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.     

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%.     

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.     

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.     

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。     

鱼露中乳酸菌及其主要产品指标关系研究

分析来自湛江和汕头市场5种鱼露中的乳酸菌种类,以及一些微生物含量和化学指标,探讨乳酸菌与产品指标的相关性.结果显示:5种鱼露含有13种不同的乳酸菌,其中乳杆菌11种,乳球菌2种;主要产品指标分析表明5种鱼露样品的微生物指标和化学指标均符合鱼露标准;乳酸菌和耐盐茵含量大小顺序比较接近;乳酸菌含量较高的样品亚硝酸盐和挥发性盐基氮含量较低.乳酸茵发酵过程产生多种酶系,可降低亚硝酸盐含量,而且乳酸菌可以抑制腐败茵的生长,从而降低产品中挥发性盐基氮含量.     

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.     

Sauces and dressings: a review of properties and applications

This comprehensive review of sauces and salad dressings covers the literature over the last decade with respect to physical and chemical properties and the applications of these products. As such, texturizing and structural systems (especially hydrocolloids) are described in detail and the application of polysaccharide thickeners as texture providers is described. Microbiological aspects of sauces are covered with relevant sections discussing the factors affecting microbiological activity and microbial spoilage and/or enhancement of a range of sauces. In addition, the use of carbohydrates and proteins as emulsifiers in many sauces is described. Quality aspects are given prominence in this review with sections being devoted to rheological and textural properties, chromatographic approaches and sensory aspects. Healthy sauces such as those having low sodium, reduced fat and cholesterol or containing inulin or microcrystalline cellulose are reviewed. Finally, for various sections the trends of the main findings as well as suggestions for future research are presented.     

单变量分析方法与多变量分析方法结合激光诱导击穿光谱分析翡翠化学成分的对比

激光诱导击穿光谱(LIBS)是一种原子光谱技术,可以同时快速分析多基质成分样品的化学元素组成,包括气体、液体和固体。然而,由于激光消融的不稳定性、基体效应以及自吸收等原因,LIBS的定量测量分析的能力一直没有得到公认。LIBS结合单变量分析方法与多变量分析方法可以快速分析多基质成分样品的化学元素含量,为了探究哪一种分析方法更适合LIBS定量测量分析翡翠,分别采用单变量和多变量的偏最小二乘法(PLS)分析翡翠样品的LIBS光谱数据并建立模型预测Ca和Al的含量。采用单变量建立模型时,对于Ca元素,校正模型与预测模型的相关系数分别为0.93和0.92,校正模型与预测模型的标准均方根误差分别为1.14和1.43;对于Al元素,校正模型与预测模型的相关系数分别为0.63和0.58,校正模型与预测模型的标准均方根误差分别为3.04和3.59。在建立多变量的偏最小二乘法模型时,使用波长197.33~762.8nm范围的峰值强度与质量分数之间的拟合,对于Ca元素,校正模型与预测模型的相关系数分别为0.97和0.95,校正模型与预测模型的相标准均方根误差分别为0.81和1.08;对于Al元素,校正模型与预测模型的相关系数分别为0.92和0.89,校正模型与预测模型的标准均方根误差分别为1.36和1.79。结果显示偏最小二乘法能够提高LIBS对缅甸翡翠的定量测量分析。     

Multivariate Classification of UHT Milk as to the Presence of Lactose Using Benchtop and Portable NIR Spectrometers

Lactose hydrolyzed milk was developed in the 1970s to serve individuals with lactose intolerance. This demand for lactose-free products by lactose-intolerant consumers has created a market segment for this food whose quality control has to be guaranteed. In order to assess milk samples for lactose content, this work proposes an analytical methodology to classify regular and lactose-free ultra high temperature (UHT) milks using multivariate classification methods and NIR spectra obtained in FT-NIR and ultra-compact NIR spectrometers, aiming at field analysis. For this, 71 samples were purchased; 41 were lactose-free UHT milk and 30 regular UHT milk. Diffuse transflectance spectra were obtained by FT-NIR (833 to 2500 nm, 4 cm^?1 of resolution and mean of 16 scans), and by ultra-compact NIR (908 to 1676 nm, with 12.5 nm of resolution and mean of 50 scans). The classification models were obtained by PLS-DA and LDA techniques with robust variables selection by SPA and GA, evaluating different spectral pre-processing (MSC, SNV, and derivatives). The three models developed (PLS-DA, GA-LDA, and SPA-LDA) with benchtop equipment spectra correctly classified all samples with sensitivity and specificity of 100%. For the portable equipment spectra, PLS-DA and GA-LDA models obtained sensitivity and specificity of 100%. The SPA-LDA model, however, presented sensitivity and specificity of 80 and 100%, respectively. These results indicate that methodologies using NIR equipment, especially the ultra-compact NIR, with multivariate classification techniques are feasible in discrimination between regular and lactose-free milk in the field, thus enabling a quick and precise analysis.     

Application of Fast Gas Chromatography and Fourier Transform Infrared Spectroscopy for Analysis of Lard Adulteration in Virgin Coconut Oil

Lard (LD) and virgin coconut oil (VCO) share some similarities such as having transparent to yellowish color and are solid at room temperature; hence, as a consequent, LD may be a potential oil adulterant in VCO. This study highlights the application of fast gas chromatography with surface acoustic wave detector (GC-SAW system) and Fourier transform infrared (FTIR) spectroscopy combined with chemometrics to analyze the presence of LD in VCO. Binary admixtures of LD in VCO in various percentage concentrations ranging from 1% to 50% (v/v) were assayed using the fast GC-SAW system and FTIR spectroscopy. Using the fast GC-SAW system, ten different chromatogram peaks were identified as the adulterant peaks. One peak in the fast GC-SAW system chromatogram was found to have the best relationship, with a coefficient of determination (R ²) value of 0.9344. Furthermore, FTIR spectroscopy coupled with partial least square (PLS) and discriminant analysis (DA) can be successfully developed for quantification and classification of LD in VCO. The results showed that PLS able to predict the LD contents in VCO with equation of y = 0.999 ×+ 0.006 y = 0.{999} times + 0.00{6} , for the correlation between actual value of LD (x) and FTIR predicted value (y) with R ² of 0.9990 at frequency regions of 3,020–3,000 cm⁻¹ and 1,120–1,000 cm⁻¹. DA can classify VCO and that adulterated with LD using the FTIR spectra at the same frequency regions used in quantification.     

Multiproduct,Multicomponent and Multivariate Calibration: a Case Study by Using Vis-NIR Spectroscopy

Vitamin C and total acidity were determined in industrialized fruit nectar and soy juices through visible-near infrared (Vis-NIR) spectroscopy and multiproduct, multicomponent, and multivariate calibration, based on partial least squares (PLS2) regression. Since samples with different types, flavors, and sugar content (light or not) were together in the model construction, the samples present higher heterogeneity and it was necessary to optimize the calibration and validation sets by outlier elimination based on leverage and unmodelled residuals in spectral data. The model was developed and validated by the evaluation of the parameters of merit such as accuracy, analytical sensitivity, adjust, linearity, residual prediction deviation, limits of detection, and quantification. The results achieved indicates that the multiproduct, multicomponent, and multivariate calibration model developed from Vis-NIR spectroscopy and PLS2 regression can be used in the industrial routine analysis as an alternative to titration reference methods that are time- and reagent-consuming methods, making the methodology extremely attractive from the industrial point of view.     

First Break Forward Interval PLS (FB-FiPLS) Procedure as Potential Tool in Analysis of FTIR Data for Fast and Robust Quantitative Determination of Food Adulteration

Ternary mixtures of sugar solutions containing maple syrup were studied quantitatively using Fourier transform infrared (FTIR) attenuated total reflectance (ATR) technique coupled with partial least squares regression (PLS) and selection of spectral variables. Two ternary mixtures were analyzed; first ternary mixture contained maple syrup, white sugar solution, and fully inverted sugar solution; second ternary mixture comprised maple syrup, white, and brown sugar solutions. In this paper, a procedure for selection of spectral variables with PLS, called first break forward interval PLS (FB-FiPLS), is tested on maple syrup adulteration. The method achieved almost exactly the same performance as synergy interval PLS (SiPLS) but with much shorter computational time. The upper limit of number of latent variables (LVs), which is the critical factor for both interval PLS methods, was determined using repeated double cross-validation on whole spectral region of calibration set for each analyzed component in each analyzed ternary mixture set. FB-FiPLS procedure for selection of spectral variables, using only root mean square error of cross validation (RMSECV) values for whole optimization of spectral variables, is fast and robust. After spectral variables and LVs for each particular model had been selected with minimum RMSECV of FB-FiPLS procedure, final results in terms of RMSECV and RMSEP for FB-FiPLS were in most cases statistically significantly better than PLS on whole spectral region and on selected spectral regions. Predictions of each component in analyzed ternary mixture set is promising (R ²(training set)?>?0.98, R ²(test set)?>?0.97), especially for fully inverted sugar solution (RMSEP?=?0.142 % w/w).     

Fourier Transform Infrared Spectroscopy with Multivariate Analysis as a Novel Method for Characterizing Alcoholic Strength,Density, and Total Dry Extract in Spirits and Liqueurs

Fourier transform infrared (FTIR) spectroscopy allows fast throughput screening of beverages (less than 2 min per sample). Using partial least squares (PLS) regression, it was demonstrated that relative density and alcohol content, which have previously been calibrated only for extract-free spirits, can also be determined in highly viscous liqueur samples. In addition, total dry extract (TDE) was successfully calibrated for the first time for both spirit and liqueur screening. An inter-laboratory validation showed that the standard errors of prediction, ranging between 0.0006 and 0.0078 (relative density), 0.23 and 0.74 % vol (alcoholic strength), and 0.55 and 7.53 g/l (TDE), were satisfactory for use of the FTIR-PLS method in the context of screening analysis in official food control of alcoholic beverages.     

近红外光谱结合PLS-DA划分烟叶等级

为了对烟叶等级进行快速分类,采用云南曲靖地区150个烟草样品近红外光谱,结合偏最小二乘判别分析(PLS-DA),建立了烟叶等级分类模型,并对60个预测集样品进行了等级分类预测.结果表明:①训练集和预测集的预测正确率分别为100.0％(150/150)和96.7％(58/60).②PLS-DA对烟叶等级具有良好的分类效果.该模型为烟叶等级分类提供了一种新的快速鉴别分析的方法.     

Determination of Frying Quality of Vegetable Oils used for Preparing Falafel using Infrared Spectroscopy and Multivariate Calibration

The frying qualities of palm and soybean oils are determined using infrared spectroscopy and multivariate calibration. Compare to soybean oil, palm oil is more resistive to the chemical and physical changes and this is attributed to the high degree of unsaturation of soybean oil (61.9%) compare to palm oil (13.8%). After 48 h in service, the oil samples were effectively clustered into two groups using principal component analysis which indicated that both oils still maintain their chemical identities. Partial least squares regression (PLS1 and PLS2) a long with mid-FTIR data are used for predicting free fatty acid, viscosity, and total polar compounds of the used oils without running expensive standard procedures. PLS1 and PLS2 outperformed PCR and MLR for predicting the quality indicators of the frying oils. For palm oil and at the optimum calibration conditions, the obtained accuracies (SD) are 105.6% (0.05), 99.8% (1.10), and 103.9% (0.16) for free fatty acid, viscosity, and total polar compounds, respectively. The proposed method is simple, less-expensive, and has comparable accuracy/precision with standard procedures that often used for monitoring frying oils.     

Cuts and Classification: The Use of Nomenclatures as A Tool for the Reform of the Meat Trade in France, 1850-1880

Reformers seeking to improve the diet of the French working classes in the middle of the 19th century agreed that fresh meat was too expensive for consumers hailing from modest and poor backgrounds. At local markets, the economic parties within the meat industry and municipal authorities strove to limit retail prices by setting an administrative price. In doing so, they faced the difficulty of establishing a just price at the shop level to provide fair remuneration for both livestock producers and butchers. The differentiation and the hierarchic ordering of pieces of meat and their relative prices were the solution to this problem. The introduction of an official, legally-binding nomenclature of meat cuts helped the reformers to overcome the problem of the heterogeneous nature of prices in shops and market stalls. These nomenclatures limited in a regulatory manner the designation of cuts, the attribution of prices, and the forms of transaction. Thus, they contributed to the homogenization of butcher stalls and the widening of the scale of prices for meat available to working-class consumers. The administrative diffusion of meat classifications was performed through meat consumer cooperatives and, somewhat later, veterinary inspectors. A proof of their legitimacy was that the terms were still present at the turn of the 20th century in any good household education manual.