Contamination of animal feed and food from animal origin with mineral oil hydrocarbons

Oils and fats used for the production of animal feed can become contaminated with mineral oil material originating from gas oils (C₁₈-C₃₅) or synthetic oils (poly alpha olefins, C₂₅ to beyond C₄₅). An important cause is assumed to be the discharge of waste oils, such as motor oil and hydraulic oils. Mineral oil material was analysed by on-line LC-GC-FID directly in the fat or in a raw extract from animal feed or foodstuffs. In Switzerland in summer/autumn 1999 concentrations in oils and fats for feed production were often found to be between 100 and 1000mg/kg. In the feeds, the average concentration was around 100mg/kg with values ranging up to a maximum of 1000mg/kg; few samples were free of contamination. In animal body fat, the average concentration determined in summer 1999 was 25mg/kg, with a maximum of 150mg/kg, although in samples from December 1999, contamination was substantially lower. In the fat phase of eggs, the average concentration was 30mg/kg, with a maximum of 80mg/kg. Paraffin oil is used for feed production, which may account for part of the contamination problem (e.g. eggs).     

大豆中异黄酮含量的测定及其近红外分析

以100份中国核心大豆种质资源为材料,建立高效液相色谱法(HPLC)测定大豆中异黄酮5组分和总异黄酮。扫描大豆的近红外光谱,以傅里叶近红外光谱法(FT-NIRS)与HPLC技术相结合,采用偏最小二乘(PLS)回归和交叉验证法,探讨利用FT-NIRS技术预测异黄酮含量的可行性。总异黄酮近红外预测模型的内部交叉验证其校正决定系数(R2)和交叉检验均方误差(RMSECV)分别为0.8763和0.515,外部验证其校正决定系数(R2)和预测均方误差(RMSEP)则分别是0.9492和0.599。结果表明,利用FT-NIRS预测大豆中总异黄酮含量是可行的,但是各异黄酮组分的近红外模型不能达到准确预测要求。大豆异黄酮近红外模型的建立对今后大豆的异黄酮选育工作可以提供帮助。     

近红外光谱结合偏最小二乘法用于纯茶油中掺杂菜籽油和大豆油的定量分析

采用偏最小二乘法(PLS)建立了油茶籽油中掺杂菜籽油和大豆油的近红外光谱定量检测模型。配制不同比例(0～100%)的油茶籽油和菜籽油、油茶籽油和大豆油混合样品共256个,采集样品在10000～4000cm-1范围内的近红外透反射光谱,模型采用交互验证和外部检验来考察所建立模型的可靠性,不需进行任何光谱预处理,所建立的PLS模型相关系数为0.9997,训练集的交叉验证均方根误差(RMSECV)为0.504,预测集的预测均方根误差(RMSEP)为0.66。应用建立的模型对未知样品进行预测,并对预测值和真实值进行比较,在掺杂油含量为2.5%～100%之间范围内准确可靠,研究结果表明,采用近红外光谱技术可以实现纯茶油中菜籽油和大豆油掺杂量检测。     

Raman spectroscopy a promising technique for quality assessment of meat and fish: A review

Raman spectroscopy data have been compared to different traditional methodologies such as protein solubility, apparent viscosity, water holding capacity, instrumental texture methods, dimethylamine content, peroxide values, and fatty acid composition commonly used to determine quality in fish and meat muscle treated under different conditions of handling, processing and storage through the changes of proteins, water and lipids of muscle food. It has been shown that Raman spectroscopy data are related to the results obtained with these traditional quality methods and could be used to evaluate muscle food quality. In addition, Raman spectroscopy provides structural information about the changes of proteins, water and lipids of muscle food that occur during the deterioration. Raman spectroscopy can also be used for determining muscle food identification. Besides, this spectroscopy technique has several advantages compared to traditional methods since it is a direct and non-invasive technique which requires small portions of sample.     

拉曼光谱快速鉴别花生油掺棕榈油的研究

本文利用激光拉曼光谱实现了纯花生油中掺棕榈油的快速定性鉴别和定量分析.通过因子分析法的二维因子图,能够定性鉴别纯花生油和掺棕榈油的花生油;通过最小二乘法建立的拉曼光谱相对峰强度（Y）与掺入棕榈油的质量百分数（X）之间的线性方程（Y=-0.233 7X ＋0.506 8,R2=0.995 9）能够实现纯花生油掺棕榈油的定量分析.     

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of 2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients of determination (R²) were found to be 0.89–0.99 depending on the mathematical algorithm used, the data pre-processing applied and the sample type used. The corresponding values for the root mean square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively, again depending on the chemometric treatment applied to the data and sample type. In addition, adopting a qualitative approach with the spectral data and applying PCA, it was possible to discriminate between the four samples types and also, by generation of Cooman’s plots, possible to distinguish between adulterated and non-adulterated samples.     

Nondestructive determination of water and protein in surimi by near-infrared spectroscopy

Nondestructive near-infrared spectroscopy (NIRS) between 400 and 1100 nm regions was employed directly on surimi using a surface interactance fibre optic accessory, to investigate the potential of NIRS as a fast method to determine water and protein contents. The reason why NIRS is well suited when assessing the presence of water or protein is due to the specificity of O–H and N–H bindings. At 980 nm only one broad peak in the original spectra can be seen due to the absorption of water since it contained nearly 80% of surimi. Predictive equations were developed using partial least squares (PLS) regression where excellent predictions for protein and water are noticed. Regression coefficients are higher than 0.98, errors are small and RPD value for protein is well over 8 and that for water is very close to it which can be used for any analytical purpose.     

The development of near-infrared spectroscopy (NIRS) calibration for prediction of ash content in legumes on the basis of two different reference methods

The aim of this study was to develop optimal NIRS calibration for ash content prediction in legumes by using the thermogravimetric (TGA) and gravimetric (GA) analytical methods. The calibration was performed on the basis of whole and structured sample sets (n = 143 and n = 99, respectively). Samples were scanned using a Rapid Content Analyzer in reflectance mode (400–2500 nm). Different mathematical treatments of the spectra preceded modified partial least squares (MPLS) regression analyses. The performance of the models was assessed by cross validation and external validation (n = 44). Models developed for the whole sample set on the basis of the TGA and GA methods were characterised by standard error of calibration (SEC) ranged from 0.28 to 0.50, standard error of cross validation (SECV) ranged from 0.43 to 0.60, coefficient of determination (R²) ranged from 0.97 to 0.89, explained variance (1 − VR) ranged from 0.94 to 0.85 and residual predictive deviation (RPD) ranged from 4.23 to 2.68, respectively. Models developed for the structured sample set on the basis of the TGA and GA methods were characterised by standard error of calibration (SEC) ranged from 0.32 to 0.42, standard error of cross validation (SECV) ranged from 0.53 to 0.56, coefficient of determination (R²) ranged from 0.97 to 0.94, explained variance (1 − VR) ranged from 0.91 to 0.89 and residual predictive deviation (RPD) ranged from 3.52 to 2.98, respectively. The obtained results showed the potential of NIRS method to accurately predict the ash content of legume grass samples that correspond to ash content determined by the TGA and GA methods.     

The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil

Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000–650 cm⁻¹ on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R²) of 0.999.     

The use of ion chromatography to detect adulteration of vodka and rum

The use of ion chromatography to determine anions (chloride, nitrate, and sulphate) for the characterisation of colourless spirits, such as vodka or white rum, is presented. After evaporation to remove the volatiles, the sample was injected directly into the ion chromatograph. The assay showed good precision, never exceeding 1.6% RSD. The analytical results of 51 samples under study reveal that, in particular, the adulteration of brand spirits can be proven by the method described.     

Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples

The potential of NIRS-based models to predict several physical parameters of oxen and young cattle beef protected by a quality mark was evaluated. Fifty-three and 67 samples of Longissimus thoracis muscle corresponding to oxen and young cattle reared in extensive conditions were analyzed for pH, colour (L^∗, a^∗, b^∗), water holding capacity (WHC) and Warner–Braztler shear force (WBSF), according to the conventional methods. Several factors which might have jeopardized the prediction of pH, a^∗, WHC and WBSF by NIRS are considered in the discussion section. However, the best NIR calibrations, tested by full cross-validation, were for L^∗ (R² = 0.869; SE_CV = 1.56) and b^∗ (R² = 0.901; SE_CV = 1.08) colour parameters in meat samples from young cattle. It can be concluded that NIRS could be a useful tool for estimating the colour of young cattle meat samples, primary consideration for consumers when making purchasing decisions.     

Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef

This study aimed to evaluate the potential of near infrared spectroscopy (NIRS) as a fast and non-destructive tool for detecting and quantifying different adulterants in fresh and frozen-thawed minced beef. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R_P²) of 0.96, 0.94 and 0.95 with standard error of prediction (SEP) of 5.39, 5.12 and 2.08% (w/w) for minced beef adulterated by pork, fat trimming and offal, respectively. The performance of the developed models declined when the samples were in a frozen-thawed condition, yielding R_P² of 0.93, 0.82 and 0.95 with simultaneous augments in the SEP of 7.11, 9.10 and 2.38% (w/w), respectively. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and non-linear regression models (logistic, probit and exponential regression) were developed at the most relevant wavelengths to discriminate between the pure (unadulterated) and adulterated minced beef. The classification accuracy resulting from both types of models was quite high, especially the LDA, PLS-DA and exponential regression models which yielded 100% accuracy. The current study demonstrated that the VIS-NIR spectroscopy can be utilized securely to detect and quantify the amount of adulterants added to the minced beef with acceptable precision and accuracy.     

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.     

Development and application of DNA markers to detect adulteration with Scopolia japonica in the medicinal herb Atractylodes lancea

Food Science and Biotechnology - Atractylodes lancea rhizomes are commonly consumed in east Asia as traditional medical herbs. However, in Korea, because of their morphological similarity, A....     

Applicability of NIR spectroscopy to determine oil and other physicochemical parameters in Rosa mosqueta and Chilean hazelnut

Due to the increasing interest in certain components, specially the oil, from non-conventional seeds as Rosa mosqueta (Rosa rubiginosa) and Chilean hazelnut (Gevuina avellana), quick determinations of oil and other parameters were carried out by using near-infrared (NIR) spectroscopy. Moisture, oil, fiber (as acid detergent fiber) and protein from solid samples of the seeds as mentioned, along with those of soybean (Glycine max), already analyzed by NIR and for serving as control for the variability of the method, were studied. Sample interactions to NIR radiations were processed using the multivariate regression algorithm Partial Least Squared (PLS) to build a calibration model. Standard error of cross-validation (SECV) was used to estimate the prediction error. Moisture of Rosa mosqueta, Chilean hazelnut presscake and soybean meal (in the ranges 10–15, 10–15, 8–10%, respectively), acid detergent fiber (60–68, 12–16, 10–15%, respectively), oil (1–4, 14–20, 5–13%, respectively) and protein (1–5, 8–15, 27–45%, respectively) were previously determined by wet analysis using standard methods, so creating a library. The possibility to analyze parameters from very different oilseeds with an acceptable uncertainty was also established. Standard errors of cross-validation were between 1.25 and 2.99%, being the oil content the best predicted parameter.     

Evaluation of minor components,sensory characteristics and quality of virgin olive oil by near infrared (NIR) spectroscopy

Chemometric analyses of NIR spectral data (12,500–4000 cm^? 1) combined with analytical and sensory parameters measured in monovarietal Cornicabra virgin olive oils (VOOs) (n = 97) produced in the PDO (Protected Denomination of Origin) “Montes de Toledo” geographic area were used to generate calibration and validation models in order to be able to predict the minor composition of VOO, in particular its phenolic and volatile compounds as well as its organoleptic attributes, and thereby the global quality of the product. Acceptable multivariate algorithms were obtained for VOOs' degree of unsaturation (fatty acid profile) (r = 0.84–0.96), several minor components – in particular hydroxytyrosol derivatives (r = 0.86–0.88) and C6 alcohols (r = 0.69–0.80) – as well as for positive sensory attributes — ‘fruity’ (r = 0.87) and ‘bitter’ (r = 0.85). The results establish that near infrared is a very useful technique that allows rapid screening of VOO samples to estimate their minor composition and sensory characteristics, and thereby their quality and commercial grade. Furthermore, it may give real time information about olive oil composition during processing in order to automatically control technological parameters.     

Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS)

Near infrared reflectance spectroscopy (NIRS) was evaluated as a tool to segregate different types of bovine meat and predict several chemical fractions on samples from two breeds, three muscles and six grading (Chilean system) categories. Samples previously minced, frozen and thawed, were scanned (400–2500 nm) and then analyzed for dry matter, crude protein, ether extract, total ash and collagen content, after freeze drying. Discriminant analysis using a partial least squares regression technique and cross validation, correctly identified breed and muscle type for most samples, but carcass grades, with the exception of samples from calves, were not successfully predicted. Best calibrations for chemical composition tested by cross-validation, showed R² and standard errors of cross validation of 0.77 and 0.58% (dry matter), 0.82 and 0.48% (crude protein), 0.82 and 0.44% (ether extract). Calibrations for total ash showed a poor, and for collagen, a very poor prediction ability.     

近红外光谱技术在茶叶品质评价中的研究与应用

介绍了近红外光谱技术的原理及其在品质评价方面的研究进展,着重阐述了该技术在茶叶品质评价中的研究概况以及存在的问题,并对该技术在茶叶品质评价中的应用前景进行了分析。     

Raman spectroscopy to assess water holding capacity in muscle from fast and slow growing broilers

Rapid and non-destructive quality methods that can rapidly scan large product numbers in early stages of production are attractive for meat production. Chicken breast muscles from low- and high-growth rate (GR) broilers were segregated into extremes of broilers with weight gain of ≤250 g/wk and ≥299 g/wk, respectively. Breast muscle was also segregated into extremes of low- and high-water holding capacity (WHC) of ≤4.08 g/100 g or ≥ 89.26 g/100 g muscle, respectively. Raman spectroscopy was used to evaluate differences in spectra of the extremes of broilers that differed in GR or muscle that differed in WHC. Spectra was collected at 785 nm excitation, 500 mW laser power, 10 s total integration time. There were 7 significantly different bands (P < 0.05) from growth rate population spectrum: 510, 1270, 1354, 1535, 1652, 1737, 1863 cm⁻¹; and 4 significantly different bands (P < 0.05) from WHC population spectrum: 538, 691, 1367, 1743 cm⁻¹. Differences in the trptophan environment, cysteine and amide I and III structure influenced WHC. The results can potentially be used to rapidly identify muscle attribute variations for customized handling in further processing.