Evaluation of malting barley quality using exploratory data analysis. I. Extraction of information from micro-malting data of spring and winter barley

This paper presents an exploratory multivariate approach for analysis of malting barley quality data. By using principal component analysis (PCA) and partial least squares regression (PLSR) complex malting quality data are combined into functional factors which are used for malting barley quality characterisation. Fifty barley samples were used in this investigation, representing 15 spring barley and 10 winter barley varieties grown at two locations in Denmark. The samples were micro-malted and mashed and analysed for 13 quality parameters according to official methods of the European Brewery Convention. These data were combined and reduced into a few latent (functional) factors using PCA by which it is demonstrated that the modification of β-glucan plays a major role in both spring and winter barleys. Additionally, the spring barley and winter barley samples display different covariate latent structures, mainly in the nitrogen and diastatic power patterns. It is furthermore shown that graphic display as facilitated by exploratory data analysis, can be utilized in order to evaluate genotype-environmental interactions by considering the position and movements of the individual objects (genotypes in this instance) in the score plots. Thus, in contrast to the classical analysis of variance, the samples can be individually evaluated and the corresponding loadings can be used to validate the genetic and environmental effect of a given sample in a quality perspective.Several of the investigated malting quality parameters are highly intercorrelated. This fact is utilized by applying PLSR to barley and malt data for the prediction of wort quality in order to exclude the mashing step. This approach was successful for the modification-dependent wort parameters, extract, wort β-glucan and viscosity.     

Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis

Near infrared (NIR) hyperspectral imaging and hyperspectral image analysis were evaluated for their potential to track changes in fungal contamination on and fungal activity immediately under the surface of whole maize kernels (Zea mays L.) infected with Fusarium verticillioides. Hyperspectral images of clean and infected kernels were acquired using a SisuChema hyperspectral pushbroom imaging system with a spectral range of 1000–2498 nm at predetermined time intervals after infection. Background, bad pixels and shading of acquired absorbance images were removed using exploratory principal component analysis (PCA). When plotting PC4 against PC5, with percentage sum of squares (%SS) 0.49% and 0.34%, three distinct clusters were apparent in the score plot and this was associated with degree of infection. Loading line plots, with prominent peaks at 1900 nm and 2136 nm, confirmed that the source of variation was due to changes in starch and protein. Partial least squares (PLS) regression models, with time as the Y variable, were calculated and also indicated that changes over time were apparent. Variable importance plots (VIP) confirmed the peaks observed in the PCA loading line plots. More systematic future experiments are needed to confirm this, but it can already be concluded that early detection of fungal contamination and activity is possible.     

基于主成分分析和随机森林回归的冬小麦冠层叶绿素含量估算

为提高冬小麦冠层光谱对叶绿素含量的估算精度,以陕西省乾县冬小麦为研究对象,利用SVC-1024i光谱仪和SPAD-502型叶绿素仪实测了冬小麦冠层反射率和叶绿素含量,分析了一阶导数光谱、10种特征参数和9种植被指数与叶绿素含量的相关性,并利用主成分分析(PCA)对叶绿素敏感的可见光波段(390～780 nm)一阶导数光谱进行降维,将特征值大于1的主分量结合特征参数和植被指数形成不同的输入变量,用偏最小二乘回归和随机森林回归构建冬小麦冠层叶绿素估算模型,并利用独立样本对模型进行验证。结果表明,小麦冠层叶绿素含量与一阶导数光谱在751 nm处的相关性最高(r=0.71),特征参数中红边蓝边归一化(SDr-SDb)/(SDr+SDb)与叶绿素含量的相关性最高(r=0.66),植被指数(VI)中修正归一化差异指数(mND705)相关性最高(r=0.74)。在输入变量相同的情况下,基于随机森林(RF)回归的预测模型均优于偏最小二乘回归(PLSR)模型,其中PCA-VI-RF模型的各精度指标均达到最优(r²=0.94,RMSE=1.05,RPD=3.70),是冬小麦冠层叶绿素...     

Feasibility study on the use of multivariate data methods and derivatives to enhance information from barley flour and malt samples analysed using the Rapid Visco Analyser

In order to extend the use of the Rapid Visco Analyser (RVA) as an analytical tool in barley breeding programs, it is necessary to find relationships between barley flour pasting properties and potential malting quality. Traditionally, the RVA is used to provide discrete values related with the pasting characteristics of the sample under analysis. Although this approach is very useful, considering the rich data generated by RVA analysis, this can result in the loss of information about starch pasting characteristics, reducing the potential of the RVA as an analytical tool. This study aims to evaluate the ability of using multivariate data methods (MVA) and derivatives to the profile generated by the RVA as a source of information to further study starch pasting characteristics to select materials in barley breeding programs or other food applications. The use of MVA techniques such as principal component analysis (PCA) and partial least squares (PLS) regression together with the use of derivatives (e.g. first and second derivatives) allows better interpretation of the RVA profile, resulting in more information related to the pasting properties of the sample.     

Discrimination of wheat grain varieties using image analysis and neural networks. Part I. Single kernel texture

The development of non-destructive methods for the evaluation of cereal grain varieties has important implications for the food processing industry. The described experiment investigated 11 varieties of spring and winter wheat of different quality class. The analysis was performed on images acquired from a flatbed scanner interfaced to a PC. Kernel images were digitalized at high resolution (2673 × 4031) with 24-bit depth and 400 dpi. The variables input into the statistical model were the textures of single kernel projections. Textures were computed separately for seven channels (R, G, B, Y, S, U, V). The results were examined with the application of discriminant analysis and neural networks. The accuracy of texture-based classification of 11 wheat varieties reached 100%. The experimental design which yielded the most satisfactory results comprised texture measurements from the combined area of 20 kernels and variables from seven channels input into the neural network. The final classification quality was not affected by the year of cultivation, moisture content or grain variety.