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.     

Prediction of maize seed attributes using a rapid single kernel near infrared instrument

Non-destructive measurements of seed attributes would significantly enhance breeder selection of seeds with specific traits, and could potentially improve hybrid development. A single kernel near infrared reflectance (NIR) instrument was developed for rapidly predicting maize grain attributes, which would enable plant breeders to quickly select promising individual seeds. With the overall goal being to develop spectrometric calibrations, absorbance spectra from 904 to 1685 nm were collected from 87 maize samples, with 30 kernels of each sample (2610 kernels total), representing a wide variability in the essential amino acids tryptophan and lysine, crude protein, oil and soluble sugar contents. Average sample spectra were matched to bulk reference values. Partial least squares regression (PLSR) calibration models with cross-validation were developed for both relative (% dry matter) and absolute (mg kernel⁻¹) constituent contents. Similarly, models using bagging PLSR were developed. The best model obtained was for relative crude protein content, with an R²p of 0.75 and a SEP of 0.47%. Kernel mass was also highly predictable (R²p=0.76, SEP=0.03 g). Tryptophan, lysine and oil were less predictable, but showed good potential for segregating individual seeds using NIR. Soluble sugar contents produced poor model statistics. Bagging PLSR yielded models with similar levels of prediction.     

Design, Construction, and Testing of an Automated NIR In-line Analysis System for Potatoes. Part I: Off-line NIR Feasibility Study for the Characterization of Potato Composition

An off-line near-infrared reflectance (NIR) feasibility study was conducted to explore the critical steps in the NIR determination of the major potato constituents (dry matter, starch, and protein) in relatively large (10 kg) potato samples. The results were important for the design of an automated industrial analysis system for potatoes with in-line NIR. The 10-kg potato samples were pulped with an industrial rotary saw blade rasp. A critical step in the NIR measurements was the occurrence of phase separation in the potato pulp. Phase separation manifests itself directly after pulping the potatoes and significantly affects the NIR spectrum. Therefore, during the NIR measurements, the potato pulp had to be stirred continuously. The NIR spectra (1,100–2,500 nm) were measured by applying an optical fiber NIR probe (EDAPT-1) connected to the NIR spectrophotometer (Technicon Infralyzer IA 500). NIR models for the concentration of dry matter, starch, and coagulating protein in potatoes have been developed. With the partial least squares regression procedure, promising NIR models were calculated. The NIR models were validated using an independent validation set of potato samples. The root mean square error in prediction of the samples in the validation set was 0.5% (w/w) for dry matter, 0.63 (w/w) for starch concentration, and 0.06% (w/w) for the coagulating protein.     

Regression-Based Models to Predict Rice Leaf Area Index Using Biennial Fixed Point Continuous Observations of Near Infrared Digital Images

Abstract

A weatherproof digital imaging system for the near infrared band (NIR, 820–900 nm) was positioned 12 m above a 600-m² rice field. During the 2008 and 2009 paddy rice seasons, the system automatically logged images at 10-min intervals throughout the day. Radiometric corrections for the NIR images utilized a solar irradiance sensor and prior calibrations to calculate 0900–1500 JST daily-averaged reflectance factors (DARF). Prior to heading, empirically derived equations for predicting leaf area index (LAI) using the 2008 DARF values in NIR, the cosines of angles between the view and the planting row directions, and between the view and the meridian directions were verified with the 2009 data set. Transformation of a model variable by arcsine square root function improved the performance of the LAI prediction by reducing the errors and bias at low LAI values. Adding variables to incorporate lateral angular components to the horizontal viewing angular parameters hardly affected the overall performance of the models and did not reduce variation. This was probably because the height and position of the camera system were the same in successive years. In-plot means of two or four predicted values in each plot reduced the root-mean square error 30%. These results indicate that radiometric NIR images derived using a fixed-point observation system can accurately predict LAI and the simple multiple linear regression equations developed for a given year can be used the following year without in-situ recalibration.     

Retrogradation behaviour of milled and brown rice pastes during ageing

Retrogradation of gelatinised starch is the main phenomenon that influences the texture of cooked rice. The rate of retrogradation is affected by several factors including amylose and amylopectin ratio, protein and fibre. The objective of this study was to analyse the pasting properties and the retrogradation behaviour of six traditional and five aromatic Italian rice varieties. The pasted gels, after cooling, were evaluated by dynamic rheological measurements for up to 7 days of storage at 4 °C. The samples were also analysed by a NIR spectrometer. The pasting properties and the retrogradation behaviour of milled rice flours strongly depended on the rice varieties. During gel ageing, a noticeable increase of G′ and G″ was observed only for the milled rice varieties Asia, Gange, Fragrance and Vialone Nano, characterised by a high amylose content. No further hardening was found either for the other milled varieties or for all the brown samples. The methods used in this work (dynamic oscillatory rheometry and FT-NIR spectroscopy) turned out to be very useful in the definition of rice starch gels ageing.     

Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to quantify fructan concentration in samples from seven grass species. Savitzky–Golay first derivative with filter width 7 and polynomial order 2 with mean centering was applied as a spectral pre-treatment method to remove unimportant baseline signals. In order to model the NIR spectroscopy data the partial least squares regression (PLSR) approach was used on the full spectra. Variable selection based on PLSR by jack-knifing within a cross-model validation (CMV) framework was applied in order to remove non-relevant spectral regions. PLSR was also used to model fructan concentrations from an augmented matrix [X|G], where X is spectra and G is correlation matrix of band specific information and X, in order to integrate the chemical band information in regression models. The present analysis showed that rapid quantification of fructans by NIR spectroscopy is possible and that jack-knifing PLSR within a CMV framework is an effective way to eliminate the wavelengths of no interest. Jack-knifing PLSR did not improve the predictive ability because the root mean square error of prediction (RMSEP) increased (1.37) compared to the full model (1.26). This was possibly due to signals from carbohydrates, which could act as cofactor in the prediction of fructans. However, jack-knifing PLSR within a CMV framework simplified the interpretation of the regression model with r² = 0.90 and RMSEP = 1.37.     

大麦籽粒抗性淀粉含量快速测定方法研究

为探索快速高效测定大麦籽粒中抗性淀粉含量的方法,利用衰减全反射中红外（attenuated total reflection mid-infrared spectroscopy,ATR-MIR）和近红外（near-infrared spectroscopy,NIR）光谱技术,分别用3种不同方法进行预处理,建立大麦样品的抗性淀粉含量快速测定红外模型,通过不同预处理预测模型的校正和内部交叉验证结果的比较,依据决定系数（r）和均方根误差（RMSE）筛选出基于ATR-MIR和NIR光谱的最佳预测模型,再对最佳预测模型进行外部验证。结果表明,经基线位移校正+范围归一化（BOC+RN）预处理后的PLS模型为最佳ATR-MIR预测模型;经标准正态变换+Savitzky-Golay法一阶求导（SNV+1thD）的预处理模型为最佳NIR预测模型。用验证集材料对BOC+RN和SNV+1thD最佳预测模型的预测效果进行外部验证,光谱预测值与化学测定值之间没有显著差异,说明两种方法均可以用于大麦籽粒抗性淀粉含量测定;ATR-MIR光谱比NIR光谱具有更好的预测能力。     

Relative nutritional value of some rices for growth ofTribolium castaneum larvae

Growth of red flour beetleTribolium castaneum (Herbst) larvae with brown or milled rice as the carbohydrate source was faster on diets containing milled rice than on those with brown rice. Larval growth was negatively correlated with amylose content of both brown and milled rice. Among high-amylose (>25%) milled rices, heavier larvae were obtained with rices of low gelatinization temperature (alkali-spreading values 6–7) than with those with higher gelatinization temperature (alkali-spreading values < 6). The differences in larval growth reflected relative digestibility of raw rice starch granules.     

Prediction of grain weight,brown rice weight and amylose content in single rice grains using near-infrared reflectance spectroscopy

The potential of near-infrared reflectance spectroscopy (NIRS) for simultaneous analysis of grain weight (mg), brown rice weight (mg) and milled rice amylose content (AC, %) in single rice grains was studied. Calibration equations were developed using 474 single grain samples, scanned as both rice grain and brown rice. An independent set containing 90 F₂ generation grains was used to validate the equations. In general, equations developed using the first derivative resulted in superior calibration and validation statistics compared with the second derivative and those developed using brown rice were superior to those developed from the rice grain. Fitting equations were developed and monitored with an external validation set. The standard error of prediction (corrected for bias) SEP(C) for AC, brown rice weight and rice grain weight for equations developed using brown rice were 2.82, 1.09 and 1.30, with corresponding coefficient of determinations (r²) of 0.85, 0.71 and 0.67, and SEP(C)/S.D. of 0.39, 0.57 and 0.59, respectively. It was demonstrated that NIRS provides a convenient way to screen single intact grains. This will be advantageous in early generation selection in rice breeding programs.     

Estimation and classification of popping expansion capacity in popcorn breeding programs using NIR spectroscopy

One of the most important quality traits in popcorn breeding programs is the popping expansion (PE) capacity of the kernel, which is the ratio of the volume of the popcorn to the weight of the kernel. In this study, we evaluated whether near infrared spectroscopy (NIR spectroscopy) could be used as a tool in popcorn breeding programs to routinely predict and/or discriminate popcorn genotypes on the basis of their PE. Three generations (F₁, F₂, and F_2:3) were developed in three planting seasons by manual cross-pollination and self-pollination. A total of 376 ears from the F_2:3 generation were selected, shelled, and subjected to phenotypic analysis. Genetic variability was observed in the F₂ and F_2:3 generations, and their average PE value was 31.5 ± 6.7 mL g⁻¹. PE prediction models using partial least square (PLS) regression were developed, and the root mean square error of calibration (RMSEC) was 6.08 mL g⁻¹, while the coefficient of determination (R_C²) was 0.26. The model developed by principal component analysis with quadratic discriminant analysis (PCA-QDA) was the best for discriminating the kernels with low PE (≤30 mL g⁻¹) from those with high PE (>30 mL g⁻¹) with an accuracy of 78%, sensitivity of 81.2%, and specificity of 72.2%. Although NIR spectroscopy appears to be a promising non-destructive method for assessing the PE of intact popcorn kernels for narrow breeding populations, greater variability and larger sample sizes would help improve the robustness of the predictive and classificatory models.     

基于PSO-SVR模型的小麦赤霉病病穗率预测方法

为探寻小麦赤霉病病穗率预测方法，基于滁州市2005-2020年小麦赤霉病病穗率资料和对应气象资料，运用相关性及灰色关联分析法(GRA)确定小麦赤霉病主要气象影响因子并作为支持向量回归(SVR)模型的输入向量，再利用粒子群算法(PSO)优化SVR模型的惩罚因子C和核函数参数g,建立基于粒子群算法优化的小麦赤霉病预测支持向量回归模型。同时针对本地不同小麦品种，构建PSO-SVR-SOUTH和PSO-SVR-NORTH的PSO-SVR分模型，应用3种模型对滁州地区小麦赤霉病病穗率进行预测。结果表明，拔节期至灌浆期是影响滁州小麦赤霉病的重要时段，各生育时期内降水量、雨日数、湿度、日照等气象因子与赤霉病有高关联；PSO-SVR赤霉病病穗率预测模型的起报时间越接近灌浆期，其预测精度越高，测试样本的预测值与实测值相关系数最高达0.68,均方根误差最小为9.55%;按照不同小麦品种构建的PSO-SVR-SOUTH和PSO-SVR-NORTH模型的预测效果要优于原PSO-SVR模型，其中最迟起报时间的PSO-SVR-SOUTH和PSO-SVR-NORTH模型的平均绝对误差分别较原PSO-SVR模型减少了...     

紫外可见光谱指纹图谱结合化学计量学在鉴别咖啡不同焙炒度中的应用

采用紫外可见光谱指纹图谱结合多元数据分析建立一种可快速鉴别不同焙炒度咖啡的方法,考察不同的光谱前处理方法对样品分类结果的影响,比较不同的模式识别方法对样品的鉴别结果。结果表明:一阶导数处理被选为最优的前处理方法,大部分样品能够在主成分分析(PCA)和系统聚类分析(HCA)中按各自特性聚为一类,线性判别分析(LDA)的分类效果优于PCA和HCA;最小二乘向量机(LS-SVM)模型的预报结果优于偏最小二乘判别分析(PLS-DA)和反传人工神经网络(BP-ANN),识别率和预报率均为100%。     

Variations in Concentration and Distribution of Health-Related Elements Affected by Environmental and Genotypic Differences in Rice Grains

Malnutrition is one of the major problems inmost of the developing countries, especially amongwomen, infants and children. Biofortification is a verynew strategy to enhance the bioavailability ofmicronutrients in staple food by using advancedbreeding meth…     

A chemometric evaluation of the underlying physical and chemical patterns that support near infrared spectroscopy of barley seeds as a tool for explorative classification of endosperm genes and gene combinations

Near infrared spectroscopic (NIR; 1100–2500 nm), chemical and genetic data were combined to study the pleiotropic secondary effects of mutant genes on milled samples in a barley seed model. NIR and chemical data were both effective in classifying gene and gene combinations by Principal Component Analysis (PCA). Risø mutants R-13, R-29 high (1→3, 1→4)-β-glucan, low starch and R-1508 (high lysine, reduced starch), near isogeneic controls and normal lines and recombinants were studied. Based on proteome analysis results, six anti-microbial proteins were followed during endosperm development revealing pleiotropic gene effects in expression timing that supporting the gene classification. To verify that NIR spectroscopy data represents a physio–chemical fingerprint of the barley seed, physical and chemical spectral components were partially separated by Multiple Scatter Correction and their genetic classification ability verified. Wavelength bands with known water binding and (1→3, 1→4)-β-glucan assignments were successfully predicted by partial least squares regression giving insight into how NIR-data works in classification. Highly reproducible gene-specific, covariate, pleiotropic classification patterns from NIR and chemical data were demonstrated in PCAs and by visual inspection of NIR spectra. Thus PCA classification of NIR-data gives the classical genetic concept, ‘pleiotropy’, a new operational definition as a fingerprint from a spectroscopic representation of the phenome carrying genetic, physical and chemical information. It is concluded that barley seed phenotyping by NIR and chemometrics is a new, reliable tool for characterising the pleiotropic effects of mutant gene combinations and other genotypes in selecting barley for quality in plant breeding.     

谷粒形状与稻米品质相关性研究

通过对安徽省农科院水稻所选育的17个水稻品种（组合）进行粒形分类，并对谷粒形状与稻米品质的相关性进行分析。结果表明，供试品种（组合）的粒长多属短粒形，长宽比多为短圆形。谷粒形状（粒长、粒度、长宽比）与加工品质（糙米率、精米率、整精米率）呈显著或极显著相关，与蒸煮品质中的碱消值呈显著相关；千粒重对各稻米品质性状的影响均未达显著水平。并提出水稻品质育种只有做到粒长、粒重兼顾，才有可能在提高产量的同时达到改良米质的目的。     

Continuous Monitoring of Visible and Near-Infrared Band Reflectance from a Rice Paddy for Determining Nitrogen Uptake Using Digital Cameras

Abstract

A two-band digital imaging system —one band for the visible red band (RED, 630?670 nm) and the other for the near infrared band (NIR, 820?900 nm)— was devised and positioned at a height of 12 m above a rice field of 300 m² in area during the 2007 growing season. The imaging system automatically logged bird’seye view images at 10-min intervals from 0800?1600 every day. Radiometric corrections for the pairs of two-band images were done using solar irradiance sensors and preceding calibrations to calculate daily band-reflectance and the normalized difference vegetation index (NDVI) values for 9 plots of rice plants, with 3 levels of planting density and basal fertilization. The daily- averaged reflectance values in the RED and the NIR bands showed different but smooth seasonal changing patterns according to the growth of plants. At the maximum tiller number and the panicle formation stages, the RED and NIR reflectance values had correlation coefficients (r) of 0.79 and 0.81 with above-ground nitrogen absorption per unit land area (NA, g m-²), respectively, whereas the NDVI using the two band reflectance values showed r-value of -0.13. An empirically derived equation for the NA using two band reflectance values showed r-value of 0.96 and a root mean square of error (RMSE) 0.5 g m^–2 (10% of the mean observed NA) in the estimation for the original (not validated) data set acquired at the maximum tiller number and the panicle formation stages. The results indicated that reflectance observation in the RED and NIR bands using the digital imaging system was potentially effective for assessing rice growth.     

Valuation of Rice Postharvest Losses in Sub-Saharan Africa and Its Mitigation Strategies

正Data on rice harvest and postharvest loss in Sub-Sahara Africa(SSA) is scanty making it difficult for stakeholders to appreciate the loss and set priority areas for loss reduction along the value chain. To address this problem, a protocol was developed and validated for postharvest loss(PHL) quantification in SSA. Quantitative losses at each segment were determined by field measurements.     

Description of batter mixing using near infrared spectroscopy

The aim of the present study was to describe the physicochemical events occurring during batter mixing at different water contents (51.8, 54.4, and 56.7 g of water/100 g of dough) using near infrared (NIR) spectroscopy. An FT-NIR spectrometer over the 1000–2500 nm range with a fibre optic probe was used to record NIR spectra in-line. The analysis of both one-dimensional statistical method (principal components analysis) and two-dimensional statistical methods (generalised two-dimensional correlation spectroscopy) was conducted to evaluate the possibilities of NIR spectroscopy to monitor physical and physicochemical modifications observed during mixing of batter. The NIR results were in agreement with the physical and physicochemical analysis traditionally used to study bread dough mixing (consistency and glutenin depolymerisation). PCA on raw NIR spectra demonstrated that PC₁ describes the same traces as the dough consistency curves. PCA on raw NIR spectra can be used to monitor the batter mixing and to identify the NIR mixing time close to the t_peak.PCA on spectra after second derivative demonstrated that PC₁ and PC₂ traces described different traces compared to the dough consistency curves. The loading spectra associated to PC₁ and PC₂ suggested that almost the same physicochemical and chemical mechanisms occur during the dough mixing at 51.8 or 54.4% water contents, but with kinetic and intensity differences. The 2D COS method allowed a sequence of chemical events occurring during mixing for the batters at 51.8 and 54.4% water contents to be tentatively proposed. The 2D COS did not give clear physicochemical differences between the three batters during mixing. The NIR results for the highly hydrated batter (56.7%) were difficult to analyse due to its high water content.     

Protein and energy utilization of brown,undermilled and milled rices by preschool children

The protein and energy utilization of brown, undermilled and milled rices (variety IR32) were studied in 5–6 preschool children through diets in which 2/3 of N was from rice and 1/3 from casein or milk powder at 200 or 250 mg N/kg body weight. Brown rice was not significantly inferior to milled rice in terms of nitrogen absorption and retention by the children. Its energy and fat contents were, however, less utilized than those of milled rice. Children on the control casein or milk diets showed better apparent nitrogen absorption than those on the rice-based diets but nitrogen retention of all the diets was similar in all three experiments.     

Description of batter mixing using near infrared spectroscopy

The aim of the present study was to describe the physicochemical events occurring during batter mixing at different water contents (51.8, 54.4, and 56.7 g of water/100 g of dough) using near infrared (NIR) spectroscopy. An FT-NIR spectrometer over the 1000–2500 nm range with a fibre optic probe was used to record NIR spectra in-line. The analysis of both one-dimensional statistical method (principal components analysis) and two-dimensional statistical methods (generalised two-dimensional correlation spectroscopy) was conducted to evaluate the possibilities of NIR spectroscopy to monitor physical and physicochemical modifications observed during mixing of batter. The NIR results were in agreement with the physical and physicochemical analysis traditionally used to study bread dough mixing (consistency and glutenin depolymerisation). PCA on raw NIR spectra demonstrated that PC₁ describes the same traces as the dough consistency curves. PCA on raw NIR spectra can be used to monitor the batter mixing and to identify the NIR mixing time close to the t_peak.PCA on spectra after second derivative demonstrated that PC₁ and PC₂ traces described different traces compared to the dough consistency curves. The loading spectra associated to PC₁ and PC₂ suggested that almost the same physicochemical and chemical mechanisms occur during the dough mixing at 51.8 or 54.4% water contents, but with kinetic and intensity differences. The 2D COS method allowed a sequence of chemical events occurring during mixing for the batters at 51.8 and 54.4% water contents to be tentatively proposed. The 2D COS did not give clear physicochemical differences between the three batters during mixing. The NIR results for the highly hydrated batter (56.7%) were difficult to analyse due to its high water content.