The application of near infrared reflectance analysis to rapid flour testing

Calibrations are presented for the analysis of flour for protein, moisture, particle size, colour and starch damage by means of near infrared (NIR) reflectance analysis. A commonly used NIR instrument was subjected to a thorough evaluation for its accuracy in flour testing for these parameters, by comparison of NIR with existing methods of analysis over an extensive period of time. The accuracy and precision of NIR for the parameters studied were satisfactory for quality control purposes and therefore the technique has an important place in the rapid flour testing.     

Near Infra-red Monitoring of Enzymatic Hydrolysis of Starch

NIR measurement was carried out for a starch dispersion circulating from a reactor to an NIR flow-through cell over a wavelength range of from 1000 to 2500 nm in 2 nm increments. Multiple regression analysis between NIR and HPLC shows a good correlation. It is shown that the NIR method can be used to determine glucose quantitatively in real time during enzymatic hydrolysis, and that NIR may be employed for controlling the reaction in a bioreactor.     

Prediction of sweetpotato starch physiochemical quality and pasting properties using near-infrared reflectance spectroscopy

A rapid predictive method based on near-infrared reflectance (NIR) spectroscopy (NIRS) was developed to measure sweetpotato starch physiochemical quality and pasting properties. The starch samples were scanned by NIRS and analyzed for quality properties by reference methods, respectively. Results of statistical modeling indicated that NIRS was reasonably accurate in predicting amylose content (AC), amylose percent (AP), total starch content (TSC), protein content (PRC), phosphorus content (PHC), solubility (SOL), swelling power (SP), average granule diameter (AGD), big granule percent (BGP), small granule percent (SGP), crystallinity (CRY), peak viscosity (PKV), hot paste viscosity (HPV), setback (SB), and pasting temperature (P_temp) with high coefficients of determination (RSQ = 0.85–0.92) and relatively low standard errors of prediction. The results showed that NIR analysis was sufficiently accurate and effective for rapid evaluation of starch physicochemical properties in sweetpotato. The NIR-based protocol developed in this study can be used for screening large number of starch samples in food enterprises and sweetpotato breeding programs.     

AREAS OF ABSORPTION RELATING TO MALT EXTRACT VALUE IN MODIFIED NEAR INFRA-RED SPECTRA OF BARLEY FLOUR

Determinations of milling energy, nitrogen, acid-soluble beta glucan and malt extract after micromalting, were made on grain samples of twenty nine barley cultivars. In addition the flour from unmalted grains was scanned over the near infra-red spectrum, and the scan data were subjected to a principal components analysis (PC). Predicted malt extract values resulting from an NIR multiple regression equation with PC terms, correlated well (r = 0.934) with the manual extract values. This confirms previous evidence that malt extract value depends largely on constituents in the resting grain, rather than on malt enzymes. An attempt to locate NIR absorptions relating to malting quality was made by restructuring the NIR spectrum of the samples according to the weightings modified by the regression coefficients in the PC regression equation for hot water extract value. The spectrum reconstructed in this way showed a number of absorption peaks and troughs. From comparison with previous NIR scans it was concluded that a strong peak at the wavelength 2100 nm was due to a starch absorption and this together with minor starch overtone peaks correlated positively with malt extract. Troughs at 2180 nm, 1980 nm and 1700 nm indicated a negative association between malt extract and some proteins. There were also wide troughs at 1830 and 2330 nm indicating a negative relationship between malt extract and beta glucan. Other peaks and troughs in the reconstructed spectrum could not readily be assigned to a constituent. A reconstructed protein spectrum consisted of peaks and troughs that agreed with previous assignations for protein absorbancies. A milling energy reconstruction was approximately the inverse of the malt extract spectrum, and an acid-soluble beta glucan reconstruction was relatively featureless and appeared to be due to the effects of particle size variations.     

Bread Quality of Wheat Flour by Near-Infrared Spectrophotometry: Feasibility of Modeling

Commercial bakeries need a means to measure the dough-handling and bread-making functionalitv of wheat flour during processing. The feasibility of using near-infrared (NIR) reflectance spectrophotometry for such measurement was examined. Flour from hard red spring and hard red winter wheat were tested for: water absorption, dough mixing time, dough mixing tolerance, loaf height, internal grain appearance, and overall bake score. The ability of NIR to measure protein level, and/ or starch damage, as well as positive relationships between the two and water absorption, probably made it successful for modeling water absorption. Models for the remaining five indices were less accurate due to the complexity of interactions between protein, starch, and lipids, and inadequate instrument sensitivity.     

The Application of Near Infrared Spectroscopy to Evaluate Malting Quality

Near infrared (NIR) spectroscopy is a rapid technique which is used within plant breeding programs for the analysis of many grain traits. This study investigated the application of NIR analysis of wort samples to select malting quality cultivars in a barley breeding program. An automatic sampling and data capture system was developed which consisted of a Perten filter NIR instrument with a flow through cell module operating in transflectance mode interfaced with a computer, peristaltic pump and sample changer. Calibrations for hot water extract, free alpha-amino nitrogen and soluble protein were developed using multiple linear regression analysis based on four wavelength terms for each trait. The correlation coefficients for both calibration and prediction data sets were highly significant (P<0.01) and the standard error of prediction was similar to that obtained by standard methods. Cultivars with known malting quality were included in the experiments and their ranking by NIR was consistent with the standard methods. The reported calibrations have been used for over four years to screen early generation breeding lines for malting quality.     

Application of near infrared spectroscopy to peanut grading and quality analysis: overview

Techniques using near infrared (NIR) spectroscopy for quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method and provides more information about the components and its structure present in the food products. It can measure more than one parameter simultaneously. The NIR spectrum includes wavelengths from 750 to 3000 nm that follow immediately after the visible region (400–700 nm). Many organic compounds can be well-defined by NIR reflectance, transmittance or diffuse reflectance system. This paper reviews the application of NIR spectroscopy to several oil seeds and examines the feasibility of using this technique for peanut quality analysis. The NIR spectroscopic instrumentation has been explained briefly for a better understanding. Also needs and limitations in use of NIR spectroscopy for peanut quality analysis and grading were explained.     

Rapid determination of sugar level in snack products using infrared spectroscopy

Real-time spectroscopic methods can provide a valuable window into food manufacturing to permit optimization of production rate, quality and safety. There is a need for cutting edge sensor technology directed at improving efficiency, throughput and reliability of critical processes. The aim of the research was to evaluate the feasibility of infrared systems combined with chemometric analysis to develop rapid methods for determination of sugars in cereal products. Samples were ground and spectra were collected using a mid-infrared (MIR) spectrometer equipped with a triple-bounce ZnSe MIRacle attenuated total reflectance accessory or Fourier transform near infrared (NIR) system equipped with a diffuse reflection-integrating sphere. Sugar contents were determined using a reference HPLC method. Partial least squares regression (PLSR) was used to create cross-validated calibration models. The predictability of the models was evaluated on an independent set of samples and compared with reference techniques. MIR and NIR spectra showed characteristic absorption bands for sugars, and generated excellent PLSR models (sucrose: SEP < 1.7% and r > 0.96). Multivariate models accurately and precisely predicted sugar level in snacks allowing for rapid analysis. This simple technique allows for reliable prediction of quality parameters, and automation enabling food manufacturers for early corrective actions that will ultimately save time and money while establishing a uniform quality. Practical Application: The U.S. snack food industry generates billions of dollars in revenue each year and vibrational spectroscopic methods combined with pattern recognition analysis could permit optimization of production rate, quality, and safety of many food products. This research showed that infrared spectroscopy is a powerful technique for near real-time (approximately 1 min) assessment of sugar content in various cereal products.     

Supervised Multivariate Analysis of Hyper-spectral NIR Images to Evaluate the Starch Index of Apples

Fruit maturity indexes are crucial in harvest time determination and commercial context. The harvest time of apples, matching the desired commercial characteristics, is assessed through starch–iodine test in practice. Fruit halves are dipped into iodine solution and patterns are visually evaluated by experts comparing them to reference charts. Aim of the work was to study the relationships of near infrared (NIR) spectral images (1,000–1,700 nm), starch/starch-free patterns visually assessed and RGB color images. Spectral images of 88 Golden Delicious Klon B apples were sampled at seven different maturity stages. Partial least-squares discriminant analysis (PLSDA) technique was used on hyper-spectral NIR images to classify single pixels using its NIR reflectance spectrum. The response variable (i.e. the classification for each pixel) was identified through the matching of single pixel obtained with the color images, segmented in two classes (starch and starch-free), and the NIR hyper-spectral matrix. Mean hyper-spectral classification obtained through PLSDA modeling on individual apple correctly classified 80.81% of the total pixels, while the unique model, i.e. a single model including all the fruits, resulted in 66.33%. In the latter case, the relationship with the RGB classification showed high values (Pearson correlation coefficient r = 0.95). The present work shows the feasibility of NIR imaging spectroscopy as a tool for apple fruit maturity determination, avoiding expert’s subjective interpretation by traditional starch index assignments.     

波谱技术在香蕉淀粉及抗性淀粉研究中的应用

淀粉是由葡萄糖分子聚合而形成的天然高分子化合物,不同来源的淀粉其结构也不尽相同。波谱技术具有检测灵敏度和准确度高、分析特征性强、非破坏性等特性,因此在研究淀粉结构方面得到广泛应用。目前利用波谱分析技术对抗性淀粉结构和性质等方面的研究鲜有综述报道,因此本文以富含抗性淀粉的香蕉为对象,概述了紫外-可见吸收光谱、红外吸收光谱、核磁共振、质谱以及X-射线衍射这些波谱技术的应用情况。首先对这些波谱技术的基本原理和应用特点进行了归纳,其次阐述了其在香蕉淀粉及抗性淀粉的直链淀粉含量、分子结构、晶型、结晶度等方面的应用。期望为应用波谱技术研究淀粉及抗性淀粉提供参考。     

Theory and application of near infrared spectroscopy in assessment of fruit quality: a review

Fruits provide nutrients for human body and are able to prevent sorts of non-communicable diseases. The fruit quality test is an area that both technology and market section concern about. Near infrared spectroscopy (NIR) is a rapid, precise, and non-destructive technique which can be well utilized in determination of fruit quality. This review paper summarizes the theory of NIR analysis, and the fundamental structure of instruments based on NIR for fruit quality assessment. Chemometrics for NIR spectroscopy involving analysis methods of data pre-processing, calibration, model transfer and evaluation, is also included. In recent 11 years, significant progresses were achieved in fruit quality assessment via NIR spectroscopy, which is the main focus in this review. Furthermore, urgent problems in this research field are discussed, expecting to be solved in the near future.     

Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R ²) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.     

短波近红外光谱结合ν-SVM法快速无损鉴别淀粉种类

选用不同厂家的红薯淀粉、马铃薯淀粉和玉米淀粉共112个样品,利用短波近红外光谱技术对淀粉种类进行鉴别。分别采用马氏距离判别法、C-支持向量机(C-SVM)、ν-支持向量机(ν-SVM)建立淀粉种类鉴别的短波近红外光谱模型;并对比多元散射矫正、平滑、一阶微分、二阶微分等多种预处理方法后的建模结果。结果表明:同时使用平滑、多元散射矫正、一阶微分3种预处理方法后,ν-SVM分类模型的效果最佳;训练集交叉验证正确率为100%,测试集正确率也达到100%。该模型快速准确无损的鉴别淀粉种类是可行的。     

Rapid Estimation of Potato Tuber Quality by Near‐Infrared Spectroscopy

Starch and protein parameters of potato tubers were estimated by near‐infrared (NIR) spectroscopy measurements of the crude potato mash. Calibration was carried out with a condensed data set of original 1481 individual samples from a varying number of varieties and breeding lines, grown at eight locations over a three‐years period. Validation of the models was performed with an independent data set (n = 133). Starch content of potato tubers was determined with the official under‐water weighting procedure of the EU, and could be predicted with 90% confidence. The NIR model of phosphorus content of starch had a prediction confidence of 53%. Total protein content could be predicted, too (62% confidence), whereas the amount of coagulable protein was not predictable (R² = 0.25). Despite the different qualification levels of the models, guiding to concrete prediction tools or to a very rough estimation graduating from “low” to “high”, the NIR technique enables potato starch processing plants to optimise both potato starch quality and processing efficiency.     

Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals

Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK‐NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK‐NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost‐effective technologies for high‐speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK‐NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK‐NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain. © 2013 Society of Chemical Industry     

近红外漫反射光谱法快速检测谷子蛋白质和淀粉含量

建立近红外漫反射光谱法测定谷子中的蛋白质和淀粉含量,提供一种快速、简便、无损的分析方法进行谷子品种资源鉴定和筛选。以191份山西核心谷子种质为材料,采用近红外漫反射光谱法建立谷子蛋白质和淀粉含量的快速检测模型。结果表明,采用一阶导数+矢量归一化光谱预处理,分别建立谷子蛋白质和淀粉含量的校正模型,模型的校正决定系数(R~2_(cal))分别为0.977 0和0.907 3,交叉验证均方根误差分别为0.203%和0.466%,外部验证决定系数(R~_(2val))分别为0.989 6和0.977 2,预测均方根误差分别为0.225%和0.368%。对于谷子蛋白质和淀粉的预测,化学法和近红外仪器法测定间无显著差异,近红外测定结果是准确可靠的。说明采用近红外漫反射光谱分析技术能够满足对谷子蛋白质和淀粉含量的检测。     

Rapid analysis of inner and outer bark composition of Southern Yellow Pine bark from industrial sources

Differences in bark chemistry between inner and outer bark are well known and may affect the suitability of various bark supplies for a particular application. Accordingly, there is a need for quality control protocols to assess variability and predict product yields. Southern yellow pine bark samples from two industrial sources were separated into inner and outer bark tissues and used to prepare both predetermined and random bark compositions for analysis. Near infrared (NIR) spectroscopy, coupled with multivariate analysis, was successfully used to develop models to predict relative amounts of inner bark. Application of mathematical treatments to the NIR data improved the calibration performance leading to improved predictions for the test samples. Results presented here show promise for the further development of this technique as a means to provide rapid and accurate predictions of the quality of bark obtainable from industrial sources.     

Investigations of Native and Resistant Starches and Their Mixtures Using Near-Infrared Spectroscopy

Resistant starches (RS) play important roles in our nutrition; therefore, the investigation of these starches is notably important. In our study, two native starches (maize and wheat) and two resistant starches (Hi-maize™260, high amylose maize starch as RS2 and Fibersym™70, phosphorylated wheat starch as RS4) were investigated as is and in their physical mixtures (samples containing 20%, 40%, 60% and 80% RS) using near-infrared (NIR) spectroscopy. The aim of our study was to examine the spectra of resistant starches and to differentiate the resistant starch components in different ratios by NIR spectroscopy. The differences of samples were presented in two characteristic absorption bands for carbohydrate: carbohydrate II (2,080–2,130 nm) and carbohydrate III (2,275–2,290 nm) regions. Additionally, principal component analysis (PCA) for all samples was carried out. It was shown that the increasing amount of amylose can be sensitively followed up in carbohydrate II region. The phosphorylated RS4 is not so characteristic probably due to the reduced mobility of amorphous chains; however, the RS4 addition can be observed. Additionally, it was proven that the carbohydrate III region is sensitive for the changes of amylose–amylopectin ratio as well. The wheat-based RS4 addition causes linear changes in maize-based mixtures; thus the botanical origin is determining in this region. The global PCA analysis justified that the RS2 addition can be sensitively followed up independent on the medium; however, the increasing amount of RS4 cannot be detected in the PCA plot. The loading spectra of PC1 component attribute great significance to the carbohydrate III region.     

Prediction of Rice Starch Quality Parameters by Near-Infrared Reflectance Spectroscopy

ABSTRACT: A rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP] = 1.39 percentage units, coefficient of determination [R²] = 0.91); pasting parameters of setback (SB) (SEP = 13.6 RVU, R²= 0.92), and breakdown (BD) (SEP = 10.2 RVU, R²= 0.88); and gelatinization peak temperature (T_p) (SEP = 1.33 °C, R²= 0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (T_o), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.     

近红外光谱分析技术在动物源性食品检测中的应用进展

近年来动物源性食品的消费量日益增大,关于动物源性食品的质量安全也受到人们的高度重视。近红外光谱(NIR)分析技术具有分析速度快、非破坏性、所需样品量小、多组分同时测定,且可对生产过程进行实时监控等优点。本文对NIR分析技术进行了简介,并列举了常用的光谱预处理方法;综述了该技术在动物源性食品检测中的研究进展,包括品质评价和安全评价;并对该技术在动物源性食品检测中的发展趋势进行了阐述。表明NIR分析技术适合评价动物源性食品的品质和安全。