Measurement of fat and sucrose in dry cake mixes by near infrared reflectance spectroscopy

A feasibility study was carried out to assess the potential of near infrared (NIR) reflectance spectroscopy for the measurement of fat and sucrose in dry cake mixes. The calibration of the NIR instrument was carried out in a research laboratory and then assessed under quality control conditions in the factory laboratory. It was possible to measure fat with an accuracy (±2σ) of ±3.4% for products with a fat content of 8–25% compared with +0.76% for the precision of the Soxhlet procedure. In the case of sucrose the accuracy was ±5.4% for products with a sucrose content of 10–40% compared with ±2.0% for a high pressure liquid chromatography (hplc) method. It must be concluded, therefore, that while NIR offers a quicker, simpler method of quality control, this is at the expense of accuracy.     

Determination of Moisture, Fat and Protein in Spray-Dried Whole Egg by Near Infrared Reflectance Spectroscopy

Near infrared reflectance (NIR) spectroscopy was used to measure moisture, protein, and fat in spray-dried whole egg. Calibrations were developed for a commercial, filter monochromator NIR instrument and tested against standard methods. A standard error of performance (SEP) of 0.15% was obtained for moisture using a calibration based on three wavelengths. Protein and fat determinations by NIR were susceptible to variability due to sample particle size differences. Calibrations employing three wavelengths were used to measure protein and fat in samples of uniform particle size (SEP = 0.20% and 0.28%, respectively), but it was necessary to utilize additional wavelengths to adequately measure these constituents in samples having particle size variability (SEP = 0.38% for both protein and fat).     

On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy

The chemical composition of industrial scale batches of frozen beef was measured on-line during grinding by near infrared (NIR) reflectance spectroscopy. The MM55E filter based non-contact NIR instrument was mounted at the outlet of a meat grinder, and the fat, moisture and protein contents determined from the average of each filter reading throughout the grinding of the batch. The filters were selected from full spectra measurements to be as insensitive to water crystallization as possible. For on-line calibration and prediction, 55 beef batches of 400–800 kg in the range of 7.66–22.91% fat, 59.36–71.48% moisture, and 17.04–20.76% protein, were ground through 4 or 13 mm hole plates. The regression results, presented as root mean square error of cross validation (RMSECV) were between 0.48 and 1.11% for fat, 0.43 and 0.97% for moisture and 0.41 and 0.47% for protein.     

On-line, proximate analysis of ground beef directly at a meat grinder outlet

The fat, moisture and protein contents of ground beef were determined on-line by a diffuse reflectance near infrared (NIR) spectroscopy instrument at the outlet of a meat grinder. Beef samples in the range of 6.2–21.7% fat, 59.6–72.9% moisture and 18.1–20.7% protein were studied. Calibrations from samples ground with hole diameters of 4, 8, 13 or 19 mm in the grinder plate were validated. In addition, calibrations of combinations of these samples from the different hole diameters were validated. Prediction errors, expressed as root mean square error of cross validation of the beef samples, were 0.73–1.50% for fat, 0.75–1.33% for moisture and 0.23–0.32% for protein, depending on the hole diameter of the grinder plate. Calibrations from samples ground with the smallest hole diameters gave lowest prediction errors. The present prediction error results are only slightly higher compared to reported prediction error results using conventional at- and off-line NIR instruments.

It is concluded that the on-line NIR prediction results were acceptable for samples ground with grinder plates of 4, 8 or 13 mm hole diameter.     

糖粉中(淀粉类)抗结剂的鉴定研究

糖粉由白砂糖粉碎加工而成,在糕点等烘焙食品中有广泛的应用,但糖粉颗粒细小,易吸潮结块。本文通过测定糖粉产品的蔗糖、还原糖含量和浊度、电导率等理化指标,观测其水溶及其碘色反应特性,分离富集抗结剂试样,进行热重曲线、显微观测、红外光谱、X-射线荧光光谱和X-射线衍射等多种仪器的比较分析,开展糖粉中的抗结剂的结构及其成分的定性研究,综合结果发现所添加的抗结剂为玉米原淀粉,建立了一套适用于糖粉中抗结剂的定性检测的体系方法。     

Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS)

Near infrared reflectance spectroscopy (NIRS) was used to discriminate different types of ground beef samples. Fifty-three and sixty-seven samples of longissimus thoracis muscle corresponding to adult castrated steers (over 4 years old) and young cattle (under 14 months old), respectively, were homogenized and scanned over the NIR range (1100-2500nm). The results showed that NIRS could successfully discriminate 100% of ground beef samples depending on the sort of animal, probably as a consequence of differences in the intramuscular fat and water contents. Thus, NIRS is a fast, inexpensive, and non-destructive method that can be used to discriminate these beef products.     

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.     

Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review

Over the past three decades, near infrared reflectance (NIR) spectroscopy has been proved to be one of the most efficient and advanced tools for the estimation of quality attributes in meat and meat products. This review focuses on the use of NIR spectroscopy to predict different meat properties, considering the literature published mainly in the last decade. Firstly, the potential of NIR to predict chemical composition (crude protein, intramuscular fat, moisture/dry matter, ash, gross energy, myoglobin and collagen), technological parameters (pH value; L*, a*, b* colour values; water holding capacity; Warner–Bratzler and slice shear force) and sensory attributes (colour, shape, marbling, odour, flavour, juiciness, tenderness or firmness) are reviewed. Secondly, the usefulness of NIR for classification into meat quality grades is presented and thirdly its potential application in the industry is shown. The review indicates that NIR showed high potential to predict chemical meat properties and to categorize meat into quality classes. In contrast, NIR showed limited ability for estimating technological and sensory attributes, which may be mainly due to the heterogeneity of the meat samples and their preparation, the low precision of the reference methods and the subjectivity of assessors in taste panels. Hence, future work to standardize sample preparation and increase the accuracy of reference methods is recommended to improve NIR ability to predict those technological and sensory characteristics. In conclusion, the review shows that NIR has a considerable potential to predict simultaneously numerous meat quality criteria.     

On-line monitoring of fat,dry matter and acrylamide contents in potato chips using near infrared interactance and visual reflectance imaging

The contents of dry matter, oil and acrylamide are some of the most relevant parameters in the quality control of potato chips. Near infrared spectroscopy (NIR) is a common technique for routine analysis of bulk chemistry in different raw materials and products because it allows a fast and non-destructive analysis of samples. The objective of this research was to investigate the possibilities of using on-line NIR monitoring of acrylamide, moisture and oil content in potato chips. Sixty samples of potato chips from individual frying runs were measured on-line using a VIS/NIR interactance line scanner. The same samples were analysed in the laboratory to determine their corresponding moisture, acrylamide and oil contents. The mean VIS and NIR spectra for the 60 samples were modelled against the reference values for acrylamide, fat and dry matter using partial least squares regression (PLSR), and the regression models were validated using full cross-validation. On-line NIR interactance was found to predict fat and dry matter of potato chips with high accuracy, i.e. prediction errors of 0.99 and 0.86% (w/w), respectively. The corresponding correlations between predicted values and reference values were 0.99 and 0.97 for fat and dry matter. For acrylamide an average prediction error of 266 μg/kg was achieved using NIR and VIS signals in combination. The correlation between predicted values and reference values was 0.83 for this model. The system may be used to separate samples with very high acrylamide contents from samples with average to low contents.     

Non-destructive determination of fat,moisture and protein in salmon fillets by use of near-infrared diffuse spectroscopy

Near infrared (NIR) diffuse spectroscopy was used to determine the fat, moisture and protein contents in whole and ground farmed atlantic salmon fillets. A remote fibre-optic probe was used for NIR measurements on 50 whole salmon fillets. The constituent ranges were: 91-205 g kg^?1 fat, 599-709g kg^?1 moisture and 186-209 g kg^?1 protein. Principal component regression resulted in the following prediction errors for ground salmon fillets, expressed as root mean square error of cross validation: 6.6 g kg-1 fat, 3.8 g kg^?1 moisture and 2.0 g kg^?1 protein. The corresponding prediction errors for non-destructive measurements on whole salmon fillets were 10.8 g kg^?1 fat, 8.5 g kg^?1 moisture and 3.7 g kg^?1 protein. Regression models using the 760-1100 m range gave lower prediction errors than models using the 1100-2500 mm or 760-2500 nm ranges. The results show that fibre-optic probe NIR instruments are suited to determine fat and moisture in whole salmon fillets non-destructively.     

基于近红外光谱技术的鲢鱼营养成分的快速分析

目的通过采集鲢鱼的近红外光谱数据和测定鱼肉营养成分含量探索鲢鱼营养成分的快速分析方法。方法采集254个鲢鱼鱼肉样品的近红外光谱数据,经过多元散射校正、正交信号校正、数据标准化等20种方法预处理,在1000~1799 nm光谱范围内,结合化学实测值分别采用偏最小二乘法、主成分分析和BP人工神经网络技术、偏最小二乘法和BP人工神经网络技术建立鲢鱼营养成分近红外定量模型。结果鲢鱼鱼肉粗蛋白含量为12.05%~19.05%,粗脂肪含量为0.24%~5.27%,水分含量为72.62%~80.58%,灰分含量为0.46%~1.50%,数据范围较大,可满足建模要求。在3种建模方法中,近红外光谱数据结合偏最小二乘法建立的鲢鱼营养成分模型最优,所得的粗蛋白、粗脂肪、水分和灰分的近红外定量模型的相关系数分别为0.9969、0.9925、0.9831和0.9976。结论采用近红外光谱数据和偏最小二乘法建立的模型具有较好的预测能力,能较为准确、快速地分析出鲢鱼鱼肉粗蛋白、粗脂肪、水分和灰分的含量。     

近红外光谱技术在南极磷虾粉水分、脂肪和蛋白质含量快速检测中的应用

利用傅里叶近红外光谱分析技术,以磷虾粉样品的实测值与模型预测值为基础,研究了采用最小二乘法建立磷虾粉原始样品与磷虾粉混合样品中水分、脂肪和蛋白含量近红外定标模型的可行性和准确性.结果表明,磷虾粉近红外图谱最佳预处理方式为:标准正态变换预处理+一阶导数+Norris导数滤波;以磷虾粉混合样品构建的近红外模型较磷虾粉原始样...     

近红外光谱分析技术快速测定儿童高钙奶粉理化指标的研究

从化学计量学角度研究了近红外光谱技术测定儿童高钙奶粉理化指标的原理。使用多元线性回归法分别建立了近红外检测的儿童高钙奶粉水分、蛋白、脂肪、乳糖和蔗糖的快速测定模型。将近红外法测定结果和标准方法测定结果进行比较,结果吻合理想。近红外光谱法具有分析快速、操作方便、节约检测成本的优点,非常适合用于奶粉生产过程中的质量控制。     

可可粉的特性与应用

<正> 近些年,随着人民生活水平的提高,休闲食品的消费随之得到发展,其中巧克力糖果的发展潜力相当大。不少外资企业纷纷将产品销往我国或在国内投资生产。然而,产品是否受广大消费者的欢迎,巧克力浓郁的香味和滑腻的口感,是消费者所考虑的重点因素。这些又有赖于其主要成分——可可豆的提纯     

近红外光谱法快速检测带鱼肉中的水分和蛋白质含量

应用傅立叶变换近红外光谱分析技术对带鱼肉中水分和蛋白质含量进行了研究。分别建立原始光谱、间隔2点一阶导数(dblg2)、3点平滑(sa3)、标准归一化(SNV)和多元散射校正(MSC)的偏最小二乘回归(PLS)模型,比较定标相关系数(Rc)、预测相关系数(Rv)、定标标准差(SEC)和预测标准差(SEP),建立了MSC预测模型,水分和蛋白质近红外检测模型的相关系数均在0.9以上。SEC分别为0.74和0.68,SEP分别为0.81和0.73。将确定的模型进行了外部验证,水分和蛋白质NIR预测值和化学分析值的配对t检验差异均不显著。说明近红外光谱法应用于带鱼肉中水分和蛋白质含量的快速检测是可行的。     

Near-Infrared Spectroscopy Determination of Physical and Chemical Characteristics in Beef Cuts

The qualities of beef cuts were compared with near-infrared (NIR) spectroscopy readings using reflectance, transmittance and a fiber optic probe. Multiple linear regression analyses were used to select the optimum wavelengths for estimating beef properties. High multiple correlation coefficients (R) were obtained for Warner-Bratzler shear value (R = 0.798?0.826), protein (R = 0.822?0.904), moisture (R = 0.895?0.941), fat (R = 0.890?0.965) and energy content (R = 0.899?0.961) with each reflectance, transmittance and using the fiber optic probe. Total pigment content also highly correlated with optical densities using transmittance (R = 0.946) and the fiber optic probe (R = 0.893). NIR with a fiber optic probe is a useful tool for determining physical and chemical characteristics of beef.     

Challenges in Model Development for Meat Composition Using Multipoint NIR Spectroscopy from At‐Line to In‐Line Monitoring

This study evaluates the efficiency of multipoint near‐infrared spectroscopy (NIRS) to predict the fat and moisture content of minced beef samples both in at‐line and on‐line modes. Additionally, it aims at identifying the obstacles that can be encountered in the path of performing in‐line monitoring. Near‐infrared (NIR) reflectance spectra of minced beef samples were collected using an NIR spectrophotometer, employing a Fabry‐Perot interferometer. Partial least squares regression (PLSR) models based on reference values from proximate analysis yielded calibration coefficients of determination of 0.96 for both fat and moisture. For an independent batch of samples, fat was estimated with a prediction coefficient of determination of 0.87 and 0.82 for the samples in at‐line and on‐line modes, respectively. All the models were found to have good prediction accuracy; however, a higher bias was observed for predictions under on‐line mode. Overall results from this study illustrate that multipoint NIR systems combined with multivariate analysis has potential as a process analytical technology (PAT) tool for monitoring process parameters such as fat and moisture in the meat industry, providing real‐time spectral and spatial information.     

基于近红外光谱技术的腐竹脂肪定量分析

采用近红外光谱技术结合化学计量学方法,建立腐竹脂肪含量的快速分析方法。收集不同生产线、不同时间的腐竹样本180 份,利用积分球附件采集漫反射光谱（4 000～10 000 cm－1）。为消除颗粒散射影响和光谱基线漂移,二阶导数和卷积平滑用于光谱预处理。采用反向区间偏最小二乘法、组合区间偏最小二乘法、搜索组合移动窗口偏最小二乘法和遗传偏最小二乘法优化建模变量,最终构建了定量预测模型。结果显示,4 种方法均可有效地提取信息变量、降低模型维度、提高预测性能;遗传偏最小二乘法一次优选获得143 个变量,构建的模型性能最佳,其校正相关系数、校正均方根误差、预测相关系数、预测均方根误差分别为0.96、0.95、0.92和1.17。研究表明,经过信息变量提取后所构建的近红外模型简单、预测精度高,可用于腐竹脂肪含量的日常监测。     

Near-infrared spectroscopic analysis of macronutrients and energy in homogenized meals

Near-infrared (NIR) reflectance spectroscopy was evaluated as a rapid and environmentally benign technique for the simultaneous determination of macronutrients and energy in commercially available, packaged meals. Reflectance spectra (400–2498 nm) of homogenized meals were obtained with a dispersive NIR spectrometer. Protein and moisture were measured by AOAC reference methods, total fat by a semi-automated acid hydrolysis, solvent extraction, gravimetric method and total carbohydrate calculated. Energy was calculated using Atwater factors. Using multivariate analysis software, PLS models (n = 113–115 products) were developed to relate NIR spectra of homogenized meals to the corresponding reference values. The models predicted components and energy in validation samples (n = 37–38 products), overall, with r² of above 0.96. Ratios of deviation to performance were between 3.6 and 6.6, and indicated adequacy of the models for screening, quality control, or process control. Performance of the models varied substantially when used to predict sub-groups of meals within the validation set.     

The determination of protein,fat and moisture in bread by near infrared reflectance spectroscopy

Near infrared (n.i.r.) reflectance spectroscopy has been employed for the determination of protein, fat and moisture in sliced white bread. N.i.r. reflectance at six wavelengths was measured using circular samples from each of six alternate slices taken from one half of each of 30 loaves of different composition. The six readings for each loaf at each wavelength were averaged and used to produce calibrations which, on prediction of the compositions of a further 30 loaves sampled in the same way, gave rise to standard deviations of differences between n.i.r. and standard procedures of 0.20% for protein, 0.18% for fat and 0.51% for moisture. Calibrations derived from the other halves of the loaves, which had been air-dried and ground to a powder, resulted in similar standard deviation of differences for protein and fat.