基于多源光谱分析技术的鱼油品牌判别方法研究

多源光谱分析技术被用于鱼油品牌快速无损鉴别。采用可见光谱分析技术、短波近红外光谱分析技术、长波近红外光谱分析技术、中红外光谱分析技术和核磁共振光谱分析技术采集了7种不同品牌的鱼油的光谱特征,并应用偏最小二乘判别分析法(partial least squares discrimination analysis,PLS-DA)和最小二乘支持向量机(least-squares support vector machine,LS-SVM)建立判别模型并比较判别结果。基于长波近红外光谱的PLS-DA模型和LS-SVM模型取得了最高识别正确率,建模集和预测集识别正确率均达到100%。采用中红外光谱和核磁共振谱分别建立的LS-SVM模型,也可以获得100%的判别正确率。而可见光谱和短波近红外光谱则判别准确率较差。且LS-SVM算法较PLS-DA更加适合用于建立光谱数据和鱼油品牌之间的判别模型。研究结果表面长波近红外光谱技术能够有效判别不同鱼油的品牌,为将来鱼油品质鉴定便携式仪器的开发提供了技术支持和理论依据。     

近红外光谱分析技术快速检测藕粉品质

目的 基于近红外光谱技术鉴别不同产地的藕粉样品与检测藕粉的掺假问题。方法 采集不同产地的藕粉样品的近红外光谱,在光谱预处理后采用相关系数法提取特征波长,以提取的特征波长变量构建偏最小二乘法-判别分析(partial least squares discriminant analysis,PLS-DA)、线性判别分析(linear discriminant analysis,LDA)以及支持向量机(support vector machine,SVM)模型,实现对不同产地藕粉的鉴别分析。同时,采集掺假地瓜粉、玉米粉、木薯粉的藕粉样品的近红外光谱,在样品类别已知情况下,运用K-Means聚类分析鉴别3种掺假类型的藕粉样品,在掺假类别未知下,运用基于局部密度判别的聚类算法进行判别。结果 以相关系数法提取的特征波长变量构建的PLS-DA、LDA和SVM三种模型对于不同产地藕粉样品的判别准确率均为100%。对于不同掺假类型的藕粉检测,在样品类别已知情况下,K-Means聚类分析能有效识别出掺假藕粉,识别精度为98.33%。在样品类别未知的情况下,基于局部密度判别的聚类算法可以有效识别出2%掺假率的藕粉样品。结论 近红外光谱技术能实现不同产地莲藕粉的快速鉴别,同时为隐蔽的藕粉掺假鉴别提供一种快速、高效、无损检测的分析方法,为藕粉的质量控制提供一定的理论基础。     

基于GC-IMS技术分析小麦粉储存过程中挥发性物质的变化

研究小麦粉在储藏过程中典型指标的变化规律,基于气相色谱-离子迁移谱（GC-IMS）技术分析小麦粉储藏过程中挥发性有机物的变化,为小麦粉储存期间品质评价提供新的分析方法和参考依据。结果表明：小麦粉中挥发性物质以醇类、醛类、酯类化合物为主;利用GC-IMS的分析结果结合主成分分析、偏最小二乘-判别分析和热图聚类分析可以有效区分不同储藏期样品,小麦粉储藏过程中挥发性物质变化与水分、脂肪酸值和品尝评分值的变化趋势,验证了小麦粉在储藏过程中的挥发性物质变化可以用于储存时间的判断。     

基于近红外和中红外光谱技术的小麦粉品质检测及掺杂鉴别方法

针对国家标准法检测小麦粉品质的传统方法存在一定缺陷,提出基于近红外光谱和中红外光谱技术快速检测面粉的方法,并基于偏最小二乘法建立了矫正模型,对小麦粉的灰分、水分、面筋品质指标进行了分析。对于小麦粉的掺杂鉴别问题,基于标准法测光谱距离建立了聚类分析模型,结果表明,可实现对小麦面粉品质的快速检测及掺杂鉴别。     

多元统计分析在小麦粉产地溯源中的应用

目的筛选小麦粉产地溯源特征元素,为深入挖掘食品安全风险监测数据,开发成熟有效的食品溯源技术积累基础。方法采用电感耦合等离子体质谱法测定河北省、新疆维吾尔自治区和江苏省共计173份小麦粉样品中的10种无机元素含量,利用主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)、正交偏最小二乘判别分析(OPLS-DA)建立模式识别模型,考察建模效果。结果 PCA模型可以将新疆维吾尔自治区样品与其他两省实现分离;PLS-DA可以实现3个地区样品的分离;河北省和新疆维吾尔自治区、江苏省和新疆维吾尔自治区均在OPLSDA模型中得到良好分离。结论利用PCA、PLS-DA和OPLS-DA三种多元统计分析方法对河北省、新疆维吾尔自治区和江苏省3个地区小麦粉中10种无机元素进行分析,筛选出铜(Cu)、铁(Fe)和砷(As)三个特征元素,这些特征元素有望被应用于小麦粉产地溯源。     

Discrimination of Producing Areas of Auricularia auricula Using Visible/Near Infrared Spectroscopy

Visible and near infrared (Vis/NIR) spectroscopy combined with chemometric methods was applied for the discrimination of producing areas of Auricularia auricula. Four major varieties of commercial A. auricula were prepared for spectral acquisition. Some pretreatments were performed, such as Savitzky–Golay smoothing, standard normal variate, and the first and second Savitzky–Golay derivative. The scores of the top four latent variables, extracted by partial least squares, were considered as the inputs of back propagation neural network (BPNN) and least squares-support vector machine (LS-SVM). The performance was validated by 60 validation samples. The excellent recognition ratio was 98.3% by BPNN and 96.7% by LS-SVM model with the threshold prediction error ±0.1. The results indicated that Vis/NIR spectroscopy could be used as a rapid and high-precision method for the discrimination of different producing areas of A. auricula by both BPNN and LS-SVM methods.     

基于近红外技术和偏最小二乘判别法对无花果成熟度的快速判别

以无花果为试验对象,对其进行近红外光谱采集,并对其糖度、单果重、纵径、横径、硬度5个指标进行K-均值聚类;根据光谱数据、主成分分析确定最优聚类效果的成分和各类别的指标分布构建偏最小二乘判别分析(PLS-DA)模型进行聚类判别,以实现对果实成熟度(幼果期、成长期、成熟期)分类的准确、快速、无损伤鉴别。结果表明,3种成熟阶段的无花果样品的糖度、单果重和硬度均具有显著性差异,成熟果和成长果与幼果的纵径和横径间具有显著性差异。根据PLS-DA判别模型累计训练集的分类正确率为99.59%,测试集的分类正确率为99.15%。说明主成分分析与光谱数据所建立的PLS-DA模型性能较好,对无花果成熟度的快速鉴别是有效且可行的。     

浅谈前路出粉法的演变与发展

前路出粉法就是加宽前路,缩短心磨,全部改气辊为齿辊,放粗筛网,高整粉路,麸渣混走,出粉越多越好。前路均衡出粉法在磨辊接长分配上,减少皮磨接长,增加心渣磨接长,与前路出粉法相比,扩大了前路出粉范围。前路磨撞结合出粉法与前路均衡出粉法相比,扩大延伸了出粉部位,增加了重筛与使用光辊,增加了强力撞击松粉机与撞击磨,并大量使用清粉机。小麦制粉技术从反复研磨到逐步研磨,基本原理仍然贯穿其中,结合先进的工艺技术,对小麦制粉工艺提出合理的的优化和改进,节能降耗仍有广阔的发展空间。     

面粉品质近红外光谱在线检测系统开发与应用

本研究运用近红外光谱无损检测技术,开发了一种适用于面粉品质检测的在线测量系统。本系统在硬件平台基础上,采用C++Builder 6.0对NIR 1.7/S微型光谱仪进行二次开发,编写了具有光谱采集、面粉品质预测、模型更新和数据存储等功能的软件。对市售170种面粉进行试验,以面粉水分含量为代表性指标。通过对比不同光谱预处理方法建模结果,发现不进行任何预处理时的面粉水分偏最小二乘回归(PLS)得到的模型精度最高。建模集和验证集决定系数(R2)分别为0.947,0.841;均方根误差(RMSE)分别为0.146%,0.198%;RPD值为2.53。模型导入软件后对30份新样品进行外部验证,预测值与测量值决定系数(R2)为0.883,均方根误差为0.206%。结果表明,该系统能够初步实现面粉水分的实时预测,为近红外在线检测技术应用提供了一定的技术参考。     

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Near-infrared (NIR) spectroscopy was investigated to determine the acetic, tartaric, formic acids and pH of fruit vinegars. Optimal partial least squares (PLS) models were developed with different preprocessing. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including wavelet transform (WT), latent variables, and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The optimal correlation coefficient (r), root mean square error of prediction and bias for validation set were 0.9997, 0.3534, and −0.0110 for acetic acid by WT-LS-SVM; 0.9985, 0.1906, and 0.0025 for tartaric acid by WT-LS-SVM; 0.9987, 0.1734, and 0.0012 for formic acid by EW-LS-SVM; and 0.9996, 0.0842, and 0.0012 for pH by WT-LS-SVM, respectively. The results indicated that NIR spectroscopy (7,800–4,000 cm⁻¹) combined with LS-SVM could be utilized as a precision method for the determination of organic acids and pH of fruit vinegars.     

小麦粉中甲醛本底值的测定

甲醛次硫酸氢钠是一种化工原料,国家已明令禁止将其作为添加剂加入食品。目前,还没有测定面粉中甲醛次硫酸氢钠的国家标准方法。本文用2002年国家质检总局推荐的方法对我国不同地区种植的122份小麦自磨粉的甲醛本底值进行了测定,进而为测定小麦粉中甲醛次硫酸氢钠含量提供科学依据。122份小麦自磨粉的本底平均值为3．85mg/kg,变幅为0．92～6．77mg/kg。95%概率下的置信区间最大值为6．31mg/kg,该数值即可作为运用该方法时所有小麦粉样品的甲醛本底值。     

Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder

The authentication of food products is critically important in a global economy in public-health and economic terms. The specific aims of this study were to evaluate the application of full-spectrum and NIR spectroscopy and to evaluate the adoption of PLS and LS-SVM models to accomplish a rapid and non-invasive quantification of the two common adulterants, flour and mungbean powder, in Spirulina powder. The results showed that, using all treatment sets, only the LS-SVM models were adequate in predicting either adulterant under both full spectra and NIR spectra. The use of NIR spectra would allow detection of adulterants even when masked by food dyes. Design value analysis indicated that the benefits per unit cost of applying the NIR spectra to quantify adulterants in Spirulina powder significantly exceeded that of using full spectra, and that the value of employing the LS-SVM models under NIR spectra exceeded that of using the PLS models.     

Determination of benzoyl peroxide, as benzoic acid, in wheat flour by capillary electrophoresis compared with HPLC

BACKGROUND: In recent years, debate on the addition of benzoyl peroxide (BP) to wheat flour has increased in China. Medical studies have so far not confirmed that BP in wheat flour causes definite damage to the human body, but its main metabolite in the human body is benzoic acid. The addition of BP to wheat flour has been forbidden in China since 1 May 2011. It is therefore necessary to develop a suitable method to determine BP in wheat flour. RESULTS: A simple method using capillary electrophoresis (CE) was developed for determination of BP in wheat flour. BP was determined as benzoic acid after preceding reduction by potassium iodide. Separation was completed in less than 7 min with a running buffer of 10 mmol L^?1 sodium borate (pH 9.18), 4 s hydrodynamic injection, 25 kV separation voltage and UV detection at 228 nm. The method was validated in terms of linearity, sensitivity, detection limit, precision and accuracy. The detection limit was 0.29 µg mL^?1. The method was successfully applied to the determination of BP in wheat flour samples, with recoveries between 96.1% and 102.6%. CONCLUSION: The performance of the CE method was comparable, and the quantitative results were in good agreement with those using high‐performance liquid chromatography (HPLC). Meanwhile the proposed CE method has the advantages of better resolution, shorter analysis time and lower cost, and would be a good alternative to HPLC for routine monitoring of BP amount in wheat flour. Copyright © 2011 Society of Chemical Industry     

Simultaneous detection for adulterations of maltodextrin,sodium carbonate,and whey in raw milk using Raman spectroscopy and chemometrics

To achieve rapid on-site identification of raw milk adulteration and simultaneously quantify the levels of various adulterants, we combined Raman spectroscopy with chemometrics to detect 3 of the most common adulterants. Raw milk was artificially adulterated with maltodextrin (0.5–15.0%; wt/wt), sodium carbonate (10–100 mg/kg), or whey (1.0–20.0%; wt/wt). Partial least square discriminant analysis (PLS-DA) classification and a partial least square (PLS) regression model were established using Raman spectra of 144 samples, among which 108 samples were used for training and 36 were used for validation. A model with excellent performance was obtained by spectral preprocessing with first derivative, and variable selection optimization with variable importance in the projection. The classification accuracy of the PLS-DA model was 95.83% for maltodextrin, 100% for sodium carbonate, 95.84% for whey, and 92.25% for pure raw milk. The PLS model had a detection limit of 1.46% for maltodextrin, 4.38 mg/kg for sodium carbonate, and 2.64% for whey. These results suggested that Raman spectroscopy combined with PLS-DA and PLS model can rapidly and efficiently detect adulterants of maltodextrin, sodium carbonate, and whey in raw milk.     

基于近红外光谱快速定量检测面粉中曲酸的方法建立

利用近红外光谱技术快速定量检测面粉中非法添加的褐变抑制剂曲酸。选取市场上常见3?种基本类型的面粉（高、中、低筋面粉）,分别制备曲酸质量分数为0.0%、0.5%、1.0%、3.0%、5.0%、10.0%的面粉样品,并采集其在1?000～2?400?nm波段下的光谱数据。对比不同预处理下高筋面粉样品数据所建偏最小二乘（partial least squares,PLS）回归模型效果,选取Savitzky-Golay一阶导数为最优预处理方法。采用区间偏最小二乘（interval partial least squares,iPLS）法选取1?088.8～1?153.5?nm为最佳光谱区间。结果表明,基于最佳光谱区间所建PLS回归模型预测效果优于基于全波段光谱数据所建模型。进一步,基于所选最优区间对中、低筋面粉和混合样品集分别建立PLS回归模型。高、中、低筋面粉及混合样品集基于最优区间的PLS模型的决定系数为0.949～0.972,标准误差为0.581%～0.830%,验证集标准偏差与预测标准偏差的比值为4.171～4.830。结果表明,基于最优区间的近红外光谱方法对不同类型面粉中曲酸质量分数为1.0%～10.0%的样品具有较好的预测结果,结合具有低检测限的化学检测方法,在对大批量样品的检测中可提高检测效率。     

An energy analysis of the manufacture of wheat starch and related products

A study was made of energy use in the manufacture of wheat starch and related products. The process examined, the Tenstar method, used wheat flour as the raw material for producing the following products: wheat starch in a dry or wet form, dried wheat gluten, a glucose syrup and powdered dextrose. Energy use in the associated flour milling process was also examined. Milling and the transport of wheat and flour accounted for 1.27-1.31 GJ t^?1 dry solids (DS) of each Tenstar product, whereas manufacture required between 4.81-31.33 GJ t^?1 product DS. Wet starch was the least energy intensive product and dried gluten the most energy intensive on this basis. However, the energy inputs to gluten when related to this product's protein content, showed wheat gluten to be a dietary protein source whose production has a very low energy requirement (57.8 GJ t^?1 protein). The energy requirements for manufacturing powdered dextrose are similar to reported requirements for refining sucrose from sugar beets, giving an energy ratio (gross calorific energy output: energy input) of 0.53 for dextrose, as compared to 0.49-0.71 for beet sugar.     

Measurement of Soluble Solids Content and pH of Yogurt Using Visible/Near Infrared Spectroscopy and Chemometrics

Visible/near infrared spectroscopy (Vis/NIRs) technique was applied to non-destructive quantification of sugar and pH value in yogurt. Partial least squares (PLS) analysis and least squares support vector machine (LS-SVM) were implemented for calibration models. In this paper, three brands (Mengniu, Junyao, and Guangming) were set as the calibration, and the remaining two brands (Yili and Shuangfeng) were used as prediction set. In the LS-SVM model, the correlation coefficient (r), root mean square error of prediction, and bias in prediction set were 0.9427, 0.2621°Brix, 1.804e−09 for soluble solids content, and 0.9208, 0.0327, and 1.094e−09 for pH, respectively. The correlation spectra corresponding to the soluble solids content and pH value of yogurt were also analyzed through PLS method. LS-SVM model was better than PLS models for the measurements of soluble solids content and pH value. The results showed that the Vis/NIRs combined with LS-SVM models could predict the soluble solids content and pH value of yogurt.     

拉曼光谱结合模拟退火的小麦粉灰分含量检测

针对当前小麦粉中灰分的检测方法测量周期长,且手工操作复杂,容易产生误差的问题,研究提出采用拉曼光谱技术,结合5种光谱预处理方法和模拟退火算法进行波数筛选两方面优化小麦粉中灰分的拉曼光谱模型。实验结果中,卷积平滑SG(17)+标准正态变换(SNV)+模拟退火(SAA)的优化方式效果最优,相关系数(R~2)为0.9875,均方根误差(RMSEC)和预测均方根误差(RMSEP)分别为0.0161和0.15,相对分析误差(RPD)高达8.1679,模型稳健性参数良好。研究结果表明,在模型待测组分浓度与波数相关性以及模型预测准确性方面展现了自身的优越性,该方法简单实用,快速准确,有望将此技术扩展应用于整个食品行业。     

小麦粉特性与生湿面品质的关系研究

研究了小麦粉特性与生湿面品质的关系。以13种小麦粉为原料,测定其蛋白质质量、溶剂保持力、内源酶活力等特性,并对制得生湿面的外观、质构及感官品质进行分析。结果表明,麦谷蛋白大聚体质量分数与面片亮度呈显著负相关,谷醇比与熟面黏聚性呈显著负相关,乳酸溶剂保持力与咀嚼性、感官评分呈显著正相关,脂肪氧合酶活力与硬度、咀嚼性呈显著正相关。经主成分分析和聚类分析,将13种小麦粉分为3类：第Ⅲ类小麦粉的蛋白质质量、面团流变学品质显著优于其他小麦粉,熟面食用品质最佳;第Ⅱ类小麦粉制得面片亮度显著高于其他小麦,外观品质最优。通过将国产小麦与第Ⅱ类/第Ⅲ类小麦搭配可用于制备外观与食用品质俱佳的生湿面专用粉。     

Simultaneous Determination of Danofloxacin and Flumequine in Milk Based on Fluorescence Spectroscopy and Chemometrics Tools

The aim of this paper was to develop a rapid screening method to determine danofloxacin (DANO) and flumequine (FLU) in milk by fluorescence spectroscopy combined with three different chemometric tools. In this study, 2-D fluorescence data and multivariate calibration based on a partial least squares discriminant analysis (PLS-DA) regression were combined to simultaneously qualify and quantify DANO and FLU concentrations in commercial ultra-high-temperature (UHT) sterilized and pasteurized milk. Calibration sets based on the UHT whole milk from brand A were built and performed using a partial least squares (PLS) regression after deproteinization. Prediction sets based on 13 types of milk were analyzed using principal component analysis (PCA), principal PLS-DA, and PLS regression models. The multivariate calibration models were better able to determine the DANO and FLU concentrations than the univariate models, and these models could be applied to other types of milk. In contrast to the PLS-DA, which had good sensitivity and specificity, the PCA yielded less satisfactory results. In the quantitative analysis, the recoveries of the two analytes were reasonable and the root mean square error of prediction was within the acceptable range. The relative standard deviations of the predicted DANO and FLU concentrations on the various testing days were 9.2 and 6.2 %, respectively, demonstrating that the analytical method had a good reproducibility.