蒸煮过程纸浆卡伯值近红外光谱法在线测定

利用近红外光谱法测量蒸煮过程黑液中木素含量的变化，从而在线测定出纸浆卡伯值。通过一元线性回归、多元线性回归和主成分回归分析显示：3种测量模型均具有较好的学习和预测精度，其中主成分回归模型具有较高的预测鲁棒性。     

计学计量学法处理可见光谱定量测定纸浆卡伯值

本文将一元线性回归、多元线性回归和主成分回归分析的化学计量学方法应用于纸浆卡伯值的在线测量中,通过测量蒸煮 液的吸光度来预测纸浆的卡伯值。     

光纤光谱技术在DDS蒸煮终点预测中的应用

针对置换蒸煮终点难以在线判断这一难题,提出了一种利用光纤光谱来在线测量蒸煮终点的有效方法。通过现代光纤光谱技术,定量分析循环加热阶段溶出到黑液中的木素含量,从而推断出纸浆中的残余木素含量;并采用多元二项式回归的方法来处理所选波段中多个波长处的黑液吸光度数据,建立线性回归模型。本方法能够避免传统卡伯值测量模型通用性差、应用范围小和局限条件多的缺陷,可实现对置换蒸煮终点的有效预测。     

基于PLSR的针叶材纸浆得率影响因素相关性分析

纸浆材的纸浆得率大小与其化学组成有较密切的相关性,通常可以用多元线性回归(MLR)分析方法研究这种相关性的密切程度。但当各性能指标间存在严重的多重共线性时,MLR方法难以克服这一缺陷。研究利用18种针叶材制浆性能典型案例数据及偏最小二乘回归(PLSR)方法探讨了化学指标和纸浆得率之间的关系,并和传统的MLR方法进行比较。研究结果表明:PLSR能较好地克服纸浆材性能指标之间的多重共线性影响,预测结果更为准确,模型解释也比MLR更为合理,为纸浆材制浆性能综合评价提供了一条有效途径。     

压力鞋垫的足-地面反力预测模型与分析

为方便医生对病人日常生活状态下足-地面反力的远程监诊,设计了鞋垫式足底压力测量系统。在足底压力的动态测量过程中,压力传感器的模拟信号经过放大滤波调理和数模转换后,通过Zigbee无线模块发送至SQL数据库,使用LabVIEW后处理程序实现足底压力的远程读取。基于足底压力数据,利用多元线性回归算法建立了足-地面反力的多元线性回归模型,发现FSR402传感器在测量10 N以下的压力时精度较高,当压力达到10 N及以上时精度显著下降。鞋垫式压力测量系统通过2次对比实验所测的测量值和预测值之间的误差为0.248 8%和0.731 8%,位于工程应用可接受的误差范围之内,而产生的误差以及预测力负值的原因是传感器精度和足部与鞋垫之间的黏弹性。     

基于双原子库稀疏分解的力学式纸浆浓度传感器测量值补偿研究

针对目前广泛使用的力学式纸浆浓度传感器测量精度较低的问题,通过研究纸浆纤维结构和力学式纸浆浓度传感器测量原理,发现了这种传感器测量精度较低的原因：第一,纸浆浓度测量信号中噪声信号难以滤除;第二,纸浆流速对纸浆浓度测量影响较大。为此,在研究纸浆浓度测量信号中噪声信号及其性质并建立纸浆浓度传感器测量模型的基础上,提出了利用稀疏分解消除纸浆浓度测量信号中各种噪声信号,同时利用纸浆浓度传感器测量模型对纸浆浓度测量值进行流速补偿。结果表明,该方法能够显著提高纸浆浓度传感器的测量精度。     

基于RBF神经网络的棉纤维成纱强力预测

由于多元线性回归模型对棉纤维的品质和成纱质量之间关系预测精度及适应性较差,提出采用径向基函数对BP神经网络进行改进,使用测得的棉纤维品质部分实验数据对网络进行训练,然后对成纱强力进行预测,得出采用改进后的RBF神经网络对纱线强力进行预测,克服了陷入局部极小和运算迭代量大的缺点,有效地提高学习速度,收敛速度加快,训练时间缩短,预测精度提高,能较好地预测纱线的强力。其成纱强力的预测结果比多元回归统计模型的结果更好。     

基于RBF神经网络的棉纤维成纱强力预测

由于多元线性回归模型对棉纤维的品质和成纱质量之间关系预测精度及适应性较差,提出采用径向基函数对BP神经网络进行改进,使用测得的棉纤维品质部分实验数据对网络进行训练,然后对成纱强力进行预测,得出采用改进后的RBF神经网络对纱线强力进行预测,克服了陷入局部极小和运算迭代量大的缺点,有效地提高学习速度,收敛速度加快,训练时间缩短,预测精度提高,能较好地预测纱线的强力.其成纱强力的预测结果比多元回归统计模型的结果更好.     

间歇蒸煮过程纸浆Kappa值分段模型研究

由于技术和工艺原因，间歇制浆蒸煮过程纸浆Kappa值难于在线测量。本文在Hatton模型的基础上，提出了分段机理回归模型预测方法，工厂数据的应用证实了该方法的可行性和有效性。     

基于高光谱图像技术的菜用大豆厚度检测

菜用大豆厚度是划分菜用大豆等级的重要衡量指标之一。采用高光谱图像技术对菜用大豆的厚度进行预测。实验中选取200个菜用大豆作为测试样本,获取其高光谱反射图像,同时用数字式游标卡尺测量厚度值。选取400～1 000 nm范围的光谱信息,采用多元散射校正、标准归一化和导数计算对光谱数据预处理,结合偏最小二乘和多元线性回归两种分析方法建立厚度校正模型和预测模型。研究发现基于多元散射校正的偏最小二乘方法的模型精度较优,校正模型和预测模型的相关系数分别为0.956和0.933,均方根误差分别为0.59 mm和0.70 mm。研究结果表明可以利用高光谱图像技术预测菜用大豆厚度。     

Paddy and maize moldy status characterization using electronic nose

Paddy and maize moldy status rapid detection using electronic nose (E-nose) was investigated in this paper. An E-nose system with eight metal oxide semiconductor gas sensors was used to measure moldy status of paddy and maize samples at room temperature. Principal component analysis (PCA) and stochastic resonance (SR) are used for data processing. PCA results showed that E-nose could distinguish paddy and maize samples in different moldy status. Signal-to-noise ratio (SNR) spectrum calculated by SR discriminated paddy and maize samples. Moldy status predicting models were developed based on SNR maximums linear fitting regression. Validating experiments demonstrated that the developed models presented high detecting accuracy. The proposed method is promising in grain quality rapid analysis applications.     

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83.4% in multiparous cows. These results suggest that predicting serum BHB from continuous test-day milk and performance variables could serve as a valuable diagnostic tool for monitoring HYK in Holstein and Jersey herds.     

Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows

Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.     

BP 神经网络在大豆油酸价近红外光谱检测中的应用

对于大豆四级油,采用BP 神经网络对其近红外光谱数据建模,对系统的结构及参数选取进行了分析,对样本训练集的设计和网络输入端的主因子方面进行了处理。对于其他的多变量建模方法,分析了其对近红外光谱有用信息的提取作用。结果显示：多元线性回归、主成分回归和偏最小二乘法对大豆四级油酸价预测的标准偏差分别为0.1472%、0.1801% 和0.1576%,BP 神经网络的预测标准偏差为0.1387%。     

电子鼻结合化学计量法对羊奶中蛋白质掺假的识别

利用电子鼻结合化学计量法对羊奶中的蛋白质掺假进行定性和定量的研究。用电子鼻检测掺入了不同蛋白质物质的羊奶,采用主成分分析、线性判别分析对电子鼻响应值进行定性分析,采用线性回归分析、Fisher判别分析以及K-最邻近值分析对电子鼻响应值进行定量分析。结果表明:主成分分析和线性判别分析都能够区分不同类别的掺假样品。线性回归分析的决定系数为84.5%,表明回归方程估测可靠程度较高。Fisher判别分析的原始分类的正确率达到100.0%,交叉验证的正确率为98.2%,说明其预测结果较好。K-最邻近值分析对训练集的分类正确率达到95.1%,对验证集的分类正确率为97.1%,说明模型的预测结果良好。说明应用电子鼻技术检测羊奶中的蛋白质掺假具有一定的可行性。     

Determination of Moisture, Protein, Fat, and Calories in Raw Pork and Beef By Near Infrared Spectroscopy

The near infrared (NIR) (1100–2500 nanometers) of raw pork and raw beef samples was determined on a Neotec Model 6350 Scanning Spectrocomputer. Spectral curves were correlated with moisture, protein, fat, and calorie data determined by standard methods. A stepwise multiple regression technique was used to determine the optimum wavelengths for predicting each constituent. Correlation coefficients were ? 0.987 for moisture, fat, and calories and 0.885 for protein. The coefficients of variation for each of the nutrients were ? 3.46%.     

Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear

Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo–uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in ‘Ya’ pear. A total of 160 samples were prepared for the calibration (n?=?120) and prediction (n?=?40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of ‘Ya’ pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in ‘Ya’ pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r ²), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively.     

Investigation of Growth and Shrinkage Mechanisms in Vapor-Induced Expansion of Extrusion-Cooked Corn Grits

The development of expansion models with good predicting capabilities requires experimental data and a more fundamental understanding of the vapor-induced expansion behavior in extrusion cooking. In this study, we used a conventional and also a high-speed camera to observe the radial and axial expansion behavior of extrusion-cooked corn grits. Image processing was performed with an automated evaluation code. The experimental setup consisted of a novel high-speed extruder, which enabled the determination of the influence of high screw speeds of up to 1,200 rpm on extrudate growth and shrinkage. In addition to optical measurement of the expansion, moisture content and temperature development during growth and shrinkage were determined. Three distinct stages of expansion were observed: extrudate growth, short-term shrinkage, and long-term shrinkage. The initial extrudate growth rate fitted well with a linear regression. Especially the second stage of shrinkage occurring in a time range of 24 h after the experiment showed that shrinkage is often underestimated in existing models. Furthermore, total shrinkage increased at higher screw speeds. The data obtained deliver new insight into expansion phenomena and can support the development of predicting models.     

基于高光谱成像的主成分分析和波谱角分类技术的天麻粉质量标志物及相关成分可视化快速检测

目的 利用高光谱成像(hyperspectral imaging, HSI)技术, 通过一种原位质量标志物评估方法, 与高效液相色谱含量测定相结合, 建立预测模型对不同来源的天麻粉进行可视化评价。方法 采用高效液相色谱法(high performance liquid chromatography, HPLC)测定不同产地、不同晾干方式天麻粉中6种质量标志物含量并作为金标准; 苯酚-硫酸法测定天麻多糖含量、水分测定仪测定含水量, 两者作为辅助评价指标。HSI选用可见近红外(visible near-infrared, VNIR)和短波红外(short wave infrared, SWIR)双波段光谱进行分析比较。结果 以鲜品切块晾干方式制备的天麻粉的质量标志物对羟基苯甲醇、多糖和水分含量均增加, 同时天麻素和巴利森苷含量显著减少甚至消失。根据高光谱图像主成分分析(principal component analysis, PCA)预处理特征, 基于波谱角(spectral angle mapper, SAM)的监督分类算法对不同产地和加工方式制备的天麻粉进行了原位、无损分类, 其建立的线性回归模型实现了质量标志物含量的反演预测。结论 通过高效液相色谱含量测定的准确性和高光谱原位预测的无损性相结合的方式进行现场可视化评价和预测, 可以为不同产地、不同加工方式的天麻粉质量标志物快速检测提供技术参考。