Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

The objective of this study was to develop a near‐infrared (NIR) imaging system to determine rice moisture content. The NIR imaging system fitted with 15 band‐pass filters (wavelengths of 870–1,014 nm) was used to capture the spectral image. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both near‐infrared spectrometry (NIRS) and the NIR imaging system to determine the moisture content of rice. Comprehensive performance comparison among MLR, PLSR, and ANN approaches has been conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six significant wavelengths selected by the MLR model, which had high correlation with the moisture content of rice, were used as the input data of the ANN. The performance of the developed system was evaluated through experimental tests for rice moisture content. This study adopted the coefficient of determination (r_val²), the standard error of prediction (SEP), and the relative performance determinant (RPD) as the performance indices of the NIR imaging system with respect to the tests of rice moisture content. Utilizing these three models, the analysis results of r_val², SEP, and RPD for the validation set were within 0.942–0.952, 0.435–0.479%, and 4.2–4.6, respectively. From experimental results, the performance of NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided a high prediction capacity for the determination of moisture in rice samples. These results indicated that the NIR imaging system developed in this study can be used as a device for the measurement of rice moisture content.     

Prediction of Triticale Grain Quality Properties,Based on Both Chemical and Indirectly Measured Reference Methods,Using Near‐Infrared Spectroscopy

The increasing demand for triticale as food, feed, and fuel has resulted in the availability of cultivars with different grain quality characteristics. Analyses of triticale composition can ensure that the most appropriate cultivars are obtained and used for the most suitable applications. Near‐infrared (NIR) spectroscopy is often used for rapid measurements during quality control and has consequently been investigated as a method for the measurement of protein, moisture, and ash contents, as well as kernel hardness (particle size index [PSI]) and sodium dodecyl sulfate (SDS) sedimentation from both whole grain and ground triticale samples. NIR spectroscopy prediction models calculated using ground samples were generally superior to whole grain models. Protein content was the most effectively modeled quality property; the best ground grain calibration had a ratio of the standard error of test set validation to the standard deviation of the reference data of the test set (RPD_test) of 4.81, standard error of prediction (SEP) of 0.52% (w/w), and r² of 0.95. Whole grain protein calibrations were less accurate, with optimum RPD_test of 3.54, SEP of 0.67% (w/w), and r² of 0.92. NIR spectroscopy calibrations based on direct chemical reference measurements (protein and moisture contents) were better than those based on indirect measurements (PSI, ash content, and SDS sedimentation). Calibrations based on indirect measurements would, however, still be useful to identify extreme samples.     

Effect of Extrusion Process Parameters on the Quality of Buckwheat Flour Mixes

A response surface analysis using a second-order central composite design was used to study the effect of extrusion process parameters on the extrudate quality of three blends containing buckwheat flour. The extrudates were prepared as three blends. Blend 1 was a 55:40:5 (w/w) mix of light buckwheat flour, wheat flour, and nonfat dry milk (NFDM). Blend 2 was a 40:55:5 mix of light buckwheat flour, corn meal, and NFDM. Blend 3 was a 30:60:10 mix of light buckwheat flour, corn meal, and NFDM. The blends were processed in a twin-screw extruder with factorial combinations of the parameters including: process temperatures of 95–150°C, dough moisture of 15–22%, and screw speeds of 260–390 rpm. The linear components alone significantly explained most of the variation of expansion index, bulk density, water absorption, and breaking strength. The greatest amount of variability was explained by process temperature for blend 1. Dough moisture accounted for the greatest amount of variation for blends 2 and 3. Maximum predicted expansion index values and high water absorption percentages were obtained at low dough moisture levels. Dough moisture and process temperatures were the most important factors predicting bulk density. Sensory evaluation of texture, color, flavor, and general acceptability scores of selected samples ranked blend 3 > blend 2 > blend 1. The in vitro protein digestibility values ranked blend 1 > blend 2 > blend 3. An increase of up to 9.5% units in the protein digestibility values was observed when compared to the nonextruded raw blends.     

Effect of Wheat Moisture Content on Meal Apparent Particle Size and Hardness Scores Determined by Near-Infrared Reflectance Spectroscopy

The AACC Approved Method for near-infrared reflectance (NIR) spectroscopy to produce a wheat hardness score for wheat market classification can be corrected for variation in wheat moisture content. The cause of the variation in NIR spectra resulting from variation in wheat moisture was investigated. Ten samples each of soft red winter, soft white winter, hard red winter, and hard red spring wheats were stored at 20, 40, 60, and 80 equilibrium relative humidity. Wheats were then ground on a cyclone grinder as required by the standard method. Variation in unground wheat kernel moisture content resulted in variation in NIR data. NIR log 1/reflectance values increased at all wavelengths as wheat moisture content increased. Spectral changes were related to changes in the apparent particle size of ground wheat meal as it was influenced by moisture content. Higher moisture contents produced slightly higher apparent particle size in meal, suggesting larger particles of pericarp that became more pliable at higher moisture (temper) levels. The apparent particle size of meal of high moisture wheats resulted in greater NIR radiation scattering and decreased reflectance. Meal moisture content itself had no effect on the two NIR wavelengths used to evaluate wheat hardness.     

At-line near-infrared spectroscopy for prediction of the solid fat content of milk fat from New Zealand butter

Near-infrared (NIR) spectroscopy calibrations that will allow prediction of the solid fat content (SFC) of milk fat extracted from butter by one measurement during manufacture were developed. SFC is a measure of the amount of the solid fraction of fat crystallized at a temperature expressed as a percentage (w/w). At-line SFC determinations are currently performed by nuclear magnetic resonance (NMR) spectroscopy, which involves a 16 h delay period for tempering of the milk fat at 0 degrees C prior to the SFC measurements, from 0 to 35 degrees C in a series of 5 degrees C increments. The NIR spectra (400-2500 nm) were obtained using a sample holder maintained at 60 degrees C. Accurate predictions for the SFC (%) were developed by principal component analysis (PCA) and partial least-squares (PLS) regression models to relate the NIR spectra to the corresponding NMR values. The independent validation samples (N = 22) had a standard error of prediction (SEP) of 0.385-0.762% for SFC between 0 and 25 degrees C, with SFC reference values ranging between 70.42 and 8.96% with a standard deviation range of 3.36-1.47. The low bias (from -0.351 to -0.025), the slopes (0.935-1.077), and the excellent predictive ability (R2; 0.923-0.978) supported the validity of these calibrations.     

Determination of moisture in Cheddar cheese by near infrared reflectance spectroscopy

Near infrared reflectance (NIR) spectroscopy was used to determine the moisture content of Cheddar cheese. Through multiple linear regression analysis, a 3-wavelength calibration was developed for use with a commercial filter monochromator instrument. For a validation set of 47 samples, the correlation coefficient squared (r2) between the NIR and oven moisture methods was 0.92, with a standard error of performance (SEP) of 0.38%. Sample temperature was found to significantly affect the spectral response; therefore, it was necessary to equilibrate all samples to a uniform temperature prior to NIR analysis. Aging may also affect the NIR characteristics of cheese, although it was possible to develop a successful calibration that encompassed a wide range of aging times.     

应用可见/近红外高光谱成像测定鲑鱼片脂肪含量分布(英文)

脂肪作为一种重要的品质参数,在大西洋鲑鱼片中的分布很不均匀。为寻找一种能替代脂肪化学检测的快速无损的方法,该研究应用可见/近红外高光谱成像测定大西洋鲑鱼片的脂肪含量分布。分别采用可见/短波近红外(400-1100 nm)和近红外(900-1700 nm)系统获取大西洋鲑鱼片样本的高光谱图像。提取样本图像的平均光谱并与其相应的脂肪含量化学值采用偏最小二乘回归(partial least squares regression,PLSR)和最小二乘支持向量机(least-squares support vector machines,LS-SVM)建立相关性模型。为降低高光谱图像的共线性和冗余度,基于竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)分别在可见/短波近红外和近红外光谱区间提取16个(468,479,728,734,785,822,863,890,895,899,920,978,1005,1033,1040,1051 nm)和15个(975,995,1023,1047,1095,1124,1167,1210,1273,1316,1354,1368,1575,1632,1661 nm)特征波长,并分别建立PLSR和LS-SVM模型。特征波长模型的性能优于全波段模型,且近红外区间的特征波长PLSR模型为最优,预测决定系数(R2p)为0.92,预测均方根误差(root mean square error of prediction,RMSEP)为0.92%,剩余预测偏差(residual predictive deviation,RPD)为3.50。最后,将最优模型用于预测高光谱图像上所有像素点的脂肪含量以展示样本上脂肪的分布。此外,还基于该技术对大西洋鲑整鱼片实现了脂肪分布可视化。结果表明高光谱成像技术结合化学计量学方法在大西洋鲑鱼片脂肪的定量和分布可视化上有一定的研究和应用前景。     

Determining the Fat Acidity of Rough Rice by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to develop calibration curves for determining the fat acidity of whole-kernel and ground rough rice with 13% moisture content at 25°C. Partial-leastsquares regression (PLSR) uses the optimal calibration curve for wholekernel rough rice to measure the coefficient of determination (r²) of validation and standard error of prediction (SEP) of 0.87 and 0.83 mg of KOH/100 g of dry matter, respectively. However, the optimal calibration curve for ground rough rice has a higher r² of validation and lower SEP of 0.94 and 0.73 mg of KOH/100 g of dry matter, respectively. From 10 to 40°C, the temperature effect causes an increase of 0.24 mg of KOH/100 g of dry matter/°C in the predicted fat acidity of whole-kernel rough rice.     

Energy from fat determined by near-infrared reflectance spectroscopy

The amount of energy derived from fat in foods is a requirement of U.S. nutrition labeling legislation and a significant factor in diet development by health professionals. Near-infrared (NIR) spectroscopy has been used to predict total utilizable energy in cereal foods for nutrition labeling purposes, and in the current study, was investigated as a method for evaluation of the amount of energy derived from fat. Using NIR reflectance spectra (1104-2494 nm) of ground cereal samples and reference values obtained by calorimetry and by calculation, modified PLS regression models were developed for the prediction of percent energy from fat and energy from fat/g. The models were able to predict the percent of utilizable energy derived from fat with SECV and R(2) of 1.86-1.89% of kcal (n = 51, range 0-43.0) and 0.98, respectively, and SEP and r(2) of 1.74% of kcal (n = 55, range 0-38.0) and 0.98, respectively, when used to predict independent validation samples. Results indicate that NIR spectroscopy provides useful methods for predicting the energy derived from fat in food products.     

Near-infrared reflectance model for the rapid prediction of total fat in cereal foods

AOAC method 996.01, used in cereal foods to determine total fat as defined by the U.S. Nutrition Labeling and Education Act (NLEA), is laborious and time-consuming and utilizes hazardous chemicals. Near-infrared (NIR) reflectance spectroscopy, a rapid and environmentally benign technique, was investigated as a potential method for the prediction of total fat using AOAC method 996.01 as the reference method. Near-infrared reflectance spectra (1104-2494 nm) of ground cereal products (n = 72) were obtained using a dispersive spectrometer, and total fat was determined according to AOAC method 996.01. Using multivariate analysis, a modified partial least-squares model was developed for total fat prediction. The model had a SECV of 1.12% (range = 0.5-43.2%) and a multiple coefficient of determination of 0.99. The model was tested with independent validation samples (n = 36); all samples were predicted within NLEA accuracy guidelines. The results indicate that NIR reflectance spectroscopy is an accurate means of determining the total fat content of diverse cereal products for nutrition labeling.     

Prediction chemical composition and alveograph parameters on wheat by near-infrared transmittance spectroscopy

Moisture, protein, wet gluten, dry gluten, and alveograph parameters (W, P, and P/L) of whole wheat grown in different countries around the world were analyzed using near infrared (NIR) transmittance spectroscopy. Modified partial least squares on NIR spectra (850-1048.2 nm) were developed for each constituent or physical property. The best models were obtained for protein, moisture, wet gluten, and dry gluten with r(2) = 0.99, 0.99, 0.95, and 0.96, respectively. Initial alveograph NIR models proposed for all wheat samples did not perform well. However, when wheat samples were divided in two groups depending on W (deformation energy) values, NIR models were highly improved, showing enough prediction accuracy for screening wheat at the receiving stage at mills or elevators.     

Rice Quality by Spectroscopic Analysis: Precision of Three Spectral Regions

Three types of spectroscopy were used to examine rice quality: near infrared (NIR), Raman, and proton nuclear magnetic resonance (¹H NMR). Samples from 96 rice cultivars were tested. Protein, amylose, transparency, alkali spreading values, whiteness, and degree of milling were measured by standard techniques and the values were regressed against NIR and Raman spectra data. The NMR spectra were used for a qualitative or semiquantitative assessment of the amylose/amylopectin ratio by determining the 1–4 to 1–6 ratio for glucans. Protein can be measured by almost any instrument in any configuration because of the strong relationship between the spectral response and the precision of the reference method. Amylose has an equally strong relationship to the vibrational spectra, but its determination by any reference method is far less precise, resulting in a 10× increase in the standard error of cross‐validation (SECv) or standard error of performance (SEP) with R ² values equal to that of the protein measurement.     

Determining Weight and Moisture Properties of Sound and Fusarium‐Damaged Single Wheat Kernels by Near‐Infrared Spectroscopy

Single kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near‐infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R²) of 0.77 and standard error of cross validation (SECV) of 1.03%. This model predicted the MC of the wheat cultivar 2137 with R² of 0.81 and a standard error of prediction (SEP) of 1.02% and RPD of 2.2. Calibration models developed using all kernels from both cultivars predicted MC, fresh weight, dry weight, or water mass in kernels better than models that used only undamaged kernels from both cultivars. Single kernel water mass was more accurately estimated using the actual fresh weight of kernels and MC predicted by calibrations that used all kernels or undamaged kernels. The necessity for evaluating and expressing constituent levels in single kernels on a mass/kernel basis rather than a percentage basis was elaborated. The need to overcome the effects of kernel size and water mass on single kernel spectra before using in calibration model development was also highlighted.     

Correlation Between NIR Spectra and RVA Parameters During Germination of Maize

The physical, chemical, and morphological changes of maize seeds during germination were investigated using near‐infrared spectroscopy (NIR) and a method based on the Rapid Visco Analyser (RVA). Near‐infrared spectra provide information about both chemical and physical changes that occur in maize seed. The RVA curves make it possible to follow the process of germination. Four RVA parameters (peak viscosity, final viscosity, trough, and setback) were linearly correlated with germination time (R = 0.64–0.96), while the first derivatives of RVA curves contain specific information about starch structure. Water‐soluble protein (WSP) content of germinated maize seeds was measured using a flow injection analyser; this technique proved to be suitable for monitoring germination by following the mobilization of proteins. WSP and RVA parameters were highly correlated (R² = 0.82–0.95) with predicted values calculated from NIR spectra of dry samples. Strong intercorrelations existed between NIR spectra and viscosity data from the beginning of the swelling and gelatinization process. The NIR and RVA methods and WSP measurements are sensitive tools for investigating the physiological status of maize seeds during germination. Detecting early phase of germination and predicting functional properties rapidly and nondestructively may enhance the importance of NIR spectroscopic methods in agricultural quality control.     

Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection

Experimental results are presented on the use of partial least squares (PLS) regression and wavelength selection for the definition of models for visible-near-infrared (Vis-NIR) evaluation of soluble solids content in fruits. First, the relatively easy to deal with-but still not studied in the literature-case of cherry fruit is presented in detail. By using a very simple selection scheme, involving the subsampling of the spectral interval from 600 to 1100 nm with a fixed step, accurate models were found, consistently showing very favorable combinations of SEC and SEP values, in the 0. 50 degrees Brix range for a total variation of about 15 degrees Brix. Apricot fruit represented a more difficult species, and wavelengths to be included in the calibration had to be individually selected for the best results. Nevertheless, parsimonious models could be found, including a total of 38 spectral lines and leading to SEP values at the 0.75 degrees Brix level.     

基于dbiPLS-SPA变量筛选的固态发酵湿度近红外光谱检测

为了提高基于近红外光谱技术的固态发酵关键过程参数——湿度快速检测的精度和稳定性,研究采用动态反向区间偏最小二乘（dbiPLS）法结合连续投影算法（SPA）进行最佳光谱子区间和特征组合变量的筛选,通过交互验证法确定偏最小二乘（PLS）模型的主成分因子数,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。试验结果显示,最佳dbiPLS-SPA模型筛选的组合变量个数为8,其RMSEP和Rp分别为1.1795%（质量分数）和0.9430。试验结果表明,dbiPLS-SPA是一个有效的波长组合变量筛选方法,可简化模型结构、增强模型精度和稳健性。     

Molecular classification of barley (Hordeum vulgare L.) mutants using derivative NIR spectroscopy

Near-infrared reflectance (NIR) spectroscopy was used in the characterization of grain morphology mutants of barley ( Hordeum vulgare L.) in relation to grain nitrogen (N) content and protein composition. Derivative spectroscopy provided spectra with enhanced resolution, allowing wavelengths to be identified with clear differences in contribution from associated chemical bonds. Comparisons of fourth-derivative spectra of wholemeal flour from high-N grains with flour from low-N grains identified wavelengths at which there were statistically significant differences between the groups. Their importance was independently confirmed by step-up regression using these wavelengths to generate an equation predicting N content (R(2) = 0.98). Fourth-derivative spectral comparisons also allowed novel biochemical differences to be predicted. Visual assessment of the spectra of all mutants revealed a variable region (1470-1520 nm, corresponding to N-H stretch vibrations) that allowed two extreme sets to be defined. The protein extracted from these two sets differed markedly in hordein content.     

Rapid determination of the main organic acid composition of raw Japanese apricot fruit juices using near-infrared spectroscopy

The potential of near-infrared (NIR) spectroscopy to rapidly determine citric and malic acid contents of raw Japanese apricot (Japanese "ume", also known as the Japanese plum) fruit juice was investigated. In total, 314 raw juice samples with different organic acid compositions were collected over a long growth period, and spectra (1100-1850 nm) of these samples were acquired using an NIR spectrophotometer with a 1-mm path length. Calibrations were performed using a partial least-squares regression method based on a calibration sample set (211 samples), while validations were performed based on a validation sample set (103 samples). The results revealed good agreement between NIR spectroscopy and capillary electrophoresis, including the correlation coefficient (r2), standard error of prediction (SEP), and bias; no statistically (p = 0.05) significant differences were found for these parameters. Moreover, standard deviation ratios of reference data in the validation sample set to the SEP were higher than 3, indicating that NIR spectroscopy may represent an acceptable method for quantitative evaluation of citric and malic acids in raw Japanese apricot fruit juice.     

苹果栽培区土壤参数的近红外及中红外测定

Soil quality monitoring is important in precision agriculture.This study aimed to examine the possibility of assessing the soil parameters in apple-growing regions using spectroscopic methods.A total of 111 soil samples were collected from 11 typical sites of apple orchards,and the croplands surrounding them.Near-infrared(NIR) and mid-infrared(MIR) spectra,combined with partial least square regression,were used to predict the soil parameters,including organic matter(OM) content,pH,and the contents of As,Cu,Zn,Pb,and Cr.Organic matter and pH were closely correlated with As and the heavy metals.The NIR model showed a high prediction accuracy for the determination of OM,pH,and As,with correlation coefficients(r) of 0.89,0.89,and 0.90,respectively.The predictions of these three parameters by MIR showed reduced accuracy,with r values of 0.77,0.84,and 0.92,respectively.The heavy metals could also be measured by spectroscopy due to their correlation with organic matter.Both NIR and MIR had high correlation coefficients for the determination of Cu,Zn,and Cr,with standard errors of prediction of 2.95,10.48,and 9.49 mg kg-1 for NIR and 3.69,5.84,and 6.94 mg kg-1 for MIR,respectively.Pb content behaved differently from the other parameters.Both NIR and MIR underestimated Pb content,with r values of 0.67 and 0.56 and standard errors of prediction of 3.46 and 2.99,respectively.Cu and Zn had a higher correlation with OM and pH and were better predicted than Pb and Cr.Thus,NIR spectra could accurately predict several soil parameters,metallic and nonmetallic,simultaneously,and were more feasible than MIR in analyzing soil parameters in the study area.     

Evaluation of the Single Kernel Characterization System (SKCS) for Measurement of Sorghum Grain Attributes

The single kernel characterization system (SKCS) has been widely used in the wheat industry, and SKCS parameters have been linked to end‐use quality in wheat. The SKCS has promise as a tool for evaluating sorghum grain quality. However, the SKCS was designed to analyze wheat, which has a different kernel structure from sorghum. To gain a better understanding of the meaning of SKCS predictions for grain sorghum, individual sorghum grains were measured for length, width, thickness (diameter), and weight by laboratory methods and by the SKCS. SKCS predictions for kernel weight and thickness were highly correlated to laboratory measurements. However, SKCS predictions for kernel thickness were underestimated by ≈20%. The SKCS moisture prediction for sorghum was evaluated by tempering seven samples with varying hardness values to four moisture levels. The moisture contents predicted by SKCS were compared with a standard oven method and, while correlated, SKCS moisture predictions were less than moisture measured by air oven, especially at low moisture content. Finally, SKCS hardness values were compared with hardness measured by abrasive decortication. A moderate (r = 0.67, P < 0.001) correlation was observed between the hardness measurements. The SKCS predictions of kernel weight and diameter were highly correlated with laboratory measurement. Moisture prediction, however, was substantially lower by the SKCS than as measured by an air oven method. The SKCS should be suitable for measuring sorghum grain attributes. Further research is needed to determine how SKCS hardness predictions are correlated to milling properties of sorghum grain.