- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples
摘要： Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near‐infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825‐1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000–4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least‐squares discriminant analysis (PLS‐DA), and partial least‐squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS‐DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R‐square and root mean square error (RMSE) values obtained for the PLS‐DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave‐one‐out cross‐validation procedure was used as an internal cross‐validation criteria and the R‐square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.
关键词： urea,principal components analysis,partial least‐squares regressions,milk adulteration,NIR spectroscopy,partial least‐squares discriminant analysis
更新于2025-09-19 17:13:59
-
SERS detection of sodium thiocyanate and benzoic acid preservatives in liquid milk using cysteamine functionalized core-shelled nanoparticles
摘要： A cysteamine functionalized core shelled nanoparticles (Au@Ag-CysNPs) was presented for simultaneous and rapid detection of sodium thiocyanate (STC) and benzoic acid (BA) preservatives in liquid milk using surface-enhanced Raman spectroscopy (SERS) technique. A spectrum covering 350-2350 cm-1 region was selected to detect STC with concentrations ranging from 0.5 to 10 mg/L and BA with concentrations ranging from 15 to 240 mg/L in milk samples. Characterization of nanoparticles using high-resolution TEM confirmed that the successful synthesis of Au@AgNPs with core (gold) size of 28 nm and shell (silver) thickness of about 5 nm was grafted with 120 μL of 0.1 nM cysteamine hydrochloride. Results showed that Au@Ag-CysNPs could be used to detect STC up to 0.03 mg/L with a limit of quantification (LOQ) of 0.039 mg/L and a coefficient of determination (R2) of 0.9833 in the milk sample. For detecting BA, it could be screened up to 9.8 mg/L with LOQ of 10.2 mg/L and R2 of 0.9903. The proposed substrate was also highly sensitive and the employed method involved only minor sample pretreatment steps. It is thus hoped that the new substrate could be used in the screening of prohibited chemicals in complex food matrices in future studies.
关键词： SERS,milk adulteration,sodium thiocyanate,nanoparticles,benzoic acid
更新于2025-09-16 10:30:52
-
Application of infrared spectroscopy techniques for the assessment of quality and safety in spices: a review
摘要： Adulteration of spices has been a major threat in the past decades as it decreases the quality and causes illness to humans. Testing of species is essential for evaluating the quality and safeguards the consumer against bogus activities. It is important to ensure the quality of spices throughout its value chain. Conventional techniques used to detect the quality and adulterations in spices are destructive nature, time-consuming, and do not suitable for online monitoring. Despite traditional methods, spectroscopy is an emerging technology; this has been substantiated to be the dynamic and progressive method in detecting the adulteration of species. It is a simple, rapid, and nondestructive analytical tool familiarized with the food industry in the detection of adulterants present in the food sample. This review paper focuses on the application of spectroscopy techniques in adulteration detection and the quality assessment of spices and herbs. The present paper would be a roadmap for the researchers and industries for the detection of adulteration more efficiently and it would take up on-line quality and safety control of spices, agricultural products and food in the future.
关键词： Spectroscopy,adulteration,food quality,nondestructive method,spices
更新于2025-09-16 10:30:52
-
Honey exposed to laser-induced breakdown spectroscopy for chaos-based botanical classification and fraud assessment
摘要： Given that honey is among the top ten foods with the highest adulteration rate in the European Union, in this research, a tool has been developed to tackle this malpractice. The combination of laser-induced breakdown spectroscopy (LIBS) and chaotic parameters has been employed to classify six European honeys of different botanical origins as well as detect samples containing the usually elusive rice syrup adulteration in weight concentrations as low as 2 %. The profiles of the LIBS emission spectra can be used to faithfully classify honey in terms of botanical origin by combining information extracted directly from the spectra with simple linear modeling. In contrast, the detection of low amounts of rice syrup in honey is not as straightforward, which is why algorithms based on chaotic parameters such as shifted (lag-k) autocorrelation coefficients were employed to extract underlying information representative of adulterated samples. Since these algorithms are capable of detecting slight changes in the composition of honeys, it has been possible to identify these adulterations with a success rate greater than 90 % when samples from honeys of different botanical origins are combined into the same model, and over 95 % when individual honey types are analyzed.
关键词： Chaotic Parameters,Classification,LIBS,Adulteration,Botanical Origin,Honey
更新于2025-09-16 10:30:52
-
Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Typings of Edible Oils through Spectral Networking of Triacylglycerol Fingerprints
摘要： Adulteration of edible oils by the manufacturers has been found frequently in modern societies. Due to the complexity of the chemical contents in edible oils, it is challenging to quantitatively determine the extent of adulteration and prove the authenticity of edible oils. In this study, a robust and simple MALDI-TOF-MS platform for rapid ?ngerprinting of triacylglycerols (TAGs) in edible oils was developed, where spectral similarity analysis was performed to quantitatively reveal correlations among edible oils in the chemical level. Speci?cally, we proposed oil networking, a spectral similarity-based illustration, which enabled reliable classi?cations of tens of commercial edible oils from vegetable and animal origins. The strategy was superior to traditional multivariate statistics due to its high sensitivity in probing subtle changes in TAG pro?les, as further demonstrated by the success in determination of the adulterated lard in a food fraud in Taiwan. Finally, we showed that the platform allowed quantitative assessment of the binary mixture of olive oil and canola oil, which is a common type of olive oil adulteration in the market. Overall, these results suggested a novel strategy for chemical ?ngerprint-based quality control and authentication of oils in the food industry.
关键词： triacylglycerols,oil networking,spectral similarity,adulteration,MALDI-TOF-MS,edible oils
更新于2025-09-11 14:15:04
-
Tea types classification with data fusion of UV–Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis
摘要： The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF+UV-Vis, SF+NIR, NIR+UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.
关键词： Fluorescence spectroscopy,Food adulteration,NIR,Teas classification,Multivariate data analysis,Data fusion,UV-Vis
更新于2025-09-10 09:29:36
-
Quantifying several adulterants of notoginseng powder by near-infrared spectroscopy and multivariate calibration
摘要： The authentication of traditional Chinese medicine (TCM) is critically important for public-health and economic terms. Notoginseng, a classical TCM of high economic and medical value, could be easily adulterated with sophora flavescens powder (SFP), corn flour (CF) or other analogues of low-grade (ALG) because of their similar tastes, appearances and much lower cost. The main objective of this study was to evaluate the feasibility of applying of near-infrared (NIR) spectroscopy and multivariate calibration for identifying and quantifying several common adulterants in notoginseng powder. Two datasets were prepared for experiment. The competitive adaptive reweighted sampling (CARS) was used to select informative variables. Two different schemes were used for sample set partition. Model population analysis (MPA) was made. The results showed that, the constructed partial least squares (PLS) model using a reduced set of variables from CARS can provide superior performance to the full-spectrum PLS model. Also, the sample set partition is very of great importance. It seems that the combination of NIR spectroscopy, CARS and PLS is feasible to quantify common adulterants in notoginseng powder.
关键词： calibration,Notoginseng,adulteration,near-infrared
更新于2025-09-10 09:29:36
-
Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging
摘要： This paper described a rapid and non-destructive method based on visible near-infrared (Vis-NIR) hyperspectral imaging system (400–1000 nm) for detection adulteration with duck meat in minced lamb. The multiple average of the reference spectral and a predicted relative spatial distribution coe?cient were applied in this study to reduce the noise of the spectra. The PLSR model with selected wavelengths achieved better results than others with determination of coe?cients (R2 P) of 0.98, and standard error of prediction (RMSEP) of 2.51%. And the prediction map of the duck minced in lamb meat was generated by applying the prediction model. The results of this study indicate the great potential of the hyperspectral technology applying to rapidly and accurately detect the meat adulteration in minced lamb meat.
关键词： Hyperspectral imaging,Minced lamb meat,PLSR,Duck meat,Adulteration
更新于2025-09-10 09:29:36
-
Detection of minced lamb and beef fraud using NIR spectroscopy
摘要： In this study, the feasibility of NIR spectroscopy to detect different types of meat fraud in both minced lamb and beef was investigated. For this, a multivariate chemometric approach was used to identify the most useful pre-processing techniques to discriminate between pure lamb and beef and adulterated samples. The results obtained in this study suggest that it is possible to use NIRS to distinguish pure from adulterated minced meat with acceptable precision and accuracy. Rates of classification between 78.95 and 100% were achieved for the validation sets. Higher % CC samples were obtained for samples mixed with pork, meat of Lidia breed cattle and foal meat than for samples adulterated with chicken, where the lowest rates of classification were achieved in both lamb and beef. Additionally, identification of adulteration of meat of Lidia breed cattle in minced beef at 2% was achieved. Furthermore, best classification results were obtained for minced beef mixed with foal meat with a 100% of samples correctly classified indicating that inclusion of foal meat in minced beef at 1% and higher can be detected by using NIRS. Regarding pre-processing techniques, in general, the most powerful ones to classify both groups of samples (pure and mixed) were those orientated to reduce the scatter, MSC and SNV, and, those to correct peak overlaps, 1st and 2nd Der.
关键词： adulteration detection,NIR spectroscopy,minced lamb,chemometrics,meat fraud,minced beef
更新于2025-09-09 09:28:46
-
Differential Scanning Calorimetry and Infrared Spectroscopy Combined with Chemometric Analysis to the Determination of Coffee Adulteration by Corn
摘要： Roasted and ground coffee is targeted by fraudulent addiction of products. In this way the determination of contaminants in coffee has economic and nutritional importance. In this study, the coffee adulteration by corn were detected using DSC (differential scanning calorimetry) and FTIR (Fourier transform infrared spectroscopy) coupled to PCA (principal component analysis), and PLS (partial least squares) models. Three different levels of roasted and ground Coffea arabica L. were used to prepare mixtures with roasted and ground corn. The level of adulteration used was between 0.5 to 40% (m/m). It was observed that both DSC and FTIR coupled with PCA are able to discriminate adulterated from unadulterated samples of coffee by corn at levels below 1%. PLS models were built with DSC and FTIR data reaching good correlation between the values of estimated and reference concentrations, with RMSECV (root mean square error of cross-validation) lower than 3.5% for DSC data and 2.7% for FTIR data.
关键词： adulteration,ATR-FTIR,chemometric analysis,DSC,coffee
更新于2025-09-04 15:30:14