The application of near-infrared (NIR) and Raman spectroscopy to detect adulteration of oil used in animal feed production

Basic vegetable blends (BVB’s) and soya oils, used in the animal feed industry, are sometimes adulterated with transformer and mineral oil as a means of illegally increasing profit. A set of BVB’s and soya oil samples adulterated with transformer oil and mineral oil were characterised using both NIRS and Raman spectroscopy. Applying chemometrics to the NIRS and Raman spectral data, very good calibration and prediction statistics were obtained for transformer and mineral oils. Using NIRS, R² values greater than 0.99 were obtained with corresponding values for root mean squared error of calibration and prediction (<0.57 and <0.55, respectively). Using Raman, R² values greater than 0.97 were obtained with the root mean squared error of calibration (<2.01) and prediction (<1.92) calculated. Furthermore, using a qualitative approach it was possible, using PCA, to discriminate between 100% soya and BVB. This study demonstrates that both NIRS and Raman technology can be successfully applied as rapid screening techniques for the detection of oil adulteration and fraud in the food and feed industry.     

Tissue deposition and residue depletion of melamine in fattening pigs following oral administration

The adulteration of animal feed as well as milk products with melamine has led to concerns about the ability to establish appropriate withdrawal intervals to ensure food safety. Two experiments were conducted in this study. The first was to investigate the deposition and depletion of melamine in blood and tissues of pigs exposed to adulterated feed with high doses of melamine. A total of 500 or 1000 mg kg^–1 melamine was added to the diet for fattening pigs (initial BW = ±60.24 kg). Melamine residues were detected in tissues (brain, duodenum, liver, heart, muscle and kidney) by LC-MS/MS. Dose-dependent effects were found between melamine residual concentration and its dose in feed. Five days after the withdrawal of melamine from the diets, the residue concentration in tissues fell below 2.5 mg kg^–1. In the second experiment, blood samples were taken at different time points from fattening pigs (BW = 100 kg) fed with adulterated feed with 1000 mg kg^–1 of melamine for 42 days. Results from the pharmacokinetics analysis showed that it would take 83 h for the melamine level in plasma depleting to the safe level of 50 ng ml^–1 after an expose of 1000 mg kg^–1 melamine contaminated feed for 42 days.     

Spectrophotometric determination of melamine in milk by rank annihilation factor analysis based on pH gradual change-UV spectral data

A new spectrophotometric method has been developed in this paper to determine melamine in milk by applying rank annihilation factor analysis (RAFA) based on pH gradual change-UV spectral data (pH-spectra). In the proposed method, the spectra of the sample solutions at different pH data points were recorded and the pH-spectra bilinear data matrix was generated. Based on these data, the RAFA was then applied to calculate the concentration of melamine in milk. The experiments have been conducted and the results were satisfactory. Under the optimised conditions, linearity of the proposed method was in the range of 0.04–4.0 μg mL⁻¹ for calibration samples, and 0.04–3.5 μg mL⁻¹ for the mixed solutions of melamine with the background milk components. The detection limit (DL) was 12 ng mL⁻¹. The relative predictive error (RPEs) and root mean square error of prediction (RMSEP) of applying RAFA were 0.91% and 0.0151, respectively.     

利用光密度法对掺假豆浆的定性判别研究

蛋白质含量是豆浆品质评价的主要指标,实验运用近红外光谱技术获得83 个真伪豆浆的光谱,并对光谱图和光密度值进行统计分析,研究以蛋白质为主要定性指标的豆浆品质等级划分的可行性,建立豆浆品质定性判别的标准。结果显示：在波长742.59～810.96 nm范围内,随着豆浆样品蛋白质含量的升高,吸收光谱峰值变化越大。实验选取OD810.96 nm与OD742.59 nm做光密度差值分布图,根据83 个校正集样品的光密度差值分布图,确定豆浆两级判别的检测标准为：ΔOD742.59～810.96 nm大于0.062 9时,豆浆为不合格豆浆;ΔOD742.59～810.96 nm小于或等于0.062 9时,豆浆为合格豆浆。根据该判别标准对37 个预测集样品进行判别,17 个不合格豆浆全部被判别,正确判别率100%,20 个合格豆浆中有2 个被误判成不合格,误判率10%,预测结果准确率较高。实验应用光密度法进行豆浆品质的评价是可行的,方法简明、结果可靠,可为豆浆品质快速检测技术的应用提供一种参考方法。     

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R ²) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.     

基于近红外光谱的大鲵肉粉掺伪鉴别及纯度检测

利用近红外光谱技术进行大鲵肉粉的掺伪鉴别及纯度检测。分别采集大鲵纯肉粉、掺入江团鱼肉粉、草鱼肉粉和土豆淀粉的掺伪大鲵肉粉（各40 个样本,4 类共160 个样本）的近红外光谱图。原始光谱经光谱预处理后,利用偏最小二乘-判别分析（partial least square-discriminant analysis,PLS-DA）法分别建立2分类（纯样和掺伪样）和4分类（纯样、掺江团鱼样、掺草鱼样和掺淀粉样）的定性判别模型,利用偏最小二乘回归（partial least squares regression,PLSR）分析法分别建立3 类掺伪大鲵肉粉的纯度定量校正模型。结果表明,PLS-DA定性模型中,经一阶导数＋多元散射校正光谱预处理后,所建2分类和4分类模型性能均为最佳,校正集和预测集的预测准确率均为100%;PLSR定量模型中,大鲵肉粉掺江团鱼肉粉、大鲵肉粉掺草鱼肉粉和大鲵肉粉掺土豆淀粉模型的校正集相关系数（Rc2）分别为0.990 6、0.986 4和0.993 3,校正集的均方根误差分别为1.14%、1.39%和0.88%;测试集的相关系数（Rp2）分别为0.994 4、0.992 4和0.990 8,测试集的均方根误差分别为0.83%、0.89%和1.22%。运用近红外光谱技术结合化学计量学方法能够对大鲵肉粉进行掺伪鉴别及纯度检测。     

Rapid Determination of Spinal Cord Content in Ground Beef by Near-Infrared Spectroscopy

The potential of near‐infrared spectroscopy to measure spinal cord content in ground beef was investigated. Near‐infrared spectra were obtained for ground beef adulterated with 0 to 100 ppm spinal cord. A partial least square model was developed with 2nd derivative near‐infrared spectral data (5400/cm to 10000/cm). The model showed a calibration of R_val= 0.94 and a validation of R_val= 0.90. The detection limit of the model was 21 ppm, and sensitivity to detect true positive spinal cord–adulterated samples was 87%. Near‐infrared spectroscopy has the potential to identify spinal cord–adulterated ground beef.     

拉曼光谱法快速检测猪肉脯中的掺伪鸡肉

目的 建立拉曼光谱法快速、准确、无损地检测猪肉脯样品中掺假鸡肉的方法。方法 制备33份猪肉中掺入不同比例鸡肉的肉脯样品,采集拉曼光谱数据,分别采用标准正态变换、多元散射校正、卷积平滑、归一化、一阶导数等5种不同预处理方法,对原始光谱数据进行预处理,采用连续投影算法、竞争性自适应重加权算法及随机蛙跳算法对光谱数据进行特征波长筛选,建立偏最小二乘法(partial least squares,PLS)模型对猪肉脯进行定性定量判别。结果 拉曼光谱数据经过多元散射校正处理的效果最佳,竞争性自适应重加权算法竞筛选效果更佳,构建猪肉脯中猪肉含量的PLS定量模型,其预测集决定系数和预测均方根误差分别为0.9762、7.2998。建立的PLS判别模型的校正集和预测集总判别正确率分别为100.00%和98.33%。结论 拉曼光谱分析技术可有效用于定性鉴别猪肉脯是否掺伪及定量分析猪肉肉脯中掺入鸡肉的比例,为肉脯掺假的快速无破坏性检测的应用提供支持。     

High-performance liquid chromatographic method for the simultaneous detection of the adulteration of cereal flours with melamine and related triazine by-products ammeline, ammelide, and cyanuric acid

Melamine has been used for the adulteration of cereal flours in order to increase their apparent protein content. Crude melamine may contain several by-products, i.e. ammeline, ammelide, and cyanuric acid. The simultaneous analysis of all four chemicals is difficult because of the formation of an insoluble salt between melamine and cyanuric acid. A simple and convenient high-performance liquid chromatography (HPLC) method for the detection of the adulteration of cereal flours with all four chemicals is proposed herein. The precipitate formation between melamine and cyanuric acid was prevented by using alkaline conditions (pH 11-12) for both standards preparation and sample extraction. The method uses matrix-matching, which involves the construction of a calibration curve on a blank (negative control) matrix, which is then used for the quantitation of melamine and by-products in adulterated (positive) samples. Matrix-matching compensates for analyte losses during sample preparation, and for matrix effects. The method was successfully applied to wheat, corn, and rice flours, and is expected to be applicable (with some modifications) to soy flour as well. The method allows for the detection of melamine, ammeline, and ammelide at approximately 5 µg g^-1, and cyanuric acid at approximately 90 µg g^-1 in wheat flour.     

The potential of NIR spectroscopy for the detection of the adulteration of orange juice

Near-infrared spectroscopy was used to investigate the adulteration of 65 authentic concentrated orange juice samples obtained from Brazil and Israel. These samples were adulterated with 100 g kg^?1 additions (ie 100 g added to 900 g) of (1) orange pulpwash, (2) grapefruit juice, and (3) a synthetic sugar/acid mixture and with 50 g kg^?1 additions (ie 50 g added to 950 g) of (4) orange pulpwash, and (5) grapefruit juice. All samples were scanned on the NIR systems 6500 spectrophotometer over the 1100-2498 nm wavelength range. Principal component analysis was used to reduce each spectrum to 20 principal components. Factorial discriminant analysis was used to distinguish between the different sample groups. Using orange juice and orange juice adulterated at the 100 g kg^?1 level, accurate classification rates of 94–95% were obtained. To classify samples adulterated at the 50 g kg^?1 level, the calibration development sample set had to be augmented by the inclusion of samples adulterated at this lower level—after this augmentation, an accurate classification rate of 94% was obtained. The results demonstrated that the application of principal component and factorial discriminate analysis to NIR reflectance spectra can detect the adulteration of orange juice with an average accuracy of 90%. Furthermore, not one adulterated sample was predicted as being an authentic orange juice throughout the entire test regime.     

近红外特征光谱定量检测羊肉卷中猪肉掺假比例

利用近红外漫反射光谱技术结合化学计量学方法对解冻掺假羊肉卷,进行猪肉掺假比例的定量检测研究。按照不同肥肉占比和不同猪肉掺假比例,制备324?个样品,并利用近红外光谱仪采集其光谱数据。对原始数据进行SG（Savitzky-Golay）平滑、SG一阶导、SG二阶导、多元散射校正、中心化、标准正态变量校正等预处理,并利用偏最小二乘回归（partial least square regression,PLSR）进行建模分析,其中SG平滑结合一阶求导预处理的模型预测效果最优。针对最佳预处理光谱采用竞争性自适应加权采样（competitive adaptive reweighted sampling,CARS）算法进行波长筛选,并建立特征波长PLSR模型,模型预测效果得到提高。其中,校正集和验证集决定系数分别为0.983?6和0.972?5,校正集和验证集的均方根误差分别为0.043?7和0.057?7,范围误差比为7.62。应用该CARS-PLSR模型对检验集进行预测,真实值与预测值的相关系数为0.913?8,结果表明采用近红外光谱分析技术可以实现不同肥肉占比羊肉卷中猪肉掺假比例的定量检测。     

核磁共振氢谱结合化学计量学快速检测掺假茶油

摘 要：以纯茶油和掺假茶油（掺入大豆油、玉米油）作为核磁共振氢谱检测对象,结合化学计量学方法分析处理核磁数据,建立一种能快速预测茶油掺假的方法。结果表明：纯茶油和掺假茶油在主成分分析得分图上有较好地区分,且掺假样品随掺假比例在图中呈规律性分布,但少部分低体积分数的掺假油与纯茶油重叠。而采用偏最小二乘判别（partial least squares discriminant analysis,PLS-DA）法可以得到更好的分离效果,在该模型中,纯茶油的判别准确率为100%。进一步采用PLS可实现对茶油掺假水平的准确定量测定。该方法可简单、快速地用于茶油的掺假鉴别,在茶油品质控制及评价方面具有很大的应用潜力。     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.     

Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

The use of fibre optic diffuse reflectance near infrared spectroscopy (NIR) in combination with chemometric techniques has been investigated to discriminate authenticity of honey. NIR spectra of unadulterated honey and adulterated honey samples with high fructose corn syrup were registered within 10,000–4000 cm⁻¹ spectral region. Discriminant partial least squares (DPLS) models were constructed to distinguish between unadulterated honey and adulterated honey samples and main bands responsible for the discrimination of samples are in the range of 6000–10,000 cm⁻¹. For these models, the correct classification rate for calibration samples were above 90%. Hundred percentage of unadulterated honey and 95% of adulterated honey samples from test set were correctly classified after appropriate preprocessing of first derivative, 13 smoothing points, followed by mean centering pre-treatment and eight model factors, respectively. Our results showed that NIR spectroscopy data with chemometrics techniques can be applied to rapid detecting honey adulteration with high fructose corn syrup.     

Robust new NIRS coupled with multivariate methods for the detection and quantification of tallow adulteration in clarified butter samples

Cows’ butterfat may be adulterated with animal fat materials like tallow which causes increased serum cholesterol and triglycerides levels upon consumption. There is no reliable technique to detect and quantify tallow adulteration in butter samples in a feasible way. In this study a highly sensitive near-infrared (NIR) spectroscopy combined with chemometric methods was developed to detect as well as quantify the level of tallow adulterant in clarified butter samples. For this investigation the pure clarified butter samples were intentionally adulterated with tallow at the following percentage levels: 1%, 3%, 5%, 7%, 9%, 11%, 13%, 15%, 17% and 20% (wt/wt). Altogether 99 clarified butter samples were used including nine pure samples (un-adulterated clarified butter) and 90 clarified butter samples adulterated with tallow. Each sample was analysed by using NIR spectroscopy in the reflection mode in the range 10,000–4000 cm^?1, at 2 cm^?1 resolution and using the transflectance sample accessory which provided a total path length of 0.5 mm. Chemometric models including principal components analysis (PCA), partial least-squares discriminant analysis (PLSDA), and partial least-squares regressions (PLSR) were applied for statistical treatment of the obtained NIR spectral data. The PLSDA model was employed to differentiate pure butter samples from those adulterated with tallow. The employed model was then externally cross-validated by using a test set which included 30% of the total butter samples. The excellent performance of the model was proved by the low RMSEP value of 1.537% and the high correlation factor of 0.95. This newly developed method is robust, non-destructive, highly sensitive, and economical with very minor sample preparation and good ability to quantify less than 1.5% of tallow adulteration in clarified butter samples.     

一种水溶液中果糖含量的近红外光谱分析法

目的建立一种水溶液中果糖含量的近红外光谱分析仪(near-infraredspectroscopyanalysis,NIRSA)检测方法。方法实验中所用的样品数量为30个,随机选取26个作为校正集样品,用于建立果糖的校正模型;4个作为验证集样品,用于校正模型的验证。将待测样品放入样品杯中,利用GSA201型近红外光谱仪采集样品的光谱。得到样品的化验值,将光谱数据和化验值导入到NIRSA化学计量学软件,经过一阶微分S-G平滑处理,利用成分的含量数据和光谱数据一一对应,创建校正模型。为验证模型的预测能力,选取4个果糖水溶液样品作为验证,调用校正模型对该样品进行预测。结果本研究中样品的含量范围在0.4171%～0.4431%之间,样品的含量比较低且范围较窄,GSA近红外光谱仪测量的绝对偏差为0.000024。结论 GSA近红外光谱仪利用光谱数据和校正模型完全能够有效检测水溶液中果糖的含量,且预测的准确度较高。     

Non-destructive evaluation of the internal fruit quality of black mulberry (Morus nigra L.) using visible-infrared spectroscopy and genetic algorithm

The present study aims at assessing a non-destructive optical method for determining the internal quality of mulberry juice. First, UV-IR spectra (300–1100 nm) were acquired for each sample with a spectral resolution of 2 nm. Then the calibration set, composed of 70 and 38 samples, was used as the validation set. Next, two types of pre-processing multiplicative scatter correction)MSC(and standard normal variate)SNV(were used before analysing the data with multivariate calibration methods of PLS and GA-PLS. The following results were obtained including the correlation coefficient (r) of 0.94, 0.89, 0.98, and 0.95 and root mean square error of validation (RMSEV) 1.31°Brix, 0.52, 0.014, and 0.014 for total soluble solid (TSS), titratable acidity (TA), ascorbic acid (AA), and total anthocyanin content (AC) of mulberry juice, respectively. After that, GA-PLS was applied to select the efficient spectral regions. Model using 31 variables and 15 factors presented and yielded the best prediction results. The best combinations of 21 spectral intervals among 40 intervals selected by GA-PLS also yielded good results with RMSEV = 0.03096, r = 0.96; RMSEV = 0.062, r = 0.98, RMSEV = 0.041, r = 0.98; RMSEV = 0.0006, r = 0.96 for TSS, AA, TA, and AC of mulberry juice, respectively. Finally, it was concluded that using the UV-IR measurement technique, spectral range (300–1100 nm), it was possible to assess the quality characteristics of mulberry juice.     

高光谱成像技术结合线性回归算法快速预测鸡肉掺假牛肉

采用近红外高光谱成像技术(900~1700 nm)结合线性回归算法对牛肉掺假快速无损检测。将鸡肉糜掺入牛肉糜中制备牛肉掺假样品,掺假比例为2%~98%(w/w),掺假间隔为2%。采集掺假样品的光谱图像,提取光谱数据,并利用偏最小二乘回归(Partial least squares regression,PLSR)和多元线性回归(Multiple linear regression,MLR)算法建立掺假样品的定量预测模型。为了减少高维共线性问题,提高模型运算效率,分别采用PLS-β系数法、逐步回归法(Stepwise)和连续投影算法(Successive projection algorithm,SPA)筛选最优波长建立优化预测模型。结果表明,基于SPA算法结合MLR建模方法得到的掺假牛肉预测模型,其预测效果最优,校正集决定系数(R_C2)和均方根误差(Root mean square error of calibration,RMSEC)分别为0.99和3.23%,验证集的决定系数(R_P2)和均方根误差(Root mean square error of prediction)RMSEP分别为0.97和5.31%,预测偏差(Residual predictive deviation,RPD)为6.82。综上,近红外高光谱成像技术结合线性回归算法可以实现对掺假牛肉的快速无损定量检测。     

A Fourier Transform Infrared Spectroscopy Method for Analysis of Palm Oil Adulterated with Lard in Pre-Fried French fries

Fourier transform infrared spectroscopy with attenuated total reflectance accessory was used to detect the presence of lard in French fries pre-fried in palm oil adulterated with lard. A Fourier transform infrared calibration model was obtained using partial least squares for prediction of lard in a blend mixture of lard and palm oil. The coefficient of determination (R²) of 0.9791 was obtained with 0.5% of detection limit. The error in calibration expressed with root mean square error of calibration was 0.979%. In addition, the error obtained during cross validation was 2.45%. A discriminant analysis test was able to distinguish between fries samples adulterated with lard and samples, which were pre-fried with palm oils. Fourier transform infrared spectroscopy is a fast and powerful technique for quantification of lard present in French fries.      

Hot topic: Pathway confirmed for the transmission of melamine from feed to cow's milk

Eight lactating Holstein cows were randomly allotted to 2 groups in a trial to establish whether a pathway exists for the transmission of melamine from feed to milk. All cows received oat hay ad libitum and 15 kg of concentrate pellets per cow daily. The concentrate pellets contained either melamine-contaminated corn gluten meal of Chinese origin (melamine treatment) or locally produced melamine-free corn gluten meal (control treatment). Cows in the melamine treatment ingested 17.1 g of melamine per day. Cows were milked twice daily, and milk samples were taken once daily during the afternoon milking for melamine and milk component analyses. Melamine appeared in the milk within 8 h after first ingestion of the melamine containing pellets. Melamine concentration reached a maximum of 15.7 mg/kg within 56 h after first ingestion, with an excretion efficiency of approximately 2%. Milk solids and milk urea nitrogen were not affected by treatment. The melamine concentration dropped rapidly after changing all cows back to the control pellets, but melamine only declined to undetectable levels in the milk more than 6 d (152 h) after last ingestion of melamine. Results from the current trial are important to the feed and dairy industries because, until now, any melamine found in milk and milk products was attributed only to the deliberate external addition of melamine to these products, not to adulterated ingredients in animal feeds.