Near‐Infrared Spectroscopy for Determination of Protein and Amylose in Rice Flour Through Use of Derivatives

The use of the derivative method for near‐infrared (NIR) calibration was investigated to determine protein and amylose content in rice flour. Samples for two years, 1996 and 1999, were combined to give a wide range of the constituents for development of the calibration model. The NIR spectral data were transformed with Savitzky‐Golay derivative with multiplicative scatter correction. To develop the best derivative models, the polynomial fits (quadratic, cubic, and quartic), convolution intervals (3–11 points for protein, 3–17 points for amylose), and derivative orders (1st derivative D1; 2nd derivative D2) were investigated. For the protein analysis, all polynomial fits with 3–11 points were acceptable to develop both the D1 and D2 models. However, the three‐point quadratic and five‐point quartic fits were not acceptable for the D1 model, and the three‐point quadratic fit was not acceptable for D2. For the amylose analysis, the D1 model produced generally better results than D2. Higher convolution intervals were required for the D2 model, whereas the D1 model was not affected by convolution intervals. A quadratic (or cubic) fit with 17‐point convolution interval was acceptable for the amylose D2 model, and the quadratic fit with 5–11 points and cubic (or quartic) fit with 7–17 points were suitable for the D1 model. Based on the standard error of cross‐validation (SECV), the calibration models developed using data for two years resulted in good precision with an SECV of 0.23% for protein using four factors and an SECV of 1.0% for amylose using 10 factors.     

Protein and Apparent Amylose Contents of Milled Rice by NIR‐FT/Raman Spectroscopy

The chemometric calibration of near‐infrared Fourier‐transform Raman (NIR‐FT/Raman) spectroscopy was investigated for the purpose of providing a rigorous spectroscopic technique to analyze rice flour for protein and apparent amylose content. Ninety rice samples from a 1996 collection of short, medium, and long grain rice grown in four states of the United States, as well as Taiwan, Korea, and Australia were investigated. Milled rice flour samples were scanned in rotating cups with a 1,064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a liquid N₂ cooled Ge detector over the Raman shift range of 175–3,600 cm^‐1. The spectral data was preprocessed using baseline correction with and without derivatives or with derivatives alone and normalization. Nearly equivalent results were obtained using all of the preprocessing methods with partial least squares (PLS) models. However, models using baseline correction and normalization of the entire spectrum, without derivatives, showed slightly better performance based on the criteria of highest r² and the lowest SEP with low bias. Calibration samples (n = 57) and validation samples (n = 33) were chosen to have similar respective distributions for protein and apparent amylose. The best model for protein was obtained using six factors giving r² = 0.992, SEP = 0.138%, and bias = ‐0.009%. The best model for apparent amylose was obtained using eight factors giving r² = 0.985, SEP = 1.05%, and bias = ‐0.006%.     

Estimation of the Deoxynivalenol and Moisture Contents of Bulk Wheat Grain Samples by FT‐NIR Spectroscopy

Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near‐infrared (FT‐NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000–4,000 cm⁻¹ (1,000–2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT‐IR/FT‐NIR spectrometer. The DON calibration predicted the DON levels in test samples with R ² = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5–25 ppm of DON was used as the cut‐off to classify samples into low‐ and high‐DON groups, 60.8–82.3% of the low‐DON samples were correctly classified, whereas the classification accuracy of the high‐DON group was 82.3–94.0%. The MC calibration predicted the MC in grain samples with R ² = 0.98 and RMSEP = 0.19%. Therefore, these FT‐NIR calibrations can be used to rapidly prescreen wheat lines to identify low‐DON lines for further evaluation using standard laboratory methods, thereby reducing the time and costs of analyzing samples from FHB screening trials.     

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.     

NIR‐FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

The classification of cereals using near‐infrared Fourier transform Raman (NIR‐FT/Raman) spectroscopy was accomplished. Cereal‐based food samples (n = 120) were utilized in the study. Ground samples were scanned in low‐iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN₂) detector over the Raman shift range of 202.45~3399.89 cm^‐1. Samples were classified based on their primary nutritional components (total dietary fiber [TDF], fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components in cereal. The Raman shifts that were most responsible for classification of each component were 1600~1630 cm^‐1 for TDF, 1440 and 2853 cm^‐1 for fat, 2910 and 1660 cm^‐1 for protein, and 401 and 848 cm^‐1 for sugar. The use of the selected spectral region (frequency region) for each component produced better results than the use of the entire region in both SIMCA and PLS‐based classifications. PLS‐based classification performed better than SIMCA for all four components, resulting in correct classification of samples 85~95% of the time. NIR‐FT/Raman spectroscopy represents a rapid and reliable method by which to classify cereal foods based on their nutritional components.     

Chemometric Localization Approach to NIR Measurement of Apparent Amylose Content of Ground Wheat

The development of new wheat cultivars that target specific end‐uses, such as low or zero amylose contents of partially waxy and waxy wheats, has become a modern focus of wheat breeding. But for efficient and cost‐effective breeding, inexpensive and high‐throughput quality testing procedures, such as near infrared (NIR) spectroscopy, are required. The genetic nature of a set of wheat lines, which included waxy to nonwaxy cultivars, results in a bimodal distribution of amylose contents that presents some special challenges for the formulation of stable NIR calibrations for this property. The obvious and intuitive solution lies in the use of some form of localization procedure and we explored three localization algorithms in comparison with the default partial least squares. Localization with respect to the waxy (zero amylose) cultivars resulted in a modified partial least squares calibration with a standard error of prediction of 0.16%. The results establish unambiguously that there are advantages in performing a suitable localization to achieve a reliable NIR calibration and prediction. The accuracy of the method can also be enhanced by application of an appropriate resampling strategy. In addition, there are advantages in performing a suitable localization to achieve a reliable NIR calibration‐prediction. It resolves the issue of how to utilize the bimodal distribution of apparent amylose values. The best results are obtained when the localization is performed simultaneously with respect to the sample property under investigation and the NIR spectra. The key problem with the measurement of amylose is the laboratory reference method which, in reality, only measures the apparent amylose content of the wheat. As a direct consequence, the measurements of amylose have such a large error that traditional calibration‐prediction procedures generate unacceptable results. To resolve this difficulty, a statistically based resampling strategy is proposed as a method of identifying samples where there is a large error in the reference measurement.     

基于近红外光谱技术的淡水鱼品种快速鉴别

为探索淡水鱼品种的快速鉴别方法,该文应用近红外光谱分析技术,结合化学计量学方法,对7种淡水鱼品种的判别分类进行了研究。采集了青、草、鲢、鳙、鲤、鲫、鲂等7种淡水鱼,共665个鱼肉样品的近红外光谱数据,经过多元散射校正(multiplicative scatter correction,MSC)、正交信号校正(orthogonal signal correction,OSC)、数据标准化(standardization,S)等20种方法预处理,在1 000~1 799 nm范围内分别采用偏最小二乘法(partial least square,PLS)、主成分分析(principal component analysis,PCA)和BP人工神经网络技术(back propagation artificial neural network,BP-ANN)、偏最小二乘法和BP人工神经网络技术对7种淡水鱼原始光谱数据进行了鉴别分析。结果表明,近红外光谱数据,结合主成分分析和BP人工神经网络技术建立的淡水鱼品种鉴别模型最优,模型的鉴别准确率达96.4%,对未知样本的鉴别准确率达95.5%。模型具有较好的鉴别能力,采用该方法能较为准确、快速地鉴别出淡水鱼的品种。     

Selecting and Sorting Waxy Wheat Kernels Using Near‐Infrared Spectroscopy

An automated single kernel near‐infrared (NIR) sorting system was used to separate single wheat (Triticum aestivum L.) kernels with amylose‐free (waxy) starch from reduced‐amylose (partial waxy) or wild‐type wheat kernels. Waxy kernels of hexaploid wheat are null for the granule‐bound starch synthase alleles at all three Wx gene loci; partial waxy kernels have at least one null and one functional allele. Wild‐type kernels have three functional alleles. Our results demonstrate that automated single kernel NIR technology can be used to select waxy kernels from segregating breeding lines or to purify advanced breeding lines for the low‐amylose kernel trait. Calibrations based on either amylose content or the waxy trait performed similarly. Also, a calibration developed using the amylose content of waxy, partial waxy, and wild‐type durum (T. turgidum L. var durum) wheat enabled adequate sorting for hard red winter and hard red spring wheat with no modifications. Regression coefficients indicated that absorption by starch in the NIR region contributed to the classification models. Single kernel NIR technology offers significant benefits to breeding programs that are developing wheat with amylose‐free starches.     

Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms

The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R²) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R² and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.     

Near-infrared analysis of soluble and insoluble dietary fiber fractions of cereal food products

The use of near-infrared (NIR) reflectance spectroscopy for the rapid and accurate measurement of soluble and insoluble dietary fiber was explored in a diverse group of cereal products. Ground samples were analyzed for soluble and insoluble dietary fiber (AOAC Method 991.43) and scanned (NIRSystems 6500 monochromator) to obtain NIR spectra. Modified PLS models were developed to predict insoluble and soluble dietary fiber using data sets expanded to include products with high fat and high sugar contents. The models predicted insoluble dietary fiber accurately with an SECV of 1.54% and an R(2) of 0.98 (AOAC determined range of 0-48.77%) and soluble dietary fiber less accurately with an SECV of 1.15% and an R(2) of 0.82 (AOAC determined range of 0-13.84%). Prediction of independent validation samples by the soluble fiber model resulted in a bias that may be related to the way the reference method treats samples with different soluble fiber constituents. The insoluble fiber model can be used to rapidly monitor insoluble dietary fiber in cereal products for nutrition labeling.     

Potential Use of Raman Spectroscopy for Determination of Amylose Content in Maize Starch

An analytical method using Raman spectroscopy was developed for the determination of amylose concentration in maize starches. FT-Raman spectra of four maize starches with amylose content varying from 3.3 to 66% were obtained. A Raman band at ≈1657 cm^-1 correlated linearly with amylose concentration in the four maize starches, and a calibration curve for Raman band intensity versus amylose content was developed. The linear correlation of the I₁₆₅₇/I₉₀₀ integrated areas with amylose content was r = 0.997. The Raman-based calibration curve allows fast and nondestructive determination of the amylose content in maize starches with minimal sample preparation.     

PH—Postharvest Technology: Comparison of Fourier Transform and Dispersive Near-Infrared Reflectance Spectroscopy for Apple Quality Measurements

The large amount of publications about quality measurements of horticultural products with near-infrared (NIR) reflectance spectroscopy demonstrates its usefulness for measuring their internal composition in a non-destructive way. Up to now, dispersive instruments have been used exclusively to yield satisfactory calibration models. In this study, Fourier transform (FT) NIR spectroscopy has been compared to dispersive NIR spectroscopy. The instrument stability, the light penetration depth and the predictive capacity of some quality characteristics between both instruments were compared. The FT spectrophotometer achieved a higher signal-to-noise ratio. The light penetration depth in a Jonagold apple differed between the two instruments: the FT spectrophotometer established a lower penetration depth. Both instruments were able to measure the quality characteristics (soluble solids content, firmness and titratable acidity) with comparable accuracy. Based on the results, it was concluded that FT–NIR reflectance spectroscopy is an interesting alternative for standard dispersive instruments for the non-destructive quality evaluation of apples.     

Quantitative Analysis of Benzyl Modification in Waxy Maize Starch by Fourier Transform (FT) Raman Spectroscopy

Waxy maize starch was chemically modified to varying benzylation levels and the degree of benzylation substitution was measured using a nuclear magnetic resonance (NMR) method. Fourier Transform (FT) Raman spectra of the chemically modified starches were acquired and aromatic C=C stretch Raman bands characteristic of the benzylation modification were used to derive a calibration curve for the Raman intensity of these marker bands versus the degree of benzylation substitution. The best‐fit linear regression to the plotted data gave a linear correlation coefficient of 0.997. The FT‐Raman technique provides a fast, nondestructive method for the measurement of the degree of benzylation substitution of modified waxy maize starches and should be applicable for use with benzylated starches from other botanical sources.     

Environmental Effects on Developing Wheat as Sensed by Near‐Infrared Reflectance of Mature Grains

For 30 years, near‐infrared (NIR) spectroscopy has routinely been applied to the cereal grains for the purpose of rapidly measuring concentrations of constituents such as protein and moisture. The research described herein examined the ability of NIR reflectance spectroscopy on harvested wheat to determine weather‐related, quality‐determining properties that occurred during plant development. Twenty commercial cultivars or advanced breeding lines of hard red winter and hard white wheat (Triticum aestivum L.) were grown in 10 geographical locations under prevailing natural conditions of the U.S. Great Plains. Diffuse reflectance spectra (1,100–2,498 nm) of ground wheat from these samples were modeled by partial least squares one (PLS1) and multiple linear regression algorithms for the following properties: SDS sedimentation volume, amount of time during grain fill in which the temperature or relative humidity exceeded or was less than a threshold level (i.e., >30, >32, >35, <24°C; >80%, <40% rh). Rainfall values associated with four pre‐ and post‐planting stages also were examined heuristically by PLS2 analysis. Partial correlation analysis was used to statistically remove the contribution of protein content from the quantitative NIR models. PLS1 models of 9–11 factors on scatter‐corrected and (second order) derivatized spectra produced models whose dimensionless error (RPD, ratio of standard deviation of the property in a test set to the model standard error for that property) ranged from 2.0 to 3.3. Multiple linear regression models, involving the sum of four second‐derivative terms with coefficients, produced models of slightly higher error compared with PLS models. For both modeling approaches, partial correlation analysis demonstrated that model success extends beyond an intercorrelation between property and protein content, a constituent that is well‐modeled by NIR spectroscopy. With refinement, these types of NIR models may have the potential to provide grain handlers, millers, and bakers a tool for identifying the cultural environment under which the purchased grain was produced.     

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.     

Rapid Variety Identification of Pure Rough Rice by Fourier‐Transform Near‐Infrared Spectroscopy

Rice variety is considered as an important factor influencing cooking and processing quality because of variations in size, shape, and constitution. Difficulty in management of rough rice with lower varietal purity becomes a significant problem in rice production and can result in the reduction of rice quality. Fourier‐transform near‐infrared (FT‐NIR) spectroscopy was used to identify the variety of rough rice through whole‐grain techniques. Moist rough rice samples (n = 259) comprising five varieties (Khao Dawk Mali 105 [KDML105], Pathum Thani 1, Suphan Buri 60, Chainat 1, and Pitsanulok 2) were gathered from different locations around Thailand and scanned in the NIR region of 9088–4000 cm^–1 in reflectance mode. Soft independent modeling of class analogies (SIMCA) and partial least squares discriminant analysis (PLSDA) methods were used for identification by utilizing preprocessed spectra. The highest identification accuracy achieved was 74.42% by the SIMCA model and 99.22% by the PLSDA model. The best PLSDA model demonstrated approximately 97% correct identification for KDML105 samples and 100% for the others. This study raises the possibility of applying FT‐NIR spectroscopy as a nondestructive technique for rapidly identifying moist rough rice varieties in routine quality assurance testing.     

Determination of Rapid Visco Analyser Parameters in Rice by Near‐Infrared Spectroscopy

The objective of these studies was to find alternative Rapid Visco Analyser (RVA) viscoelastic parameters that are predictable by near‐infrared spectroscopy (NIRS). Currently, RVA instruments are widely used in assessing cooking and processing characteristics in rice. The ability to predict RVA parameters by NIRS would be useful in rapidly determining rice pasting qualities, but NIRS does not correlate with the traditional parameters (peak viscosity, final viscosity, breakdown, consistency, and setback). Alternative RVA parameters were sought by collecting RVA and NIRS data for a total of 86 short, medium, and long grain rice cultivars. The amylose contents were 0.41–24.90% (w/w) and protein concentrations were 8.47–11.35% (w/w). Partial least squares (PLS) regression models generated for the entire NIR spectrum against the RVA curve showed viscosity at 212–228 sec (80°C ± 1) varied linearly with NIR spectra (1,100 to ‐2,500 nm). Regression coefficient values were R = 0.961 for 212 sec and R = 0.903 for 228 sec. The PLS correlation coefficient for the prediction of amylose at 212–228 sec decreases along with the NIRS correlation to the same time frame. An opposite trend was observed for the correlation with protein at 212–228 sec. This comparison suggests the importance of amylose and protein in water absorption during this time frame.     

An Automated Near‐Infrared System for Selecting Individual Kernels Based on Specific Quality Characteristics

An automated sorting system was developed that nondestructively measured quality characteristics of individual kernels using near‐infrared (NIR) spectra. This single‐kernel NIR system was applied to sorting wheat (Triticum aestivum L.) kernels by protein content and hardness, and proso millet (Panicum miliaceum L.) into amylose‐bearing and amylose‐free fractions. Single wheat kernels with high protein content could be sorted from pure lines so that the high‐protein content portion was 3.1 percentage points higher than the portion with the low‐protein kernels. Likewise, single wheat kernels with specific hardness indices could be removed from pure lines such that the hardness index in the sorted samples was 29.4 hardness units higher than the soft kernels. The system was able to increase the waxy, or amylose‐free, millet kernels in segregating samples from 94% in the unsorted samples to 98% in the sorted samples. The portion of waxy millet kernels in segregating samples was increased from 32% in the unsorted samples to 55% after sorting. Thus, this technology can be used to enrich the desirable class within segregating populations in breeding programs, to increase the purity of heterogeneous advanced or released lines, or to measure the distribution of quality within samples during the marketing process.     

Classification of wine and alcohol vinegar samples based on near-infrared spectroscopy. Feasibility study on the detection of adulterated vinegar samples

Near-infrared (NIR) spectroscopy was used to discriminate between wine vinegar (red or white) and alcohol vinegar. One orthogonal signal correction method (OSC) was applied on a set of 73 vinegar NIR spectra from both origins and artificial blends made in the laboratory in order to remove information unrelated to a specific chemical response (tartaric acid), which was selected due to its high discriminant ability to differentiate between wine vinegar and alcohol vinegar samples. These corrected NIR spectra, as well as raw NIR spectra and 14 physicochemical variables, were used to develop separate classification models using the potential functions method as a class-modeling technique. The aforementioned models were compared to evaluate the suitability of NIR spectroscopy as a rapid method for discriminating between vinegar origins. The transformation of vinegar NIR spectra by means of an orthogonal signal correction method prompted a notable improvement in the specificity of the constructed classification models. The classification model developed was then applied to artificial vinegar blends made in the laboratory to test its capacity to recognize adulterated vinegar samples.