica rice

Rice proteins, as a cheap plant protein source from the by-products of rice dreg processing, could potentially replace commonly used proteins such as soy and whey proteins in selected food products. In this study, the functional properties, surface hydrophobicity (H₀), sulfhydryl and disulfide bond contents, thermal properties, as well as secondary structures of native rice endosperm protein (REP) and processed rice dreg protein (RDP) extracted from long-grain indica rice, were compared. RDP was found to have a higher solubility associated with its relatively higher emulsifying and foaming properties than REP, as well as its water/oil holding capacity, although it was a denatured protein. The emulsifying properties were dependent on the solubility and H₀, while solubility was also related to the disulfide bond contents. Distinct differences in H₀, thermal properties, and disulfide bond contents between REP and RDP could be due to the conformational changes, as the industrial processing steps in the production of rice syrups caused an increase in β-turns at the expense of β-sheets and random coils of REP, leading to the unfolding of β-sheets into higher-ordered supramolecular structures for RDP, which could be responsible for its better functional properties.     

红外线体温仪在生猪屠宰场体温筛检中的试用效果

为寻找一种简单、快捷的宰前体温检测方法,设置警示温度,随机抽取宰前猪1000头,分别用水银温度计测量肛温,用红外线体温仪和电子体温计测量耳根表面温度。结果显示,红外线体温仪的检测结果与水银温度计检测结果符合率为93.11%。因此,红外线体温仪比较适合用于较大规模屠宰场的宰前检疫体温初筛。     

近红外光谱法预测粗皮桉木材的化学成分质量分数

采用近红外光谱法对粗皮桉木材中化学成分质量分数进行快速预测。用常规湿化学方法测定了粗皮桉木材样品的化学成分质量分数,结合近红外光谱仪采集相应的光谱,对原始光谱进行二阶导数预处理后,用偏最小二乘法建立相应的模型并对其进行外部验证。结果表明:粗皮桉木材综纤维素校正模型的相关系数为0.96,预测模型的相关系数为0.92,RPD为2.30。木质素校正模型的相关系数为0.91,预测模型相关系数为0.88,RPD为2.11。利用近红外光谱分析方法可以快速预测粗皮桉木材中综纤维素和木质素质量分数。     

基于近红外透射光谱分析技术的小麦蛋白质含量测定

近红外分析技术具有快速、简便、准确、非破坏性的优点,为小麦品质检测提供了一个新的技术手段。笔者首先对光谱数据进行移动窗口平均平滑和马氏距离筛选,在以上光谱预处理基础上,建立了小麦籽粒蛋白的偏最小二乘校正模型。仿真试验结果表明：该模型能够较准确地预测小麦蛋白质的含量,预测相关系数、预测均方误差和平均相对误差分别为0.9809、0.1130、1.973%。与用原始数据所建校正模型相比,预测效果显著提高。     

Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques

Citrus greening is a serious disease affecting citrus production in Florida and different parts of the world. This disease is spread by an insect vector and the trees are killed several years after infection. There is no known treatment for the disease. Disease detection and removal of infected trees is a critical part of citrus greening disease management efforts. This paper reports the evaluation of spectral features extracted from visible-near infrared spectroradiometer spectra for their potential to detect citrus greening disease. The extraction of spectral features is an effort to lower the cost of the optical sensor while maintaining their performance. Spectral features: (i) spectral reflectance bands and (ii) vegetation indices (VIs) were derived from 350-2,500 nm spectral reflectance data using two feature extraction methods: stepwise discriminant analysis and stepwise regression analysis. Following the selection of spectral features, the features were assessed using two classifiers, quadratic discriminant analysis (QDA) and soft independent modeling of classification analogies (SIMCA) to determine the overall and individual class classification accuracies. The classification results indicated that both the spectral features (spectral bands and VIs) yielded good overall (higher than 80%) and healthy class (higher than 85%) classification accuracies using the QDA-based algorithm. The SIMCA-based algorithm yielded good average citrus greening class classification accuracy (higher than 83%) using selected spectral features. Thus, the present study demonstrates the applicability of utilizing spectral features for detection of greening in citrus.     

基于连续投影算法的小麦湿面筋近红外校正模型优化

为减少建模过程中的计算量、提高模型的稳健性及预测精度,将连续投影算法用于小麦湿面筋近红外校正模型的建立。首先采用SPXY算法选择具有代表性的校正集样本,然后对光谱数据作不同预处理,增强光谱特征;运用连续投影算法对原始光谱和预处理后的光谱进行敏感波点提取,进而分别建立多元线性回归校正模型。测试结果表明,对光谱标准正态变量变换后利用连续投影算法提取敏感波点所建多元线性回归模型预测效果最好,预测均方根误差和预测相关系数分别为1.3332和0.94319,优于同等条件下建立的偏最小二乘回归模型。     

脲醛树脂结构研究进展

在过去的２０余年里,为获得结构更合理、性能更优异的脲醛树脂,人们使用近代分析手段对脲醛树脂结构进行了广泛的研究,付立叶变换红外光谱（ＦＴＩＲ）和１３Ｃ核磁共振（１３ＣＮＭＲ）是目前脲醛树脂结构研究的最主要方法,其中１３ＣＮＭＲ是在分子水平描述脲醛树脂结构的最完善、最有效的工具。本文综述近２０年脲醛树脂结构研究进展,内容包括结构研究方法、内容和结果。     

基于小规模数据集的柑橘树冠层施药情况的分类模型

目的 提升柑橘果园的智能化管理水平,快速无损获取柑橘树冠层的施药情况,改善小规模数据集导致施药情况分类模型易发生过拟合的问题。方法 提出一种基于卷积神经网络的柑橘树冠层施药情况分类模型——VGG_C模型。模型以VGG模型核心思想为基础进行构建,通过交叉熵损失函数优化,加速概率分布与真实分布的迭代过程,并在输出端引入不确定性度量计算以及在下采样模块中插入Droupout方法,降低由于数据较少而发生过拟合的概率。结果 VGG_C模型针对训练集的分类损失值为0.44%,比ResNet和VGG模型分别降低了87%和91%;准确率为95.3%,比ResNet和VGG模型分别提高了5%和10%;验证集的预测平均准确率为96.4%。结论 VGG_C模型通过多层卷积模型协同实现柑橘冠层热红外图像特征的高效提取,通过优化输出端结构提高了柑橘冠层施药情况分类模型在小数据集规模上的训练测试优度,可为柑橘树施药情况的智能化判断提供有效参考。     

表儿茶素类单质红外光谱特性研究

儿茶素类物质的测定对茶叶品质检测有重要作用,本工作利用傅立叶红外光谱技术对表儿茶索、表没食子儿茶素、表儿茶素没食子酸酯和表没食子儿茶素没食子酸酯4种儿茶素类单质进行了中红外光谱实验研究,得到了对应单质的红外吸收光谱。结果表明,3147~3498cm^-1范围内的宽带吸收来自于苯环上羟基的伸缩振动吸收,而400～1750cm^-1范围内多峰结构吸收,对应于骨架振动和取代基的变形振动吸收;对应中红外吸收光谱的多峰结构谱段高频端表儿茶素没食子酸酯和表没食子儿茶素没食子酸酯与对应儿茶素相比都多了一强吸收峰,对应波数分别为1681cm^-1和1692cm^-1,这是由于前两者都相应多出了-C=O结构。这些结果对于茶叶品质的光谱检测有重要的参考价值。