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1.
Maize Starch Yield Calibrations with Near Infrared Reflectance 总被引：1，自引：0，他引：1
Marvin R. Paulsen Lester O. Pordesimo Mukti Singh Steven W. Mbuvi Binying Ye 《Biosystems Engineering》2003,85(4):455-460
Maize starch yield is affected by variety, environmental growing conditions, and drying conditions. One-hundred gram starch yield tests that predict actual wet milling starch yield were used as a reference method for developing an extractable starch calibration on a NIRSystems Model 6500 spectrophotometer. A maize starch yield calibration was developed from 940 samples and used to predict a validation set of 304 samples. It had a standard error of prediction (SEP) of 1·06, a coefficient of determination r2 of 0·77 and a ratio of performance to deviations (rpd) of 2·1. This indicates about 95% of similar samples could have starch yield predicted by near-infrared reflectance within about±2·1%. The calibration should be successful in segregating maize lots for high and low starch yield percentages. 相似文献
2.
Degree of milling (DOM) of rice plays a key role in determining rice quality and value. Therefore, accurate, nondestructive, quick, and automated surface lipid content (SLC) measurement would be useful in a commercial milling environment. This study was undertaken to provide calibration models for commercial use to provide quick and accurate evaluation of milled rice SLC and Hunterlab color parameters (L,a,b) as indications of rice DOM. In all, 960 samples, including seven cultivars from seven southern United States locations, stored for 0, 1, 2, 3, and 6 months, were milled for four durations to obtain samples of varying DOM. The samples were used to develop calibration models of milled rice SLC and L,a,b values. Another sample set (n = 58) was commercially milled and used to validate the developed models. A DA 7200 diode array analyzer was used to scan milled rice samples in wavelength spectra of 950–1,650 nm. SLC and color parameters were measured using a Soxtec system and a HunterLab colorimeter, respectively. The partial least squares regression (PLS) method using the full near‐infrared spectra was used to develop prediction models for rice SLC and color parameters. Milled rice SLC was well fitted with a correlation of determination of predicted and measured values of (R2 = 0.934). Color parameters were also successfully fitted for L (R2 = 0.943), a (R2 = 0.870), and b (R2 = 0.855). Performance of the developed models to predict rice DOM was superior in predicting SLC and L,a,b values with R2 predicted and measured values of 0.958, 0.836, 0.924, and 0.661, respectively. 相似文献
3.
The degree of milling (DOM) of rice is a measure of how well the germ and bran layers are removed from the surface of rice kernels during milling. Because the majority of rice kernel lipids are found on the surface, measuring the surface lipid content (SLC) of rice after milling may be one way to quantify the DOM of rice. While there are several methods to measure the lipid content (LC) of rice, there is not an established standard method for determining the SLC of milled rice. The objective of this study was to evaluate the primary operating variables of a Soxtec apparatus in measuring the SLC of milled rice. This was accomplished by varying the preextraction drying, boiling, rinsing, and postextraction drying durations, as well as the solvent used for extraction, to achieve the maximum extraction of lipids from rice. Experiments were performed on stored Oryza sativa L. ‘Cypress’ and ‘Bengal’ rice milled for 10, 30, and 60 sec. Results showed that durations of 1 hr of preextraction, 20 min of boiling, 30 min of rinsing, and 30 min of postextraction drying provided the maximum lipid extraction from milled head rice with petroleum ether. Of the three solvents tested, petroleum ether, and ethyl ether yielded similar extraction results. 相似文献
4.
The effects of the degree of milling (based on surface lipids content [SLC]) on cooked rice physicochemical properties were investigated. Head rice yield (HRY), protein, and SLC decreased with increasing milling, while the percent of bran removed and whiteness increased. Results showed that SLC significantly (P < 0.05) affected milled as well as cooked rice properties across cultivar, moisture content (MC) at harvest, and location (Stuttgart, AR, and Essex, MO). Cooked rice firmness ranged from 90.12 to 111.26 N after milling to various degrees (SLC). The decrease in cooked rice firmness with increasing milling was attributed to the lowering of total proteins and SLC. Cooked rice water uptake increased with increasing degree of milling. Water uptake by the kernel during cooking dictated the cooked rice firmness. The increase in cooked rice stickiness with increasing degree of milling was attributed to an increase in starch leaching during cooking because of the greater starch granule swelling associated with a greater water uptake. 相似文献
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6.
A digital image analysis method was developed to quickly and accurately measure the degree of milling (DOM) of rice. The digital image analysis method was statistically compared to a chemical analysis method for evaluating DOM, which consisted of measuring the surface lipids concentration (SLC) of milled rice. The surface lipid area percentage (SLAP) obtained by the image analysis method and the SLC obtained by chemical analysis had a high coefficient of determination using a quadratic model (R2 = 0.9819) and using a logarithmic model (R2 = 0.9703). The quadratic model and the logarithmic model were validated using the test data set and it received high coefficients of determination (R2 = 0.9502 and R2 = 0.9459, respectively). 相似文献
7.
The authentication of rice (Korean domestic rice vs. foreign rice) has been attempted using near‐infrared spectroscopy (NIRS). Two sample sets (n1 = 280 and n2 = 200) were used to obtain calibration equations and the spectral regions used for this study were 500–600 nm, 700–900nm, and 980–2,498 nm. Modified partial least square (MPLS) regression was used to develop the prediction model. The standard error of cross validation (SECV) and the r2 were 0.165 and 0.91 respectively for 1st calibration set and 0.165 and 0.93 for 2nd calibration set respectively. The results of the independent validation (n3 = 80) showed that all of 80 samples were identified correctly. Even though authentication of rice was performed successfully using NIRS, the calibration statistics in this study showed that further effort is needed for implementation of NIRS for authentication of rice for industry purposes. 相似文献
8.
The development of genetically modified starches has relied on the use of maize (Zea mays L.) endosperm mutant alleles that alter starch structural and physical properties. A rapid method for predicting amylose content would benefit breeders and commercial handlers of specialty starch corn. For this reason, a study was conducted to investigate the use of near-infrared transmittance spectroscopy (NITS) as a rapid and nondestructive technique for predicting grain amylose content (GAC) in maize. Many single- and double-mutant inbreds and hybrids were used to create a calibration set for the development of a predictive model using partial least squares analysis. A validation set composed of similar genetic material was used to test the prediction model. A coefficient of correlation (r) of 0.94 was observed between GAC values determined colorimetrically and those predicted by NITS; however, the predicted values were associated with a large standard error of prediction (SEP = 3.5). Overall, NITS discriminated well among high amylose and waxy genotypes. The NITS calibration was used to determine levels of contamination by normal kernels in waxy and high-amylose (Amy VII) grain samples intended for wet milling. In both cases, a 5% contaminated sample could be detected from pure samples according to predicted NITS values. 相似文献
9.
Long-grain rice variety Kaybonnet was milled to three degree of milling (DOM) levels in two commercial milling systems (a single-break, friction milling system and a multibreak, abrasion and friction milling system) and separated into five thickness fractions. For both milling systems, the surface lipid content (SLC) and protein content of the milled rice varied significantly across kernel thickness fractions. SLC was influenced by DOM level more than by thickness, while the protein content was influenced by thickness more than by DOM level. Particularly at the low DOM levels, the thinnest kernel fraction (<1.49 mm) had higher SLC than the other kernel fractions. Protein content decreased with increasing kernel thickness to 1.69 mm, after which it remained constant. In both milling systems, thinner kernels were milled at a greater bran removal rate as indicated by SLC differences between the low and high DOM levels. For rice milled to a given DOM level, the multibreak system produced fewer brokens than did the single-break system. 相似文献
10.
The surface lipid content (SLC) of rice is often used to objectively measure the degree to which bran has been removed from rice kernels, commonly known as degree of milling (DOM). This study was conducted to evaluate new, rapid extraction technology for potential timesaving measurements of SLC of milled rice. The SLC of two long‐grain rice cultivars, Cypress and Drew, were determined using three extraction systems: Soxtec, accelerated solvent extraction (ASE), and supercritical fluid extraction (SFE). Before milling, rough rice was separated into three thickness fractions (<1.84, 1.84–1.98, and >1.98 mm) and samples from each thickness fraction were milled for durations of 10, 20, and 30 sec. Head rice collected from each milling duration was extracted using each of the three methods. Results showed that regardless of the extraction method, thinner kernels had lower SLC measurements than thicker fractions. In most cases, both the ASE and Soxtec produced SLC greater than that of the SFE. The ASE also showed SLC measurements at least as great as those from Soxtec extraction, suggesting that the ASE is as thorough in extracting lipids as commonly used methods. 相似文献
11.
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 (rval2), 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 rval2, 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. 相似文献
12.
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. 相似文献
13.
M. R. Campbell S. R. Mannis H. A. Port A. M. Zimmerman D. V. Glover 《Cereal Chemistry》1999,76(4):552-557
A study was conducted to investigate methods of improving a near-infrared transmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining amylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations ranging in starch-amylose content (SAC) from -8.5 to 76%, relative to a standard curve. The influence of three factors were examined including comparing calibrations made against SAC versus grain amylose content (GAC), developing calibrations using partial least squares (PLS) analysis versus artificial neural networking (ANN), and using all samples in the calibrations set versus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PLS analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard error of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all NITS models. The study revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did not improve performance statistics except for PLS models for SAC in which a decrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples with SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be useful when a rough screening method is needed for SAC. For example, NITS may be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples. 相似文献
14.
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. 相似文献
15.
Head rice yield (HRY) is the primary parameter used to quantify rice milling quality. However, HRY is affected by the degree of milling (DOM) and thus HRY may not be comparable between different lots if the DOM is different. The objective of this study was to develop a method by which HRY values can be adjusted for varying DOM values when measured by surface lipid content (SLC). Seventeen rough rice lots including long‐grain and medium‐grain cultivars and hybrids were harvested from two 2003 and five 2004 locations. Duplicate subsamples of each lot were milled in a McGill No. 2 laboratory mill for 10, 15, 20, or 40 sec after zero, one, two, three, and six months of storage. HRY and SLC were measured. The average HRY versus SLC slope across all milling duration data sets was 9.4. As such, it is suggested that, when milling with a McGill No. 2 laboratory mill, the HRY of a rice lot can be adjusted by a factor of 9.4 percentage points for every percentage point difference between the rice lot SLC and a specified SLC. 相似文献
16.
Hiroshi Okadome Hidechika Toyoshima Naoto Shimizu Keitaro Suzuki Ken'ichi Ohtsubo 《Cereal Chemistry》2005,82(4):414-419
The purpose of this study was to develop highly accurate regression models with texture parameters of cooked milled rice grains for predicting pasting properties in terms of quality index of rice flour. Two methods were adopted as the texture measurement to acquire predictors for the models. In the calibration set, all the multiple regression models by a single‐grain method exhibited a higher R2 than those by a three‐grain method. Each of the former models also showed a lower SEP and a higher RPD in the validation set. The prediction performance was best for consistency (RPD = 2.4). The single‐grain method was more advantageous for the pasting prediction. These results suggest that the models based on grain texture could predict rice flour quality. 相似文献
17.
The degree of similarity between rice milled in a McGill #2 laboratory mill and commercial milling processes was evaluated using eight physical, physicochemical, and end‐use properties. There was no statistical difference between the two milling systems with respect to color parameters L* and a*, final viscosity, texture, and end‐use cooking properties (α = 0.05). Overall, the kernel dimensions of length, width, and thickness were less in the McGill #2 laboratory‐milled rice than the same rice milled commercially. The incidence of bran streaks and peak viscosity values were each higher when the rice sample was milled commercially in 27, and 28, respectively, of the 29 samples by means comparison. The decrease in kernel dimensions and incidence of bran streaks were attributed to the more aggressive nature of the single‐pass, batch milling system of the McGill #2 laboratory mill as compared with multipass, continuous milling systems that are used commercially. Finally, as surface lipid content (SLC) decreased, L* increased and a*, b*, and the incidence of bran streaks decreased for both milling systems. 相似文献
18.
基于音频和近红外光谱融合技术的西瓜成熟度判别 总被引：3，自引：3，他引：0
为了满足西瓜成熟度的快速无损检测需求,该研究主要利用声学技术、近红外光谱技术结合K最近邻法(k-nearest neighbor,KNN)、线性判别分析(linear discriminant analysis,LDA)和反向传播人工神经网络(back propagation artificial neural network,BP-ANN)3种化学计量学方法对不同成熟度的西瓜进行定性判别;同时采用联合区间偏最小二乘筛选法(synergy interval partial least squares,Si-PLS)分别建立声学技术、近红外光谱技术、融合技术的西瓜可溶性固形物预测模型。结果表明融合技术处理结果均优于单一信号,其LDA模型数据的西瓜成熟度模型识别率较佳,校正集和预测集的识别率分别为100.00%和91.67%。同时,基于融合技术所建立的西瓜可溶性固形物预测模型效果较佳,其校正集的均方差根误差(root mean squared error of the calibration set,RMSECV)为0.601%,预测集的均方差误差(root mean squared error of the prediction set,RMSEP)为0.725%,相比的单独音频信号其均方根误差分别降低了0.081、0.068个百分点。研究结果可为高精度的西瓜品质快速鉴别提供参考。 相似文献
19.
农产品产地加工与储藏工程技术分类 总被引：1，自引：1，他引：0
生鲜牛肉的含水率对其牛肉的加工、储藏、贸易与食用质量有重要影响,为了提高牛肉的经济价值和食用品质,需要研究牛肉含水率的无损检测技术。以取自不同超市的内蒙小黄牛和鲁西黄牛背最长肌为研究对象,有效样本86个,其中,75%的样本作为校正集,25%的样本作为验证集。采集牛肉新鲜切口处400～1170 nm波长范围内的漫反射光谱,用国标方法测定牛肉含水率。经过多元散射校正（multiplicative scatter correction, MSC）、变量标准化（standard normalized variate, SNV）和直接正交信号校正（direct orthogonal signal correction, DOSC）等方法预处理,在400～1170 nm范围内分别建立多元线性回归（multiple linear regression, MLR）模型、主成分回归（principal component Regression, PCR）模型和偏最小二乘回归（partial least squares regression, PLSR）模型。结果表明使用MSC预处理方法建立的模型预测效果最佳,其中用PLSR建模结果最好,校正集的相关系数和校正标准差分别是0.92和0.0069,验证集的相关系数和验证标准差分别是0.92和0.0047,外部验证的相关系数和验证标准差分别是0.85和0.0054。结果表明,可见/近红外光谱结合MSC预处理方法建立的PLSR模型,可以对牛肉含水率进行准确的快速无损评价,为生鲜牛肉含水率快速无损检测技术的应用提供理论参考。 相似文献
20.
Rapid prediction of acid detergent fiber, neutral detergent fiber, and acid detergent lignin of rice materials by near-infrared spectroscopy 总被引：3，自引：0，他引：3
A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs. 相似文献