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。     

近红外光谱法用于掺假羊奶的快速无损鉴别

利用近红外光谱技术结合多种化学计量学方法,研究了快速鉴别掺假羊奶的方法。将淀粉溶液,含尿素的淀粉溶液,含尿素和奶油的淀粉溶液按不同比例掺入纯羊奶中,进行近红外光谱采集。分别采用偏最小二乘差别分析(PLS-DA),fisher线性判别和多层感知器(MLP)神经网络法建立校正模型并进行检验验证。结果表明,MLP神经网络的鉴别效果最好,其校正模型的正判率达到99.4%,验证集的正判率达到100%。说明采用近红外光谱技术结合适当的化学计量学方法可以实现羊奶掺假检测的快速无损鉴别。     

Implementation of chemometrics in quality evaluation of food and beverages

Conventional methods for food quality evaluation based on chemical or microbiological analysis followed by traditional univariate statistics such as ANOVA are considered insufficient for some purposes. More sophisticated instrumental methods including spectroscopy and chromatography, in combination with multivariate analysis—chemometrics, can be used to determine food authenticity, identify adulterations or mislabeling and determine food safety. The purpose of this review is to present the current state of knowledge on the use of chemometric tools for evaluating quality of food products of animal and plant origin and beverages. The article describes applications of several multivariate techniques in food and beverages research, showing their role in adulteration detection, authentication, quality control, differentiation of samples and comparing their classification and prediction ability.     

Feature Selection and Recognition from Nonspecific Volatile Profiles for Discrimination of Apple Juices According to Variety and Geographical Origin

Abstract: Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid‐phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography‐mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. Practical Application: This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS‐SPME‐GC‐MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product.     

气相色谱法测定猪肉馅中鸭肉掺杂比例

目的:有效打击市场上猪肉馅掺杂行为。方法:基于气相色谱法分析不同肉类中的特征脂肪酸种类以及含量,手动建立猪肉、鸭肉的标准指纹图谱,分析猪肉馅中掺入10%,30%,50%,70%的鸭肉掺杂样品色谱图;对掺杂试验进行模拟,建立掺杂量0%,10%,30%,40%,50%,60%,70%,80%,90%,100%与掺杂肉样的特征脂肪酸的预测回归模型,并对不同掺杂量的肉馅进行验证实验。结果:试验回归预测模型的R2值＞0.99,在验证实验过程中,掺杂样品的最低检出限为0.5%。结论:试验建立的掺杂预测模型有很好的精确度和准确度。     

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.     

Application of Mid-infrared Spectroscopy for the Measurement of Several Quality Parameters of Alcoholic Beverages, Wine and Raki

Mid-infrared (IR) spectroscopy, which is a rapid and relatively small amount of waste producing technique, was used to predict several quality parameters of two types of alcoholic beverages, wine and raki. Mid-infrared spectra of red, rose and white wines and a traditional aniseed alcoholic beverage, raki were collected and relations were established between measured chemical parameters (pH, brix, total phenol content, anthocyanin content, titratable acidity, sugar content, electrical conductivity and some colour parameters) of these beverages and their infrared spectra using chemometric techniques. Partial least square regression provided excellent prediction of total phenol (R ²?=?0.97) and anthocyanin contents (R ²?=?0.98) of wine samples and a good prediction of pH (R ²?=?0.9), brix (R ²?=?0.92) and colour intensity (R ²?=?0.93) values were obtained. Brix, total phenol and sugar content of raki samples were also estimated very successfully (R ²?=?0.99) for raki and good prediction was obtained with pH value. Mid-IR spectroscopy in combination with chemometrics could be a promising technique for determination of several quality parameters of alcoholic beverages simultaneously and rapidly.     

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.     

Technical note: Nontargeted detection of adulterated plant proteins in raw milk by UPLC-quadrupole time-of-flight mass spectrometric proteomics combined with chemometrics

We built and validated a chemometric model to detect possible milk adulteration with plant proteins. Specifically, we extracted proteins in raw milk, treated with tryptic digestion, and obtained peptide fingerprints by UPLC-quadrupole time-of-flight-mass spectrometry with proteomics to differentiate authentic milks from their counterparts adulterated with nonmilk proteins. This approach is able to detect soybean and pea powder-adulterated milks at as low as 1% (wt/wt). Additionally, we obtained the characteristic peptide sequences for milk authentication by principal component analysis. The prediction accuracies for milk authentication by partial least-squares-discriminant analysis were greater than 95%. These results indicated that peptide fingerprints with the chemometric analysis could be successfully applied for milk quality control.     

GC和LF-NMR结合化学计量学方法检测掺假油茶籽油

为了对油茶籽油品质控制及评价提供支撑,以纯油茶籽油和掺假油茶籽油(分别掺入菜籽油、花生油、棕榈油和高油酸花生油)为试验材料,采用气相色谱法(GC)分析其脂肪酸组成,采用低场核磁共振技术(LF-NMR)测定其横向弛豫特性数据,结合主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)和偏最小二乘分析(PLS)等化学计量学方法建立油茶籽油掺假的定性和定量分析模型。结果表明：5种植物油的脂肪酸组成和LF-NMR横向弛豫特性数据存在显著区别;油茶籽油和其他4种植物油在PCA得分图上可清晰区分;PLS-DA模型可有效区分油茶籽油和掺假油茶籽油,判别正确率均可达100%;建立的油茶籽油中掺入菜籽油、花生油、棕榈油、高油酸花生油的PLS定量预测模型,真实值与预测值的相关系数(R²)分别为0.994 1、0.998 6、0.997 6、0.978 1。综上,GC和LF-NMR结合PCA、PLS-DA以及PLS等化学计量学方法可用于油茶籽油掺假类别判定及掺假量分析。     

High-power gradient diffusion NMR spectroscopy for the rapid assessment of extra-virgin olive oil adulteration

A high gradient diffusion NMR spectroscopy was applied to measure diffusion coefficients (D) of a number of extra-virgin olive, seed, and nut oils in order to ascertain the suitability of this rapid and direct method for discrimination of adulterated olive oils. Minimum adulteration levels that could be detected by changes in D were 10% for sunflower (SuO) and soybean oil (SoO), and 30% for hazelnut (HO) and peanut oil (PO). Qualitative and quantitative prediction of adulteration was achieved by discriminant analysis (DA). The highest prediction accuracy (98–100%) was observed only when two DA models were concomitantly used for sample classification. The first DA model provided recognition of high adulterated EVOO with more than 20% of SuO or SoO, and 30% with PO, whilst the second model could differentiate EVOO adulterated with 10% of SuO or SoO, and more than 30% of HO. The validation test performed with an independent set of randomly adulterated EVOO samples gave 100% classification success. The high accuracy levels together with minimal requirements of sample preparation, and short analyses time, prove the high-power gradient diffusion NMR spectroscopy as an ideal method for rapid screening of adulteration in valuable olive oils.     

An Improved and Validated Sample Cleanup Method for Analysis of Ethyl Carbamate in Chinese Liquor

Ethyl carbamate (EC) is a potential human carcinogen widely existing in fermented foods and alcoholic beverages. The solid‐phase extraction (SPE) coupled to gas chromatography mass spectrometry is a widely‐used method to determine EC levels, but the accuracy varies with sample matrix and the effects of operation parameters are rarely examined. In this study, the influence factors involved in EC determination were investigated using Chinese liquor as sample matrix, and the improved method was further applied. Three types of SPE columns, including diatomite, Florisil, and primary‐secondary amine, were compared in extraction efficiency, and the diatomite column exhibited the highest extraction efficiency. The optimal volumes of elution solvents with diatomite column were 15 mL for 3‐mL samples solution loaded. In addition, the alcoholic strength for EC determination should be diluted below 20% (v/v) to avoid the enhancement of matrix‐induced chromatographic response. Moreover, the pH neutralization could help improve EC recovery and peak resolution, reducing interfering effects. Based on these results, the improved method showed that the limit of detection, the limit of quantification, and average recoveries were 1.10 μg/L, 3.65 μg/L, and 93.06%, respectively. To further elucidate the underlying factors related to EC accumulation, partial least square regression analysis was conducted, and the results suggested that EC levels had the closest relationship with alcoholic strength among the remaining precursors.     

The profile in polyphenols and volatile compounds in alcoholic beverages from different cultivars of mulberry

Abstract: Mulberry fruits have different chemical compositions because of cultivar variation. In this study, polyphenol profile and volatile composition of alcoholic beverages produced from different mulberries of Da 10, Hongguo 2 hao, and Hongguo 1 hao were investigated. Statistically significant differences were detected in the chemical composition of the analyzed alcoholic beverage samples. The beverage of Da 10 possessed the highest content of total phenols, total flavonoids, and total anthocyanins, which was determined by spectrophotometry analysis, contrarily Hongguo 1 hao beverage showing the lowest. Phenolic acids and flavonols were analyzed by high performance liquid chromatography analysis. Protocatechuic acid was the main phenolic acid in all analyzed alcoholic beverage samples, and its concentration ranged from 8.20 mg L^?1 (Hongguo 1 hao beverage) to 29.98 mg L^?1 (Da 10 beverage). High level of flavonols was found in both Da 10 and Hongguo 2 hao beverage samples, whereas Hongguo 1 hao beverage sample was characterized with lower level of flavonols. Furthermore, volatile compounds were analyzed by solid phase microextraction‐gas chromatography mass spectrometry analysis. The amount of esters varied widely among the 3 alcoholic beverage samples. The concentration of aliphatic alcohols in Hongguo 2 hao and 1 hao beverage samples was higher than Da 10 sample, whereas Da 10 beverage sample contained more aromatic alcohols than the others. Hongguo 1 beverage sample showed the highest concentration of acetic acid. This study demonstrated that quantitative differences of phenolic compounds and volatile compounds among cultivars were significant, which highly affected the quality of alcoholic beverage from mulberry. Practical Application: Mulberry fruits had different chemical compositions because of cultivar variation. The analyzed Da 10 cultivar of mulberry appeared to be a good raw material for alcoholic beverage‐making because of its high level of total sugar and acidity. This study demonstrated that alcoholic beverages from mulberry contain high amounts of polyphenols. These bioactive compounds may act synergistically to promote health, and reduce risk factors of chronic diseases.     

电子鼻快速检测区分羊肉中的掺杂鸡肉

为实现掺假羊肉的快速、客观检测,利用电子鼻定性和定量分析混入鸡肉的掺假羊肉糜。单因素试验表明顶空体积、载气流速、样品量和顶空生成时间对电子鼻传感器的响应影响极显著;主成分分析确定了电子鼻检测的较佳条件：样品量10 g、载气流速200 mL/min、顶空容积250 mL及顶空生成时间30 min。在此条件下检测混入鸡肉的掺假羊肉,结果发现采用主成分分析时,掺入鸡肉的比例随主成分一降低而增大,但相邻比例彼此重叠,难以有效区分;采用典则判别分析时,混入不同比例鸡肉的羊肉糜样品能较好地区分;采用主成分回归分析和偏最小二乘回归分析建立的定量预测模型（R2>0.95）能有效预测混入的鸡肉比例。电子鼻在混入鸡肉的掺假羊肉鉴别中具有可行性,论文可为羊肉掺假鉴别提供理论依据。     

An Automated Single Interface Flow System for the Spectrophotometric Determination of Ethanol in Beverages Based on Schlieren Effect

A new and expeditious automated procedure for the determination of ethanol content in alcoholic beverages (distilled liquors and wine) by using a single interface flow analysis (SIFA) system with spectrophotometric detection is presented. The developed approach was simply based on refractive index gradient measurements, known as Schlieren effect, produced when acidified beverages samples were introduced in a single-line SIFA system that used deionized water as carrier (trailing) stream and a computer-controlled spectrophotometer as detector. In the implemented SIFA system, no chromogenic reagent is used, being the refractive index gradient a consequence of the mutual interdispersion of sample and carrier zones at the formed single interface, and no predefined volumes of both sample and carrier are required, which greatly simplifies system operation and optimization. The proposed methodology allowed the determination of ethanol concentrations between 4% and 24% (v/v) with good precision (RSD < 5.2%, n = 3) and a sampling frequency of about 58 h⁻¹ with consumption of 400 μl of sulfuric acid 0.5 mol l⁻¹ and 400 μl of sample per measurement. The developed procedure was applied to whisky, gin, rum, vodka, the sugar cane-based beverage “cacha?a,” and white wine, and the obtained results were in a good agreement with those provided by the reference method (RD < 5.0%). Recovery values acquired in the analysis of spiked ethanol samples (between 90.0% and 111.0% of the added amount) were also satisfactory.     

紫外光谱结合化学计量学检测初榨橄榄油掺伪研究

以紫外光谱为技术手段,结合偏最小二乘法和BP人工神经网络2种化学计量学方法建立了初榨橄榄油/混合橄榄油二元掺伪体系的定量预测模型.试验结果表明,2种统计模型定量预测性能良好,偏最小二乘模型的训练集交叉验证均方根误差RMSEcv和预测集均方根误差RMSEP均达到0.011,预测值与真实值相关性达到0.996 2;BP人工神经网络迭代次数为61步,训练集拟合残差为9.684×10-5,网络预测值和真实值相关系数为0.998 3,对于5％以上掺伪比例的油样BP神经网络能够精确地预测.     

枇杷蜂蜜中饴糖的近红外光谱检测

实验通过对纯枇杷蜂蜜及主动掺入1%、2%……30%饴糖的假枇杷蜂蜜进行近红外光谱扫描,采用TQAnalysisv6对数据进行预处理,建立饴糖含量的定性及偏最小二乘法和主成分回归法定量分析模型,并将模型应用于蜂蜜样品的分析预测。结果显示,采用原始光谱或一阶微分处理建立的判别分析模型均能够较好地区分掺饴糖蜂蜜与纯蜂蜜。根据PLS算法、PCR算法建立的定量模型相关系数分别为0.99771、0.98654,用于预测的蜂蜜样品实际值与预测值之间的决定系数分别为0.992、0.974。由此可见,用近红外光谱技术鉴别蜂蜜中是否添加饴糖是可行的,在实际操作中可以采用近红外光谱法快速定性判别蜂蜜中是否含有饴糖,也可根据化学计量法确定饴糖的含量,为蜂蜜打假提供依据。     

基于近红外光谱的酸枣仁不同伪品掺假检测

本文采用近红外光谱技术对酸枣仁及其三种常见伪品理枣仁、枳椇子和兵豆进行定性定量检测研究。分别制备不同伪品掺杂质量分数为1%~90%的单种掺杂物实验样品,以及多种伪品同时掺杂的样品,采集800~2500 nm范围的近红外光谱数据。首先利用主成分分析（principal component analysis,PCA）对酸枣仁及三种伪品进行初步定性鉴别。对于单一掺假物样品,采用五种不同预处理方法对光谱数据进行去噪。利用偏最小二乘回归（partial least squares regression,PLS）方法,建立PLS1模型定量预测掺假物含量,并采用连续投影算法（successive projection algorithm,SPA）挑选最优波长,优化定量模型。结果表明,理枣仁掺假建立的3波长检测模型的预测集决定系数R2_p为0.9659,均方根误差（root mean square error,RMSEP）为6.1910%。枳椇子掺假建立的8波长检测模型的预测集决定系数R2_p为0.9491,均方根误差（RMSEP）为7.6232%。兵豆掺假建立的5波长检测模型的预测集决定系数R2_p为0.9666,均方根误差（RMSEP）为6.1437%。对于多掺杂物样品,建立了PLS2模型同时对不同成分进行定量预测,酸枣仁效果最好,R2_p≥0.7115,枳椇子预测效果最差,R2_p≥0.2007。研究表明,利用近红外光谱技术可以实现酸枣仁不同伪品掺假的快速无损检测。所建方法为后续酸枣仁及其他种子类中药材便携式无损检测仪器的开发提供了理论基础与参考依据,对保证中药材质量安全具有重要社会意义。     

Application of real-time refrigeration turbidimetry for rapid discrimination and purity assessment of refined peanut oils adulterated with other seed oils

Real-time refrigeration turbidimetry and common chemometric methods, including principal component analysis (PCA) and partial least squares (PLS), were applied to the discrimination and purity assessment of some refined peanut oils adulterated with other seed oils. A special turbidimeter was developed, and the working temperature fluctuation could be lower than ±0.25 °C. The curves of turbidance versus refrigeration time of RPO samples were constructed and analyzed by PCA and PLS-1. Discrimination of pure RPO and adulterated samples was successfully achieved by calculating and comparing the Euclidean or Manhattan distances of their first two principal components obtained by PCA. The PLS-1 was applied to the purity assessment of RPO samples. The correlation coefficient and root-mean-square error of prediction were 0.94 and 2.75%, respectively. Forty-six RPO samples were analyzed by the proposed method and gas chromatography (GC). The analytical results obtained show that only one adulterated sample confirmed by PCA was misjudged. The absolute errors of the purity assessment results obtained by PLS-1 relative to those obtained by GC were less than 4%.     

HPLC analysis and safety assessment of coumarin in foods

Coumarin is a component of natural flavourings including cassia, which is widely used in foods and pastries. The toxicity of coumarin has raised some concerns and food safety authorities have set a maximum limit of 2 mg/kg for foods and beverages in general, and a maximum level of 10 mg/l for alcoholic beverages. An efficient method for routine analysis of coumarin is liquid chromatography with diode array detection. The optimal sample preparation for foods containing cinnamon was investigated and found to be cold extraction of 15 g sample with 50 mL of methanol (80%, v/v) for 30 min using magnetic stirring.