山药片红外联合热风干燥热质传递收缩模拟与品质

为了准确揭示山药片红外联合热风干燥传热传质机理,在考虑山药片收缩变形特性的基础上,通过有限元软件COMSOL6.1建立了“温度场-湿度场”多场耦合的山药片红外联合热风干燥传热传质模型。模拟研究基于山药片在不同温度（50、60、70 ℃）下收缩变形的传热传质,并通过试验进行验证。分析不同温度对山药片品质（色差、复水比、多糖和尿囊素含量）的影响。结果表明：1）山药片体积比随干燥温度的升高而增加,在干燥温度分别为50、60、70 ℃时,其值分别为34.55%、37.23%、39.04%。2）在干燥温度为50、60、70 ℃时,红外联合热风干燥收缩模型可准确预测山药片干燥过程中干燥温度和含水率,其决定系数R²分别为0.973、0.976、0.981和0.983、0.984、0.974。3）山药片外部温度升高,表面水分开始蒸发,形成水分梯度。随着干燥的继续,红外热量在山药片内部不断积累,导致内部温度升高,水分向外扩散,进而减小了内外水分梯度。随着干燥温度的升高,增加了山药片温度和湿度梯度,促进了热量和质量的传递,提高了水分迁移的速率。4）在60 ℃时,干燥品质最优,其色差为7.49、复水比为2.65 kg/kg、多糖含量为24.17 mg/g、尿囊素含量为2.66 μg/g。该模型为其他物料在红外联合热风干燥技术的模拟研究提供有益借鉴。     

基于可见/近红外光谱技术的毛脚茧的光谱快速鉴别方法研究

基于可见/近红外光谱技术,研究了鲜茧毛脚茧快速鉴别的方法。采用主成分分析法对毛脚茧和成熟茧进行定性分析,再对光谱数据采用最小二乘支持向量机方法建模鉴别,通过连续投影算法从400-1000 nm波段的鲜茧光谱数据中选取了12个特征波长。采用最小二乘支持向量机法基于400 nm,430 nm,487 nm,512 nm,604 nm,616 nm,732 nm,759 nm,784 nm,852 nm,985 nm,999 nm这12个特征波长建模,结果对毛脚茧的判别准确率达到100%。表明可见/近红外光谱可以用于毛脚茧的鉴别检测。     

复合蛋白酶在即食海参加工中的应用

用复合蛋白酶处理盐渍海参,作为加工冻干即食海参的预处理样品,研究其酶处理最适工艺。试验结果表明,酶处理温度40℃,处理50 m in,水浸泡13 h,酶用量为肉重0.075%时,处理后的海参胶原蛋白溶解度(DD%)为(38.14±0.52)%,与水发海参接近[(39.34±0.47)%],口感达到最佳。从酶处理海参和盐渍海参的红外光谱分析可知,二者的营养成分无明显差异,达到生产冻干即食海参的要求。     

基于硅光电池S1087的温室大棚光强控制系统研究

研究了一套用于调节温室大棚光照强度的自动控制系统。以MSP430作为主要控制器件,设计的I/V转化电路可将硅光电池输出的微弱电流信号转化为电压信号;采用两个S1087组成差分放大电路,以有效抑制温度漂移;通过NRF905SE红外无线传输模块,可以准确地将现场测量数据发送给监测中心,并接收来自监测中心的控制命令。试验结果表明,项目设计的控制系统运行稳定,抑制温度漂移能力好,光照强度测量精度高、量程大,能够准确地远程传送数据,从而实现远程在线控制,具有较高的应用价值。     

基于LBV变换与小波变换的OLI图像融合方法

以陕西省榆阳区2013年6月9日的Landsat 8 OLI图像为基础数据源,对比分析LBV-Wavelet RF等5种图像融合算法的使用效果。对图像预处理后,分别采用HIS变换、Brovey变换、HPF变换、PCA变换和LBV-Wavelet RF方法进行融合和SVM监督分类,然后从目视评价和定量评价两方面对比分析各种融合算法的使用效果。在目视评价方面,判读融合前、后9种地类光谱特征的一致性;融合后图像是否具有全色波段图像的空间结构特征,是否存在细节模糊。在定量评价方面,采用灰度均值差、灰度均方根差评价融合后图像对多光谱信息的保持性能;采用相关系数均值、相关系数均方根差评价融合后图像对高空间分辨率信息的融入度;采用总体分类精度、Kappa系数评价融合前、后SVM监督分类精度差异。结果表明LBV-Wavelet RF方法能够使融合后图像在保持原多光谱图像光谱信息的同时,增强纹理结构特征,提高对细小地物的辨识能力;融合后图像SVM监督分类的总体分类精度和Kappa系数分别为84.01%和0.787,较原多光谱图像分别提高13.45%和15.91%。     

Prediction of total phenols,condensed tannins,and 3-deoxyanthocyanidins in sorghum grain using near-infrared (NIR) spectroscopy

The reported high phenolic levels in sorghum have led an interest from sorghum breeding programs in developing and identifying germplasms with high phenolic levels, which require screening a large number of samples to find those with the highest levels. Since wet chemistry screening methods are slow, expensive, and destructive, the use of NIR calibration curves could be an alternative. The objectives of this project were to determine the variation range in total phenols, condensed tannins, and 3-deoxyanthocyanidin levels in a diverse set of sorghum genotypes and to assess the predictive value of NIR curves to estimate these compounds in sorghum. A calibration curve to estimate each compound was developed and validated with an independent validation set. Calibration curve correlations for total phenols, condensed tannins, and 3-deoxyanthocyanidins were r = 0.98, 0.97, and 0.99, respectively. Correlations between NIR-predicted values and reference values in the validation set were significant for total phenols (r = 0.93), condensed tannins (r = 0.81) and 3-deoxyanthocyanidin (r = 0.82). These indicated that sufficient variation for these compounds existed within sorghum and that NIR calibration curves could be used to rapidly and non-destructively predict total phenols, condensed tannins, and 3-deoxyanthocyanidins concentrations in whole grain sorghum.     

Integration of independent component analysis with near infrared spectroscopy for evaluation of rice freshness

The storage time and conditions of rice has an enormous effect on its appearance, flavor, and quality of the nutrients; and the acidity of rice usually increases with prolonged storage. Therefore, evaluation of freshness is an important issue for rice quality. In this study, the NIR (near infrared) spectra combined with independent component analysis (ICA) technique was used to evaluate the rice freshness. A total of 180 white rice samples were collected from 6 crop seasons for the purpose of developing an ICA-NIR based procedure for rice freshness as quantified by pH values. Values of pH were determined by a BTB-MR (bromothymol blue – methyl red) method. The best calibration model of white rice was developed using the smoothed first derivative spectra, five ICs and cross-validation; the results indicated that r² (coefficient of determination) = 0.924, and in units of pH, SEC (standard error of calibration) = 0.145, SEP (standard error of prediction) = 0.146, bias = 0.001, and RPD (residual predictive deviation) = 3.65. Freshness of white rice could be distinguished either visually by a 3-dimensional diagram composed from ICs 2, 3 and 4, or statistically by a calibration model. The results show that ICA with NIR has the potential to be adopted as an effective method for evaluating rice freshness.     

Amino acid profile of the quinoa (Chenopodium quinoa Willd.) using near infrared spectroscopy and chemometric techniques

The high content of amino acids of the quinoa, especially essential amino acids (higher than other cereals) makes a food increasingly demanded by consumers. A total of twelve amino acids (arginine, cystine, isoleucine, leucine, lysine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine) were analyzed in quinoa samples from Chile by near infrared spectroscopy (NIR) with direct application to the samples of a remote fiber-optic reflectance probe. The calibration results using modified partial least squares (MPLS) regression satisfactorily allowed the determination of the concentrations of this amino acid group with high multiple correlation coefficients (RSQ = 0.97–0.71) and low standard prediction errors (SEPC = 0.07–0.20). The prediction capacity (RPD) for the arginine, the cystine, the isoleucine, the lysine, the serine, the threonine, the tryptophan, the tyrosine and the valine ranged between 2.6 and 5.2, for the rest of amino acids were higher to 1.8, indicating that the NIRS equations obtained were applicable to unknown samples. It has confirmed that NIRS technology is a method that may be useful to replace the traditional methods for routine analysis of some amino acids.     

激光诱导击穿原子光谱在土壤分析中的应用

土壤分析是土壤学研究及应用的前提和基础,传统化学土壤分析方法逐渐不能适应现代土壤学海量信息数据快速获取的需求。激光诱导击穿光谱作为一种全新的、反映土壤组成元素原子信息的光谱技术,其无需对样品进行复杂前处理,可实现原位、快速、多元素连续在线检测,每条光谱记录土壤样本独一无二的特征,可以视为土壤“指纹”,成为现代土壤分析的有效技术之一。首先介绍和分析了激光诱导击穿原子光谱的原理、光谱获取的主要影响因素、光谱数据处理的化学计量学方法等;然后阐述和梳理了基于激光诱导击穿光谱技术在土壤分析方面的应用成果和进展,包括土壤鉴定、土壤养分评估、土壤重金属测定、微观/介观尺度土壤原位表征等;最后讨论和总结了激光诱导击穿光谱技术在土壤分析中所面临的挑战及其应用展望。     

非接触式测温鉴定母猪典型生理状态

体温是衡量母猪发情与否的关键生理指标。母猪发情期与间情期体温的明显不同,是运用红外热成像监测母猪体温变化以鉴定母猪发情的依据。该研究基于Y3TB01体温筛查智能摄像机和红外热像仪C3实时非接触拍摄母猪红外热图像,并运用图像分析技术获取体温,为非接触式测温用于鉴定母猪典型生理状态提供支持。试验以空怀期、发情期、妊娠1～8 d和妊娠9～16 d大白初产和经产母猪共720头为研究对象,利用红外设备和电子体温计分别测量母猪的眼睛、耳、耳蜗、乳房、外阴、臀部和直肠温度,筛选不同胎次发情母猪和返情母猪的体表关键部位温度,进行差异分析。结果表明：运用Y3TB01体温筛查智能摄像机可以代替直肠温度测定,准确监测母猪体表温度（相关系数为0.973）;红外热像仪C3检测体温异常母猪的体表温度,臀部温度可以作为筛选大白初产发情母猪和返情母猪的测定部位（准确率分别为77%和72%）,外阴温度可以作为筛选大白经产发情母猪和返情母猪的测定部位（准确率分别为88%和81%）。因此,Y3TB01体温筛查智能摄像机和红外热像仪C3配套技术能准确监测母猪体温和异常体温报警,明确不同胎次不同生理时期大白母猪体表部位温度和体温分布,准确鉴定出发情母猪和返情母猪。研究为非接触式测温鉴定母猪发情技术提供了科学依据,对规模化猪场母猪的饲养管理与疫情防控具有重要意义。