UV-Visible Spectroscopy and Multivariate Classification as a Screening Tool to Identify Adulteration of Culinary Spices with Sudan I and Blends of Sudan I + IV Dyes

This work propose a feasible, rapid, and simple method for detecting culinary spices adulterated either with Sudan I dye or blends of Sudan I + IV dyes at three concentration levels. The method is based on the use of UV-visible spectroscopy with multivariate analysis. Four types of spices were studied: three paprika varieties (mild, hot, and smoked) and a spice commonly consumed in Argentina called aji molido. Principal components analysis was firstly applied as an exploratory analysis and then, two classification techniques were used: K-nearest neighbors (KNN) and partial least squares-discriminant analysis (PLS-DA). Three classes were defined: unadulterated samples and adulterated samples with Sudan I or blends of Sudan I + IV dyes at 1, 2.5, and 5 ppm (mg L^?1). Classification techniques gave satisfactory results: between 89 and 100 % for PLS-DA and between 83 and 92 % for KNN. The sensitivity and specificity of the models were above 83 %. It has to be highlighted that none of the adulterated samples were assigned as unadulterated, which is very positive because of the implication that these results have on consumer health. The capability of detecting mixtures of Sudan dyes is a very important advantage because each Sudan dye generates different hazardous metabolites in human body so their toxicity may be enhanced by the simultaneous presence of such dyes.     

Monitoring of Adulteration and Purity in Coconut Oil Using Raman Spectroscopy and Multivariate Curve Resolution

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution–alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of coconut oil. Sunflower, soybean, canola, sesame, corn, castor bean, peanut, palm kernel, babassu, mineral, and Vaseline oils have been used as adulterants in this work. Control charts were developed to evaluate the purity of an oil sample using the scores from the MCR-ALS analysis of a data set containing pure and adulterated oils. These control charts were able to detect the adulteration of coconut oil in a range of 2–30% with all the oils tested. Additionally, quantification models were developed using MCR-ALS with correlation constraints for coconut oil adulterated with sunflower, canola, Vaseline, babassu, and palm kernel oils. The models presented satisfactory results, which had absolute errors below 5%, for samples adulterated with sunflower, canola, and Vaseline oils. The babassu and palm kernel adulterants could also be quantified with a superior margin of error. The results indicated that using Raman spectroscopy with MCR is a clean and non-destructive method for assessing coconut oil purity that can be used without removing a sample from its bottle.     

Rapid Determination of Invert Cane Sugar Adulteration in Honey Using FTIR Spectroscopy and Multivariate Analysis

ABSTRACT: Fourier transform infrared spectroscopy with an attenuated total reflection sampling accessory was combined with multivariate analysis to determine the level (1% to 25%, wt/wt) of invert cane sugar adulteration in honey. On the basis of the spectral data compression by principal component analysis and partial least squares, linear discriminant analysis (LDA), and canonical variate analysis (CVA), models were developed and validated. Two types of artificial neural networks were applied: a quick back propagation network (BPN) and a radial basis function network (RBFN). The prediction success rates were better with LDA (93.75% for validation set) and BPN (93.75%) than with CVA (87.50%) and RBFN (81.25%).     

基于紫外光谱的花生油掺伪检测

为建立一种花生油掺伪检测方法,利用花生油和其他食用油在紫外光谱特征吸收峰不同,建立定量检测模型及并对模型进行验证分析。结果表明：花生油与其他食用油在280nm处特性吸收存在差异性,利用这一特性建立花生油掺伪定量分析模型,其方程为y＝46.72x－27.51(y为掺伪比例/%;x为吸光度),相关系数为0.9779,标准差为3.099%。盲样验证预测值与实际掺伪值高度线性相关,相关系数为0.9841,标准差为3.849%,检出限约为10%,相对误差均小于15%。紫外光谱花生油掺伪快速检测方法是可行的。     

Classification of Barley according to Harvest Year and Species by Using Mid-infrared Spectroscopy and Multivariate Analysis

In order to monitor malt quality in the malting industry, despite yearly variations in the barley quality, 394 barley samples were analysed using conventional （moisture, protein and B-glucan content） and mid-infrared Fourier transform spectroscopy FT-IR. The experimental dataset included barley from three harvest years, two barley species, 77 barley varieties, and two-row and six-row barley, from 16 cultivation sites. For each sample, the malt quality indices were also assessed according to European Brewing Convention （EBC） standards. Principal component analysis （PCA） was carried out on mean-centred, normalized and derivative spectra using 200/cm width spectral bands. The most informative spectral bands were observed in the 800-1,000/cm and 1,000-1,200/cm ranges. PCA revealed that barley harvested in 2010 and in 2011 had bands that were very close together, while 2009 harvest clearly displayed a difference in its quality. PCA made it possible to distinguish two species and confirmed that two-row winter barley quality was closer to two-row spring barley quality than to six-row winter barley. Results indicate that mid-infrared spectrometry （MIR） could be a very useful and rapid analytical tool to assess barley qualitative quality.     

Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis

Milk is one the most widely consumed foods in the world, with an average annual production of 723 million tons in the second half of the past decade. However, to increase milk’s profitability, some actors in the dairy chain may adulterate it, altering its chemical composition, and reducing the nutritional value of this food. The quality of milk is therefore assessed through chemical and physical analyses such as total dry matter, total Kjeldahl nitrogen, ash, acidity, fat, reducing sugars, depression of freezing point, and relative density. In this work, we have used Principal Components Analysis (PCA) and supervised methods (PLS-DA, SIMCA, kNN, and SVM-DA) to explore physicochemical data from milk and to classify and discriminate between samples that were compliant or not to the parameters set in the Brazilian Regulation for the Inspection of Animal Products and other national regulations. Classification results regarding specificity and selectivity for PLS-DA, SIMCA, kNN, and SVM-DA for noncompliant samples in the test group were, respectively, the following: 91, 88; 100, 97; 78, 67; 78, and 71%.     

近红外光谱结合化学计量学研究芝麻油的真伪与掺伪

应用傅里叶变换近红外光谱(FTNIR)结合化学计量学分别建立了芝麻油的真伪鉴别与掺伪定量的快速分析方法。真伪鉴别分别采用FTNIR结合主成分分析-簇类软独立模式识别(PCA-SIMCA)和偏最小二乘法-人工神经网络(PLS-ANN),建立了芝麻油、大豆油、花生油、葵花籽油的分类模型。经过验证,两种分类模型的准确识别率均达到了100%。芝麻油中掺伪油的定量分析采用FTNIR结合PLS。通过采集不同比例的芝麻油-大豆油与芝麻油-葵花籽油二元系统的FTNIR谱图,应用PLS分别建立二元系统定量分析模型并通过验证集检验其可靠性,研究结果表明该模型可以准确预测芝麻油中10%~100%的掺假油,其预测值与实际值的相对标准偏差(SEP)分别为1.027(大豆油)和0.9660(葵花籽油)。     

拉曼光谱技术在肉类掺假检测方面的应用研究进展

随着人们对高品质肉要求的提高,对快速、可靠的肉类掺假鉴别需求也不断增加。拉曼光谱技术是基于非弹性散射原理建立起来的分子结构表征技术,该技术能检测出官能团分子的化学结构,经过几十年的发展已广泛应用于食品检测,具有快速、无损、无污染、简单、可重复等优点,在肉品掺假检测方面取得了较好研究结果,应用潜力巨大。本文对近年来拉曼光谱技术在肉类掺假检测方面的研究进展和最新研究成果进行综述,并讨论其存在的问题及发展前景。     

Detection and Quantification of Adulteration in Honey through Near Infrared Spectroscopy

Honey is one of the important traditional medicines since ancient times. In this article, a case study was carried out using near infrared spectroscopy techniques with Chemometrics to detect the Jaggery adulterants in the honey. Jaggery was used to prepare adulterant solution of different proportionate by manually mixing with four types of different honey samples. In total, 160 spectra were collected using the XDS^TM Optiprobe analyzer reflection type spectrometer and a calibration model was built using partial least square regression. The honey adulteration was predicted statistically with the calibration error 0.00751 and coefficient of determination R² of 0.9924.     

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。     

Comparison of FTIR, FT-Raman, and NIR Spectroscopy in a Maple Syrup Adulteration Study

ABSTRACT: Maple syrup is prone to adulteration with cheaper sugars, such as corn syrup, due to its simplicity in chemical composition. The adulterated samples were characterized by Fourier Transform infrared (FTIR) spectroscopy in the region of 400 to 4000 cm_-1. Other techniques used for detection and in characterization of samples were the near infrared (NIR; 600 to 1700nm) and Fourier Transform-Raman (FT-Raman; 400 to 4000cm_-1) spectroscopy. Quantifying and classifying adulterants using chemometrics shows that all spectroscopic methods adopted were efficient, but FTIR and FT-Raman were superior to NIR in quantitative characterization of adulterants in maple syrup.     

基于多元统计分析的牛排掺假定量判别及差异分析

结合市场对食品中不同成分的定量分析的需求,模拟不同比例混合样品,利用高分辨质谱进行数据采集及分析,分别考察主成分分析、偏最小二乘判别分析和正交偏最小二乘判别分析（orthogonal partial least squares-discrimination analysis,OPLS-DA）统计方法对不同混合比例的样品数据进行分析。相比之下,OPLS-DA模型具有更好的判别效果。通过进行S-Plot分析,分别筛选出25?条猪源多肽及牛源多肽,经线性拟合分析,其中牛源的10?条多肽和猪源的6?条多肽的线性相关系数R2大于0.99,并开展了真实性样品的验证实验,证明筛选的多肽可用于预计样品中的成分含量。这项研究建立了一种量化牛排的方法,并为筛选差异显著性的定量多肽提供新思路。     

近红外光谱技术定性鉴别蜂蜜品种及真伪的研究

提出了蜂蜜品种及真伪定性鉴别的新方法。在12000～4000cm-1范采集荆条蜜、槐花蜜、油菜蜜和掺假蜜的近红外光谱,结合一阶导、多元散射校正及变量标准化)三种方法对光谱进行预处理,以主成分分析结合马氏距离判别法,在不同谱区建立蜂蜜品种及真伪定性鉴别模型。研究发现6100～5700cm-1谱区为最佳建模波段,品种判别正确率达90%以上,真伪鉴别正确率为93.10%。     

Fourier Transform Infrared and Raman Spectroscopy Studies of the Time-Dependent Changes in Chicken Meat as a Tool for Recording Spoilage Processes

Time-dependent changes of chicken meat were studied using Fourier transform IR and Raman techniques. Small pieces of intact chicken breast muscle (pectoralis major) were used in the investigations. They were stored in air at 22 °C up to 10 days and their IR and Raman spectra were measured successfully. Analysis of the obtained spectra was performed using a deconvolution of the experimental bands into Lorentz components. All integral intensities of the observed bands were standardized using the statistical R2 coefficient of determination. The R² values were automatically created as the output of the Origin software. The time-dependent changes of the spectra were used for meat spoilage detection. The analytical relationships between the integral intensities of selected bands have been derived indicating an increase of free amino acids content as the main effect of the chicken breast muscle spoilage.     

基于近红外光谱技术的咖啡掺假快速鉴别方法

本文研究了利用近红外光谱技术结合AdulterantScreen算法建立的咖啡快速鉴别方法。采用傅里叶变换近红外光谱仪采集咖啡样品光谱,建立标样(材料光谱)光谱数据库,采集掺假物光谱数据,建立掺假物光谱数据库,建立了咖啡分类模型及掺假物模型,运用Adulterant Screen算法技术对模型的主成分及掺假成分进行计算分析,建立了咖啡掺假快速鉴别模型。本咖啡快速鉴别方法对含量在2%、5%、10%、15%、20%、30%及40%巴西莓果粉和5%、10%、15%、20%、30%及40%大麦掺假咖啡可以实现有效的掺假鉴别,最低识别咖啡中巴西莓果粉及大麦掺假含量分别为2%及5%。利用近红外光谱技术结合Adulterant Screen算法建立咖啡快速鉴别模型可以鉴别咖啡中巴西莓果粉和大麦等掺假物,为咖啡样品掺假鉴别提供了一种快速、可靠、无损的检测方法,能有效的运用于咖啡样品掺假鉴别的日常检测工作中。     

生物食品防腐剂乳酸链球菌素菌种筛选及鉴定

本文从样品的采集着手，通过增殖培养、分离纯化、检出待鉴定菌株，在《伯杰细菌鉴定手册》的基础上，着重讨论了一准确、简单、快速、可行的鉴定方法。     

拉曼光谱技术在橄榄油掺伪及品质鉴定中的应用研究进展

拉曼光谱是一种非弹性的散射光谱,基于拉曼散射效应,对与入射光频率不同的散射光谱进行分析以得到分子振动、转动方面信息,由于不同物质具有不同的特征光谱,因此拉曼光谱具有优秀的指纹能力,具有快速、灵敏检测和识别橄榄油的优势。橄榄油是所有植物油中唯一可不经提炼而直接以原始液态食用的名贵植物油,具有多种生理活性,有益人体健康。因其具有较高的商业价值从而有人铤而走险掺假伪造,如何快速有效识别橄榄油成为质量检测部门及相关企业着重关注的问题。本文从橄榄油的品质鉴定如不饱和度、碘值、游离脂肪酸、氧化稳定性和掺假鉴别两方面综述其应用研究现状,指出其存在问题并展望未来的发展趋势。     

COI序列应用于羊肉掺伪非定向筛查技术的研究

本项目研究了应用COI序列对羊肉中掺入的其他动物源材料进行非定向筛查,并对山羊肉及绵羊肉进行分辨。利用了DNA条形码技术对六个羊品种(系)的线粒体基因组细胞色素酶基因片段进行引物筛选,并利用筛选出的引物对七种动物源样本的COI片段进行扩增,以确定所筛选出的引物是否能将羊肉与其他动物源样品进行有效区分。以LCOI490/HCO2198、F1-1/R1-1、ITS3/ITS4为引物,通过对36份血液样本(山羊3个品种,绵羊3个品种)的基因组DNA进行PCR扩增,其中以F1-1/R1-1为引物得到的PCR产物电泳检测条带清晰,结果重现性好;利用F1-1/R1-1引物对鸡肉、猪肉、驴肉、牛肉、莱芜黑山羊肉、洼地绵羊肉和鸭肉等七种样本的COI片段进行扩增,并对扩增片段进行测序,山羊、绵羊之间的序列同源性仅为36.62%,其他各品种间的序列差异也很明显。结果表明:以F1-1/R1-1为专用引物可以有效区分羊肉与其他动物源样品,并能够对山羊肉及绵羊肉样品进行有效区别。     

Near-Infrared Spectroscopy for Classification of Oranges and Prediction of the Sugar Content

A nondestructive method for the classification of orange samples according to their growing conditions and geographic areas was developed using Vis/Near infrared spectroscopy. The results showed that the NIR spectra of the samples were moderately clustered in the principle component space and pattern recognition wavelet transform (WT) combined artificial neural network (BP-ANN) provided satisfactory classification results. Additionally, a partial least square (PLS) method was constructed to predict the sugar content of certain oranges. It showed excellent predictions of the sugar content of oranges, with standard error of prediction (SEP) values of 0.290 and 0.301 for Shatangju and Huangyanbendizao, respectively.     

Using Fish Sauce as a Substitute for Sodium Chloride in Culinary Sauces and Effects on Sensory Properties

Historically, fish sauce has been a standard condiment and ingredient in various Southeast Asian cuisines. Moreover, fish sauce imparts umami taste, which may enhance perceived saltiness in food. This quality suggests that fish sauce may be used as a partial substitute for sodium chloride (NaCl) in food preparation, which may present a valuable option for health‐conscious and salt‐restricted consumers. However, the degree to which NaCl can be decreased in food products without compromising taste and consumer acceptance has not been determined. We hypothesized that NaCl content in food may be reduced by partial replacement with fish sauce without diminishing palatability and consumer acceptance. Preparations of 3 types of food were assessed to test this hypothesis: chicken broth (n = 72); tomato sauce (n = 73); and coconut curry (n = 70). In the first session, the percentage of NaCl that could be replaced with fish sauce without a significant change in overall taste intensity was determined for each type of food using the 2‐Alternative Forced Choice method. In the second session, subjects rated 5 samples for each food with varying NaCl and/or fish sauce content on 3 sensory attributes: deliciousness; taste intensity; and saltiness. Our results demonstrate that NaCl reduction was possible in chicken broth, tomato sauce, and coconut curry at 25%, 16%, and 10%, respectively, without a significant loss (P < 0.05) in deliciousness and overall taste intensity. These results suggest that it is possible to replace NaCl in foods with fish sauce without reducing overall taste intensity and consumer acceptance.