Adding Rice Starch to Hard Wheat Flour for Improving its Dough Properties and Baking Quality

Results show that the addition of rice starch to hard wheat flour improves the dough properties and baking quality as demonstrated by the tests and the readings of Farinograph, Extensograph and Amylograph as well as increases the reducing sugars, acidity and the volume of the produced bread. Experiments proved that best results were observed when rice starch was added to hard wheat flour at 6 to 8%.     

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.     

Variability and relationship among Mixolab and Falling Number evaluation based on influence of fungal α-amylase addition

BACKGROUND: In bread‐making technology, α‐amylase activity is routinely measured with a Falling Number device to predict wheat flour quality. The aim of this study was to determine the possibility of using Mixolab parameters to assess the Falling Number (FN) index. The effects of different doses of fungal α‐amylase addition on the Mixolab characteristics and FN index values were investigated. RESULTS: Principal component analysis was performed in order to illustrate the relationships between the Mixolab parameters and the FN index. To highlight the linear combination between the FN index values and the Mixolab parameters used to evaluate starch pasting properties (C3, C4, C5 and point differences C34 and C54), a multivariate prediction model was developed. Greatest precision (R = 0.728) was obtained for the linear regression FN = f(C4, C54) model. This model was tested on a different sample set than the one on which it was built. A high correlation was obtained between predictive model and measured FN index values (r = 0.896, P = 0.01). CONCLUSION: The model provides a framework to predict the evolution of the FN index, which is predicted by the torque for cooking stability (C4) and the difference between points C5 and C4 (C54). The obtained results suggested that the Mixolab device could be a reliable instrument for evaluation of the FN index values. Copyright © 2012 Society of Chemical Industry     

近红外光谱技术快速测定鹅肉新鲜度

目的：应用近红外光谱技术快速检测鹅肉的新鲜度,评价指标包括总挥发性盐基氮和pH值。方法：采集完整冷鲜鹅肉的近红外光谱（950～1 650 nm）,光谱经多种校正预处理后,采用偏最小二乘法建立鹅肉新鲜度的定量预测数学模型。结果：对于这2 种指标均采用标准常态变量结合偏最小二乘法所建立模型的预测效果最好,总挥发性盐基氮和pH值定量校正数学模型的模型决定系数分别为0.727、0.991,内部交互验证均方根误差分别为3.666、0.028。用此模型对预测集20 个样品进行预测,预测值与实测值的相关系数分别达到0.976、0.705,预测值平均偏差分别为－0.240、－0.024,预测值和实测值之间没有显著性差异（P＞0.05）。结论：近红外光谱作为一种无损快速的检测方法,可用于评价鹅肉新鲜度。     

Development and evaluation of multiple regression models for prediction of sweet cherry liking

In two experiments, multiple regression models were developed and evaluated to identify the relevant sensory attributes for cherry liking. In Experiment 1, 16 judges evaluated 18 cherry varieties for seven visual characteristics (colour intensity, uniformity-of-colour, speckles, size, stem length, external firmness and ‘visual’ liking) and seven flavour/texture characteristics (flesh firmness, flesh colour intensity, juiciness, sweetness, sourness, flavour intensity and ‘flavour/ texture’ liking). Stepwise multiple regression was used to develop the most appropriate statistical models for prediction of visual and flavour/texture liking based on visual and flavour/texture characteristics, respectively. Both models were simple and easily understandable with two sensory variables. The best model for visual liking required only size and uniformity-of-colour variables; whereas, the best model for flavour/texture liking required sweetness and flavour intensity variables. In Experiment 2, 18 judges evaluated 30 sweet cherry cultivars, using the same methodology, to create a validation data set. Correlation coefficients (R) and prediction standard errors (PSEs) between the observed (Experiment 2) and predicted (Experiment 1) liking scores were used to evaluate the prediction equations. The prediction equation for flavour/texture liking was most satisfactory (R = 0.85, PSE = 0.61). A new equation developed from the validation data confirmed the importance of sweetness and flavour intensity. In contrast, the prediction equation for visual liking was less satisfactory (R = 0.56) and a new equation developed from the validation data set confirmed only size as an important variable.     

Real-time process modeling of particleboard manufacture using variable selection and regression methods ensemble

This study focuses on the real-time prediction of mechanical properties such as internal bond strength (IB) and modulus of rupture (MOR) for a wood composite panels manufacturing process. As wood composite panel plants periodically test their products, a real time data fusion application was developed to align laboratory mechanical test results and their corresponding process data. Fused data were employed to build regression models that yield real-time predicted mechanical property values when new process data become available. The modeling algorithm core uses genetic algorithm to preselect a meaningful subset of process variables. Calibration models are then built using several regression methods: multiple linear regression, ridge regression, neural networks, and partial least squares regression (PLS). Four different predicted response values were generated for each new record of real time process variables. On-line validation results showed good performance of the ridge regression method with a 0.89 correlation coefficient between actual and predicted MOR values, a root mean square error (RMSEP) of 1.05 MPa and a mean normalized error of 9 %. IB was best predicted by PLS with a 0.81 correlation coefficient between actual IB and PLS predicted IB values, a RMSEP of 75.1 kPa, and a mean normalized error of 15 %.     

Chemometric Approach to Characterization of Flour Mill Streams: Chemical and Rheological Properties

The aim of the present study was to utilize chemometric methods (the principal component analysis and hierarchical cluster analysis) for monitoring the certain aspects of flour mill streams quality and their interrelation to selected rheological properties. Thirty-seven flour mill streams were separated from industrial mill of 300 t/day capacity. All flour streams were analyzed for ash, protein, wet gluten, and damaged starch content and rheological properties as determined by Brabender Farinograph, Extensograph, and Amylograph. The obtained results indicated that break, sizing, and reduction flour streams exhibited different rheological behavior in relation to a change in protein, wet gluten, ash, and mechanically damaged starch content within the milling passages. Rheological properties of dough during mixing and kneading as well as during extension were different with regard to the technological phase of milling from which they were extracted. The obtained results could be utilized for selection of certain flour streams in production of special-purpose flours.     

Prediction of storage quality of fresh‐cut green peppers using artificial neural network

To extend the shelf‐life of fresh‐cut fruits and vegetables, it is essential to develop models that can accurately predict their storage quality. In view of this, an artificial neural network (ANN) model based on back propagation (BP) algorithm was developed to predict the storage quality (degree of yellowness, water loss, textural firmness and vitamin C content) of fresh‐cut green peppers. The prediction accuracy of ANN was compared with that of multiple linear regression‐based models. The root mean square error (RMSE), mean absolute error (MAE), sum of squared residuals (SSR) and standard error of prediction (SEP) were used as comparison parameters. The results showed that the accuracy and goodness of fit of the storage quality parameters predicted by ANN were better than those predicted by multiple linear regression‐based models. The RMSE, MAE, SSR and SEP values obtained from the former were much lower than those obtained from the latter.     

Effectiveness of mid-infrared spectroscopy for the prediction of cow milk metabolites

Proton nuclear magnetic resonance (¹H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of ¹H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through ¹H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional ¹H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.     

基于材料参数的中支烟烟气常规成分释放量预测模型

为建立基于材料参数的中支烟烟气常规成分释放量预测模型,采用中心组合结合正交试验设计方法设计了不同材料参数样品,使用线性回归和逐步回归方法构建了中支烟烟气总粒相物、焦油、烟碱、CO和水分释放量以及烟支开式吸阻和总通风率等7个预测模型,根据统计学原理中交叉验证标准差（RMSECV）最小及预测值与实测值线性相关系数最大的原则筛选出最优预测模型。采用市售中支烟对各预测模型进行了验证。结果表明:7个模型的预测精度良好,总粒相物、焦油、烟碱、CO、水分释放量以及烟支开式吸阻和总通风率平均预测相对偏差分别为4.0%、2.1%、6.0%、4.5%、8.3%、6.6%和3.2%,且对于不同配方、辅材参数的中支烟具有较好的适用性。      

近红外光谱变量选择及其在苹果可溶性固形物含量无损速测中的应用

目的采用近红外光谱技术,筛选有效变量对苹果可溶性固形物含量进行无损快速检测。方法以改进无变量信息消除算法为变量筛选方法,采用多元线性回归算法建立校正模型,采用外部盲样对模型进行预测准确度评价。结果基于改进无信息变量消除算法,筛选1391、1435、1521、1589nm4个关键波长作为变量,其所建校正模型的测定系数为0.6823,校正误差均方根为1.06,交互验证测定系数为0.6780,交互验证误差均方根为1.06。外部验证测定系数为0.6585,预测误差均方根为1.07。经F检验,预测模型的预测值与测定值之间具有显著相关性。结论该方法基本能够满足苹果可溶性固形物含量无损快速检测的需求,并可为水果可溶性固形物含量无损快速检测仪器的研制提供一定的技术参考。     

Prediction of minerals,fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy

Prediction models for the mineral, fatty acid (FA) and cholesterol contents of commercial European cheeses using near infrared transmittance spectroscopy were developed. Cheese samples (n = 145) were from different dairy species and ripening time. Sample spectra were matched with mineral, FA and cholesterol reference data to develop prediction models. Modified partial least squares regressions were validated through cross-validation procedure on the complete dataset (n = 145) and through external validation after dividing the data into calibration (74%) and external validation (26%) sets. Satisfactory models were developed for Ca, P, S, Mg and Zn, and for FA groups (saturated, unsaturated, monounsaturated and polyunsaturated FAs), major FAs (myristic, palmitic and oleic acids) and some minor FAs, whereas cholesterol content could not be predicted with adequate accuracy. Results of the present study are a precursor to at-line utilisation of prediction models for the most abundant cheese minerals and FAs at an industry level.     

Wheat dough rheology and bread quality effected by Lactobacillus brevis preferment,dry sourdough and lactic acid addition

The influence of chemical and biological acidification on dough rheological properties and bread quality has been investigated. Two different flour types were used. Dough was chemically acidified with lactic acid. Two types of biologically acidified dough were prepared: dough with dry sourdough and with a Lactobacillus brevis preferment. Wheat dough rheological properties were investigated using the Farinograph, Extensograph and Amylograph. The baking response was also determined using standard baking tests. Addition of acidifiers resulted in firmer doughs with less stability, decreased extensibility and decreased gelatinisation maximum. The biological acidifiers increased the bread specific volume. Lactic acid addition had no influence on bread specific volume. In general, biological and chemical acidification decreased bread hardness. The addition of dry sourdough significantly decreased the lightness and increased the yellowness and redness of the bread crumb. The crust chroma, hue angle and brownness index were significantly changed by addition of acidifiers.     

Prediction of warp and weft yarn crimp in cotton woven fabrics

The aim of this study was to develop statistical models for the prediction of warp and weft crimp percentage of cotton woven fabrics. The developed models are based on the empirical data obtained from carefully developed 60 fabric samples with different yarn linear densities, fabric densities, and weave designs. The predictability and accuracy of the developed models was assessed by correlation analysis of the predicted and actual crimp values of another set of eight fabric samples which was not used for the development of models. The results show fairly good capability and accuracy of the prediction models.     

甘薯水分和还原糖协同向量NIR快速检测方法

利用近红外光谱技术建立新鲜甘薯水分和还原糖含量的预测模型,实现快速检测与分析,为甘薯品质分析和种质资源筛选提供便捷。选用不同品系甘薯样品146份,109份作为校正样品,37份作为验证样品。运用不同光谱预处理方法、协同区间偏最小二乘最优波长选择法以及主成分回归和偏最小二乘法建立甘薯水分和还原糖模型。结果显示,所建甘薯水分(还原糖)最优模型的决定系数、预测均方根误差和标准差比率分别为0.974(0.885),1.154(0.270)和6.334(3.148)。表明2种模型具有较好的检测性能,近红外光谱模型的预测值与其相应的化学值有较好的相关性,适用于大批量甘薯选育时水分和还原糖含量的快速检测。     

基于LVF-NIR的樱桃可溶性固形物含量无损速测模型的建立与优化

目的建立基于便携式近红外光谱仪的樱桃可溶性固形物含量无损快速定量检测模型,从而实现樱桃品质的无损快速检测。方法以北京通州产红灯樱桃、黄玉樱桃为研究对象,采用便携式线性渐变分光近红外光谱仪采集光谱数据,并采用折光仪测定其可溶性固形物含量;采用偏最小二乘回归结合全交互验证算法将光谱数据与可溶性固形物含量测定值建立定量校正模型,采用外部验证集对模型的预测性能做进一步测试。结果红灯樱桃可溶性固形物含量模型的R_C~2、RMSEC、R_(CV)~2、RMSECV、RPD分别为0.9194、0.79、0.8920、0.92、3.54,黄玉樱桃可溶性固形物含量模型的R_C~2、RMSEC、R_(CV)~2、RMSECV、RPD分别为0.8618、0.76、0.8246、0.86、2.70;两种樱桃可溶性固形物含量合并模型的R_C~2、RMSEC、R_(CV)~2、RMSECV、RPD分别为0.9125、0.81、0.8946、0.89、3.38。结论基于便携式线性渐变分光近红外光谱仪数据所建校正模型具有较好的准确度,可满足樱桃可溶性固形物含量的无损快速检测需求。     

近红外光谱技术快速测定鹅肉嫩度

目的：应用近红外光谱技术快速检测鹅肉的嫩度值。方法：采集完整鹅肉的近红外光谱（950～ 1 650 nm）,光谱经多种校正预处理后,再分别采用主成分回归和偏最小二乘法建立鹅肉嫩度的定量预测数学模 型。结果：采用5点移动窗口平滑处理结合偏最小二乘法所建立模型的预测效果最好,嫩度定量校正数学模型的模 型决定系数为0.908 0,内部交互验证均方根误差为113.618 6。用此模型对预测集20 个样品进行预测,预测值与实 测值的相关系数达到0.971 1,预测值平均偏差为21.673 g,预测值和实测值之间没有显著性差异（P＞0.05）。结 论：近红外光谱作为一种无损快速的检测方法,可用于评价鹅肉的嫩度。     

Prediction and evaluation of urine and urinary nitrogen and mineral excretion from dairy cattle

Urine excretion is a substantial factor in the amount of manure that needs to be managed, and urinary N can contribute to ammonia volatilization. Development and validation of prediction equations focusing on dietary factors to decrease urine and urinary nutrient excretion will provide information for managing urine and feces separately or for other future technologies. The objective of this study was to develop equations for prediction of urine excretion and excretion of urinary N, Na, and K and to evaluate both new and previously published prediction equations for estimation of urine and urinary nutrient excretion from lactating dairy cows. Data sets from metabolism studies conducted at Washington State University were compiled and evaluated for excretion of minerals. Urine excretion averaged 24.1 kg/d and urinary nitrogen excretion ranged from 63 to 499 g/d in the calibration data set. Regression equations were developed to predict urine excretion, urinary N excretion, and urinary Na and K excretion. Predictors used in the regression equations included milk yield, body weight, dietary crude protein percentage, milk urea nitrogen, and nutrient intakes. Previously published prediction equations were evaluated using data sets from Washington State University and the University of Wisconsin. Mean and linear biases were evaluated by determining the regression of residuals on predicted values. Evaluation and validation of prediction equations are important to develop equations that will more accurately estimate urine and urinary nitrogen excretion from lactating dairy cows.     

UV spectrophotometry method for the monitoring of galacto-oligosaccharides production

Monitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple, fast and inexpensive UV spectrophotometric method, together with partial least squares regression (PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS) and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were trained and their prediction abilities evaluated by cross-validation and external validation being the results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra data of the fermentation samples, gave, in general, the best performance being able to accurately and precisely predict lactose and total GOS levels, with standard error of prediction lower than 13 g kg⁻¹ and coefficient of determination for the external validation set of 0.93–0.94, showing residual predictive deviations higher than five, whereas lower precision was obtained with the multiple model generated with PLS. The results obtained show that UV spectrophotometry allowed an accurate and non-destructive determination of sugars in fermentation samples and could be used as a fast alternative method for monitoring GOS production.     

Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy

Cheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerød, Denmark) over the spectral range, from 5,000 to 900 wavenumber × cm⁻¹. The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole data set, but the coefficient of determination of validation values for the external validation sets were much lower for all traits (0.30 to 0.73), and particularly for fat recovery (0.05 to 0.18), for the training sets compared with the full data set. For each testing subset, the (co)variance components for the measured and FTIR-predicted phenotypes were estimated using bivariate Bayesian analyses and linear models. The intraherd heritabilities for the predicted traits obtained from our internal cross-validation using the whole data set ranged from 0.085 for daily yield of curd solids to 0.576 for protein recovery, and were similar to those obtained from the measured traits (0.079 to 0.586, respectively). The heritabilities estimated from the testing data set used for external validation were more variable but similar (on average) to the corresponding values obtained from the whole data set. Moreover, the genetic correlations between the predicted and measured traits were high in general (0.791 to 0.996), and they were always higher than the corresponding phenotypic correlations (0.383 to 0.995), especially for the external validation subset. In conclusion, we herein report that application of the cross-validation technique to the whole data set tended to overestimate the predictive ability of FTIR spectra, give more precise phenotypic predictions than the calibrations obtained using smaller data sets, and yield genetic correlations similar to those obtained from the measured traits. Collectively, our findings indicate that FTIR predictions have the potential to be used as indicator traits for the rapid and inexpensive selection of dairy populations for improvement of cheese yield, milk nutrient recovery in curd, and daily cheese production per cow.