Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.     

LiNi0.8Co0.2-XMXO2的合成和电化学性能研究

锂离子电池手机、笔记本电脑、电动汽车技术、医疗仪器电源以及宇宙空间等领域。目前商品化锂离子电池正极活性材料主要是LiCoO2、LiNiO2、LiMn2O4,及其掺杂化合物。LiNiO2具有比容量高,功率大、价格适中等优点,但也存在合成困难,热稳定性差等问题,其实用化进程一直比较缓慢。     

A hierarchical screening method for detection of illegal adulterants in Fur seal ginseng pills by profiling analysis of HPLC multi‐dimensional fingerprints

A novel method for hierarchical screening of illegal adulterants in Fur seal ginseng pills products was developed by multi‐dimensional fingerprint profiling analysis. Fingerprint feature of the samples was acquired by high‐performance liquid chromatography analysis of 11 batches of samples with diode array detector and fluorescence detector, and then potential illegal adulterants including phosphodiesterase type‐5 inhibitors, androgens, α receptor antagonists and yohimbine, were further separated at multiple wavelengths to reduce or remove interferences from sample matrix for highlight their chromatographic characteristics. Accordingly, a hierarchical screening strategy was designed by first‐order and second‐order fingerprints combined with spectral examination to achieve high accuracy and reliability. The method was successfully applied to screening of illegal adulterants in real samples, and it also exhibited good quantification performance through validation tests. From 16 batches of samples, three suspected samples were confirmed to be positive, containing 9.37μg/g of testosterone, 18.8 μg/g of tadalafil, and 48.5 μg/g of sildenafil, respectively. The recoveries and relative standard deviations were in the range of 83.6–103.1% and 4.2–6.8%, respectively. The proposed method provided a simple, efficient and promising alternative to monitoring functional foods.     

消费者预防在线销售掺假行为的博弈模型及最优策略

建立了描述消费者与带掺假行为的在线零售商之间相互博弈的双层规划模型,其中消费者为领导者,在线零售商为随从者.消费者预防在线销售掺假行为的两种策略是进行商品品质检查和采用延期付款,在线零售商则依据消费者的预防策略决定是否销售掺假商品.根据消费者和在线零售商的可能采用的策略,对模型分四种情形展开分析与讨论,并分别在不同情形下得到了消费者与在线零售商的最优决策.结果表明,消费者延期付款的最优时间和进行商品品质检查能有效遏制在线零售商掺假行为.     

基于消费者行为的食品溯源信息监管策略研究

溯源信息真实性直接影响食品可溯源体系建设和消费者对食品安全信心。本文基于消费者行为理论,构建溯源信息掺假与不掺假以及掺假比例大小、政府监管力度强弱情形时企业最优定价决策模型。结果表明,无论消费者购买可溯源产品的溯源信息量高低,企业均选择溯源信息掺假,此时政府两种策略:一是加强监管力度和惩罚力度,迫使企业不敢溯源信息掺假;二是提升产品低溯源信息程度,缩小高低溯源信息程度差异,促使企业无可掺假信息空间。通过高低溯源信息程度对企业最优决策影响研究,低监管情形下企业最优决策受到高低溯源信息程度影响较为显著,高监管情形下企业最优决策受其影响较小。     

Targeted and nontargeted screening and identification of 50 antihypertensive adulterants in dietary supplements and herbal medicines using quadrupole–orbitrap high resolution mass spectrometry with compound database

In the present work, a novel database of drug compounds and a rapid screening method based on ultra‐high performance liquid chromatography coupled to high resolution orbitrap mass spectrometry were developed and applied in the screening and identification of targeted and nontargeted antihypertensive adulterants in dietary supplements and herbal medicines. The established screening database includes retention time, exact mass, fragments, isotopic pattern, and MS² spectra library of the target compounds and thus provides automated search and identification of the targets with a single injection. The nontargeted compounds in the samples are identified through the full MS scan and MS² data by using the Chemspider database and the data analysis in XCalibur, MassFrontier and TraceFinder software. In addition, this method possesses excellent quantitative capacity. The novel approach was applied to 65 batches of samples that are claimed as “all‐natural” products having the antihypertensive function, among which nine batches were found to be positive. Multiple targeted and nontargeted antihypertensive adulterants were detected at levels ranging from 2.8 to 27.9 mg/g. The novel database and screening method demonstrated herein will be promising and powerful tools for rapid screening of antihypertensive adulterants in dietary supplements and herbal medicines.     

核磁共振技术结合化学计量学方法用于蜂蜜的掺假鉴别

采用核磁共振技术(NMR)结合化学计量学分析手段研究了真蜂蜜和掺假蜂蜜的指纹图谱变化情况。采用无监督的主成分分析(PCA)和有监督的偏最小二乘判别分析(PLS-DA)、正交偏最小二乘判别分析(OPLS-DA)等多元统计分析方法从核磁信号中提取各组的分类信息。结果表明:建立的OPLS-DA模型能够区分真假蜂蜜,所建模型对蜂蜜真假判别的解释能力为90.5%,对未知样本的预测能力为75.5%、识别率为89.7%。置换测试验证表明,化学计量学模型具有很好的稳定性和预测性,可信赖性强,且模型稳健。通过OPLS-DA模型的载荷图和相关系数分析找到了对区分掺假蜂蜜有显著作用的标志物。结合相关系数分析,建立了辨别真假蜂蜜的多元线性回归方程。该方法可简单、快速地用于未知蜂蜜的掺假鉴别,为规范蜂蜜市场提供有利的依据。     

基于代谢组学的食品真实属性鉴别研究进展

随着食品工业的快速发展以及生活水平的提高,人们对食品的质量安全提出了更高的要求,市场上的食品掺假造假现象也日益受到社会的关注。目前,主要的食品掺假手段包括假冒物种及品种、冒充或虚标原产地、原料品质以次充好、掺入杂劣质及违禁原料等。因此亟须建立切实有效的食品真实属性鉴别方法。近几年来,国内外一些学者开始将代谢组学研究平台应用于解决食品安全问题的研究,对食品中尽可能多的代谢产物从整体角度进行定性定量分析,为食品真实属性鉴别研究提供了一种新的研究工具。该文综述了基于代谢组学的食品物种及品种鉴别、产地溯源、品质分级和掺假掺杂识别等真实属性鉴别研究,为进一步保证食品质量安全、保障消费者利益提供了技术支撑。     

Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration

We have investigated the potential of Raman spectroscopy with excitation in the visible spectral range (VIS Raman) as a tool for the classification of different vegetable oils and the quantification of adulteration of virgin olive oil as an example. For the classification, principal component analysis (PCA) was applied, where 96% of the spectral variation was characterized by the first two components. A significant similarity between sunflower oil and extra‐virgin olive oil was found using this approach. Therefore, sunflower oil is a potential candidate for adulteration in most commercially available olive oils. Beside the classification of the different vegetable oils, we have successfully applied Raman spectroscopy in combination with partial least‐squares (PLS) regression analysis for very fast monitoring of adulteration of extra‐virgin olive oil with sunflower oil. Different mixtures of extra‐virgin olive oil with three different sunflower oil types were prepared between 5 and 100% (v/v) in 5% increments of sunflower oil. While in the present context the adulteration usually refers to the addition of reasonable amounts of the adulterant (given the similarity with the basic product), we show that the technique proposed can also be used for trace analysis of the adulterant. Without using techniques like surface‐enhanced Raman scattering (SERS), a quantitative detection limit down to 500 ppm (0.05%) could be achieved, a limit irrelevant for adulteration in commercial terms but significant for trace analysis. The qualitative detection limit even was at considerably lower concentration values. Based on PCA, a clear discrimination between pure extra‐virgin olive oil and olive oil adulterated with sunflower oil was achieved. The adulterant content was successfully determined using PLS regression with a high correlation coefficient and small root mean‐square error for both prediction and validation. Copyright © 2009 John Wiley & Sons, Ltd.