Adulterated Powder Research Articles

Rapid identification of milk powder adulteration based on surface-enhanced Raman spectroscopy

In recent years, milk powder adulteration has emerged as a matter of great concern. In this study, a rapid, accurate, and efficient detection method based on surface-enhanced Raman spectroscopy (SERS) combined with principal component analysis (PCA) was established to detect milk powder adulteration. The “coffee ring” effect-based gold nanoparticles (Au NPs) as the SERS-enhancing substrate were coupled with a portable Raman spectrometer, which enabled the differentiation of various brands of milk powder and the detection of melamine in milk powder. The substrate exhibited good SERS enhancement ability with an enhancement factor of 104. Furthermore, a strong linear correlation with a correlation coefficient (R2) of 0.9903 was observed between the melamine Raman intensity and concentration from 0.5 to 5.0 mg/kg. The calculated limit of detection of melamine (LOD) was 0.15 mg/kg, while the limit of quantitation (LOQ) was 0.5 mg/kg. In addition, when the method was applied to the detection of melamine in milk powder samples, this method achieved the recovery rates of melamine in milk powder samples ranged from 92.83% to 98.86% with relative standard deviations between 0.84% and 1.14%. In summary, the established method offers the advantages of cost-effectiveness, less sample requirement, and shorter detection time, meeting the needs for milk powder classification and rapid melamine detection.

Identification and quantification of adulteration in collagen powder by terahertz spectroscopy − the effect of spectral characteristics on performance is considered

Terahertz spectroscopy is an emerging rapid detection method that can be used to detect and analyze food quality issues. However, models developed based on various spectral characteristics of terahertz have shown different performances in food identification. Therefore, we preliminarily analyzed the effect of terahertz spectral characteristics on the identification and quantification of collagen powder adulterated with food powders (plant protein powder, corn starch, wheat flour) with the use of random forest (RF), linear discriminant analysis (LDA), and partial least squares regression (PLSR), and determined the spectral characteristics suitable for identification and quantitative analysis. Then, the selected spectral characteristics data were preprocessed using baseline correction (BC), gaussian filter (GF), moving average (MA), and savitzky-golay (SG). Feature variables were extracted from preprocessed spectral characteristics data using genetic algorithm (GA), random forest (RF), and least angle regression (LAR). The study indicated that the BC-GA-LDA classification model based on the absorption coefficient spectra achieved an accuracy of 96.96% in identifying adulterated collagen powder. Additionally, the GA-PLSR model developed based on the power spectra demonstrated excellent performance in predicting adulteration levels, with the coefficient of determination (Rp2) values ranging from 0.93 to 0.99. The results showed that the rational selection of terahertz spectral characteristics is highly feasible for the accurate detection of collagen powder adulteration.

Identifying the degree of cornstarch adulteration in turmeric powder using optimized convolutional neural network

Adulteration in different spices is an emerging challenge in human civilization. It is commonly detected using different analytical and instrumental techniques. Despite good accuracy and precision many of such techniques are limited by their high processing time, skilled manpower requirement, expensive machinery and portability factor. Computer vision methodology driven by powerful convolutional neural network (CNN) architectures can be a possible way to address those limitations. This paper presents a CNN driven computer vision model which can detect cornstarch adulteration in turmeric powder along with the degree of adulteration. The model has been optimized using binary genetic algorithm (BGA) for improved performance and consistency. The experimentations presented in this paper were conducted with an in-house database prepared for 4 levels of adulteration and found to provide about 98% overall accuracy. The less expensive and faster detection capability of the model along with its mobility makes this proposal a promising addition to the existing spice adulteration screening methods.

Application of two dimensional correlation Fourier Transform Infrared spectroscopy (2D-COS-FTIR) to the quantification of carbohydrates in milk powder

The importance of milk and its products accelerated their adulteration as well as their characterization in term of techniques used, composition determination and adulteration quantification. Fourier Transform Infrared spectroscopy (FTIR) is one of the most widely used and increasingly developed methods to identify chemical contaminations and determine their concentrations. This study employs FTIR and two dimensional correlation (2D-COS) spectroscopies to detect milk powder adulteration with sucrose, lactose and starch in the concentrations range from 0.0 % to 6.0 % w/w. With the help of synchronous 2D-COS-FTIR spectra, the FTIR regions from 830 to 930 and 930–1200 cm−1 were determined as the qualified regions for the detection and carrying out quantitative analysis of milk adultered carbohydrates. Based on the area beneath the corresponding region or the height of a specific band limits of detection were determined as 0.25, 1.2 and 1.5 % for sucrose, starch and lactose respectively. The method was applied on commercial milk powder samples and one sample was assigned to have been adultered with 4 % sucrose. FTIR spectroscopy combined with 2D-COS offers an advanced tool for rapid screening and quantification of milk adulteration.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

Detection of different adulteration in cinnamon powder using hyperspectral imaging and artificial neural network method

Cinnamon is one of the medicinal spices that is important in point of economic and human health. The goal of the present research was to classify the levels of different adulterations in the spice using hyperspectral imaging technology. In the present research, three adulterants were investigated including sea foam powder and chickpea and wheat flour with 0, 5, 15, 30, and 50% adulteration levels. After sample preparation, the hyperspectral images of them were acquired using a line scan imaging system. The effective wavelengths were selected and image features were extracted. The effective features were selected and classified using the artificial neural network method. The classification accuracies of the classifier to identify sea foam powder and chickpea and wheat flour adulterants were equal to 98.9, 100, and 100%, respectively. The results showed the high ability of hyperspectral imaging combined with artificial neural networks method to detect adulteration in cinnamon with high reliability.

Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques

Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared spectroscopy (NIRS). This study developed models to rapidly classify and predict 0, 10, 20, 30, 40, and 50% w/w of soybean and maize flour in red, black and yellow maca cultivars using a handheld spectrophotometer and chemometrics. Soy and maize adulteration of yellow MP was classified with better accuracy than in red MP, suggesting that red MP may be a more susceptible target for adulteration. Soy flour was discovered to be a more potent adulterant compared to maize flour. Using 18 different pretreatments, MP could be authenticated with R2CV in the range 0.91–0.95, RMSECV 6.81–9.16 g/,100 g and RPD 3.45–4.60. The results show the potential of NIRS for monitoring Maca quality.

Identification of lyophilized avocado powder adulteration using the cavity perturbation technique at microwave frequencies

In this paper, we propose the cavity perturbation technique using a high Q cylindrical cavity at 2.4 GHz to identify adulteration of dry-freeze avocado powder with two of the most common adulterants (chickpea powder and green color). Three different adulteration conditions are explored, namely, avocado powder mixed with color, avocado powder mixed with chickpea powder, and avocado powder mixed with 50% colour–50% powder, at various contamination ratios (100, 90, 77, 62, 50, and 0%). For all cases, the perturbation technique shows clear linear quadratic regression trends that make adulteration identification possible.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Neural network-based Raman spectral recognition model for milk powder and wheat flour

The problem of counterfeit milk powder has occurred in recent years, and one of the standard techniques is to adulterate milk powder with wheat flour to reduce the cost. Since the morphology and colour of wheat flour are similar to that of milk powder, it is not easy to distinguish it from the naked eye, so a rapid method for detecting wheat flour adulteration in milk powder is urgently needed. This study used Raman spectral analysis as a novel and efficient screening technique. With the advantages of high sensitivity, non-destructive detection, low cost, no contamination, and online analysis, this method can play an essential role in rapid food detection and production control. By processing and analyzing the spectral data, a neural network model for identifying milk powder and wheat flour based on Raman spectroscopy has been developed in this study, and the model's performance has been evaluated. The results show that the model can quickly and accurately identify the proportion of wheat flour in milk powder to determine whether milk powder is adulterated or not. The results of this study are of great significance in ensuring the safety of infant milk powder. In the future, the method can be further optimized, for example, to improve detection speed and accuracy and to provide a more reliable quality control method for the milk powder industry.

Identification of pasteurized mare milk and powder adulteration with bovine milk using quantitative proteomics and metabolomics approaches

Adulteration in dairy products presents food safety challenges, driven by economic factors. Processing may change specific biomarkers, thus affecting their effectiveness in detection. In this study, proteomics and metabolomics approaches were to investigate the detection of bovine milk (BM) constituents adulteration in pasteurized mare milk (PMM) and mare milk powder (MMP). Several bovine proteins and metabolites were identified, with their abundances in PMM and MMP increasing upon addition of BM. Proteins like osteopontin (OPN) and serotransferrin (TF) detected adulteration down to 1 % in PMM, whereas these proteins in MMP were utilized to identify 10 % adulteration. Biotin and N6-Me-adenosine were effective in detecting adulteration in PMM as low as 10 % and 1 % respectively, while in MMP, their detection limits extend down to 0.1 %. These findings offer insights for authenticating mare milk products and underscore the influence of processing methods on biomarker levels, stressing the need to consider these effects in milk product authentication.

The feasibility of using ATR-FTIR spectroscopy combined with one-class support vector machine in screening turmeric powders

Once turmeric has been ground into powder, it is difficult to tell visually if it has been tampered with. In this study, ATR-FTIR spectroscopy was used in tandem with one-class support vector machine (OCSVM) to detect adulteration in turmeric powder. The OCSVM models were trained using 42 pure turmeric powder samples, optimized using 30 pure turmeric powder samples, and subsequently evaluated by classifying 30 pure and 120 adulterated (cornstarch, Metanil Yellow, Orange II, and Sudan I) samples. Preprocessing methods, such as Savitzky-Golay (SG)-derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC), were used individually and in combination to obtain the best-performing model. Models were assessed by comparing the sensitivity, specificity, and efficiency values and compared with one-class soft independent modeling of class analogy (OCSIMCA). The best performing OCSVM model (sensitivity = 1.00, specificity = 0.89) was obtained by first conducting an MSC on the raw data followed by SG-2nd derivative transformation. It also has an efficiency value of 0.94, which was 0.14 higher than when data preprocessing was not done. Compared to the results of OCSIMCA, the OCSVM model gave a higher efficiency value and can detect lower levels of cornstarch adulteration. Also, the results showed that inclusion of data preprocessing can lead to a better classification model. With the obtained evaluation parameter values, ATR-spectroscopy coupled with OCSVM demonstrated its potential for screening turmeric powder products.

Detection and quantification of corn starch and wheat flour as adulterants in milk powder by near- and mid-infrared spectroscopy coupled with chemometric routines

Adulterating foods, such as milk powder (MP), is a common practice in countries with no rigid policy on food quality control. This study employs Fourier transform near-infrared and mid-infrared (FT-NIR, FT-MIR) spectroscopy and chemometric analysis to detect milk powder adulteration with corn starch (CS) and wheat flour (WF) from 0.00 to 30.00% w/w concentrations. Partial least square regression (PLSR) models were developed, optimized, and compared to quantify corn starch and wheat flour adulterations. According to the results, the root mean square error prediction (RMSEP) for FT-NIR and FT-MIR in corn starch was 0.74 and 1.69% w/w and 0.82 and 2.63% w/w for wheat flour, respectively. FT-NIR spectroscopy, rather than FT-MIR coupled with the appropriate chemometrics models represents a more valuable tool for simple, rapid, and nondestructive detection of adulterants in milk powder. The recent availability of portable instruments, combined with suitable chemometric tools, makes it possible to discriminate adulterated food samples in situ.

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging.

This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000-1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques.

Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.

Microstructure and mechanical properties of ZrC modified Ni60 hard-facing alloy fabricated by laser metal deposition

Crack-free specimens were prepared from ZrC-modified Ni60 alloy by laser metal deposition. The effects of ZrC powder adulteration on the mechanism of microstructure refinement were investigated. The results demonstrated that the adulteration of ZrC powder did not provide nucleation sites for the hardening phase. The ZrC powder was added to Ni60 alloy powder to reduce the content of columnar dendrites and the eutectics of laser metal deposition specimens. The transformation of the dendritic hardening phase into a massive hardening phase was induced. The fine Ni–B–Si eutectic microstructure changed to a massive NiZr eutectic and peritectic microstructure with increasing ZrC powder mass fraction. The microstructure transformation mechanism constrained the initiation and propagation of cracks in the deposited specimens. The present research provides a method for improving the cracking defect of laser additive manufacturing Ni–Cr–B–Si system alloys and a theoretical basis for promoting the application of the alloy in additive manufacturing.

Rapid and non-destructive detection of organic carrot powder adulteration using spectroscopic techniques

Additives used for food adulteration are difficult to detect because they have properties similar to the samples they are added to. Organic foods have more positive effects on food safety compared to their conventional counterparts. Therefore, it is important to assess the detectability of additives that resemble the main ingredient and are used for adulteration purposes in organic powdered products. In this study, the feasibility of using a chromameter and near infrared spectroscopy (NIRS) to discriminate adulterated organic black carrot powders, which were dried by hot air (HA) and intermittent microwave (IMW), was investigated. Principal component analysis (PCA) and partial least squares regression (PLSR) were used for data analysis. The best PLSR models for estimating the adulteration level (%) were obtained using NIRS data (R2val=0.98, RMSEP=4.9%) for samples dried by HA. Furthermore, results showed that temperature or power levels applied during drying affected the efficiency of adulteration determination in samples.

Spectral separation degree method for Vis-NIR spectroscopic discriminant analysis of milk powder adulteration

Adulteration detection of adding ordinary milk powder to high-end dedicated milk powder is challenging due to the high similarity. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the discriminant analysis models of pure brand milk powder and its adulterated milk powder (including unary and binary adulteration) were established. Standard normal variate transformation and Norris derivative filter (D = 2, S = 11, G = 5) were jointly used for spectral preprocessing. The separation degree and separation degree spectrum between two spectral populations were proposed and used to describe the differences between the two spectral populations, based on which, a novel wavelength selection method, named separation degree priority combination-kNN (SDPC-kNN), was proposed for wavelength optimization. SDPC-wavelength step-by-step phase-out-kNN (SDPC-WSP-kNN) models were established to further eliminate interference wavelengths and improve the model effect. The nineteen wavelengths in long-NIR region (1100–2498 nm) with a separation degree greater than 0 were used to establish single-wavelength kNN models, the total recognition-accuracy rates in prediction (RARP) all reached 100%, and the total recognition-accuracy rate in validation (RARV) of the optimal model (1174 nm) reached 97.4%. In the visible (400–780 nm) and short-NIR (780–1100 nm) regions with the separation degree all less than 0, the SDPC-WSP-kNN models were established. The two optimal models (N = 7, 22) were determined, the RARP values reached 100% and 97.4% respectively, and the RARV values reached 96.1% and 94.3% respectively. The results indicated that Vis-NIR spectroscopy combined with few-wavelength kNN has feasibility of high-precision milk powder adulteration discriminant. The few-wavelength schemes provided a valuable reference for designing dedicated miniaturized spectrometer of different spectral regions. The separation degree spectrum and SDPC can be used to improve the performance of spectral discriminant analysis. The SDPC method based on the separation degree priority proposed is a novel and effective wavelength selection method. It only needs to calculate the distance between two types of spectral sets at each wavelength with low computational complexity and good performance. In addition to combining with kNN, SDPC can also be combined with other classifier algorithms (e.g. PLS-DA, PCA-LDA) to expand the application scope of the method.

Precise qualitative analysis of foreign protein adulteration in milk powder by fully exploring the spectral information in laser-induced breakdown spectroscopy

To improve the qualitative accuracy of foreign protein adulteration in milk powder, a novel method named multidimensional spectral information laser-induced breakdown spectroscopy (MSI-LIBS) was proposed, which fully mined the effective information in the spectra by integrating the absolute intensity, the first derivative spectra, and the ratio spectra. Compared with traditional LIBS, the performance of the models based on MSI-LIBS was significantly improved. The accuracy of the cross-validation set of support vector machine, k-nearest neighbor, and random subspace method-linear discriminant analysis models increased from 80.98%, 75.61%, and 79.25% to 85.17%, 79.32%, and 81.18%, respectively. The accuracy of the prediction set increased from 81.50%, 76.03%, and 79.07% to 85.82%, 79.74%, and 81.28%, respectively. Furthermore, the visualization results of t-distributed stochastic neighbor embedding also showed that there was a more obvious boundary between the spectra of different samples based on MSI-LIBS. Therefore, these results fully prove the effectiveness of MSI-LIBS in improving the performance of LIBS classification.