Effects of the Adulteration Technique on the Near-Infrared Detection of Melamine in Milk Powder

Double-Tube Multiplex TaqMan Real-Time PCR for the Detection of Eight Animal-Derived Dairy Ingredients

Milk and dairy products represent important sources of nutrition in our daily lives. The identification of species within dairy products holds importance for monitoring food adulteration and ensuring traceability. This study presented a method that integrated double-tube and duplex real-time polymerase chain reaction (PCR) with multiplex TaqMan probes to enable the high-throughput detection of animal-derived ingredients in milk and dairy products. The detection system utilized one pair of universal primers, two pairs of specific primers, and eight animal-derived specific probes for cow, buffalo, goat, sheep, camel, yak, horse, and donkey. These components were optimized within a double-tube and four-probe PCR multiplex system. The developed double-tube detection system could simultaneously identify the above eight targets with a detection limit of 10-0.1 pg/μL. Validation using simulated adulterated milk samples demonstrated a detection limit of 0.1%. The primary advantage of this method lies in the simplification of the multiplex quantitative real-time PCR (qPCR) system through the use of universal primers. This method provides an efficient approach for detecting ingredients in dairy products, providing powerful technical support for market supervision.

Trace element levels and bio-active compounds in ethanolic leaf extract of Chrysophyllum albidum characterised using gas chromatography - mass spectrometry

AIM AND OBJECTIVES

Chrysophyllum albidum, also known as Africa star apple, has so many ethnobotanical uses in African healing system. Thus, a study that reveals possible trace elements and its phytochemical profile will give an essential insight to the bioactivity profile of the plant. The study was to identify the trace elements and the bioactive compounds present in Chrysophyllum albidum (C. albidum) leaf extract MATERIALS AND METHODS: The ethanol leaf extract of Chrysophyllum albidum was evaluated for trace elements using an Atomic Absorption Spectrophotometry (ASS) while the chemical composition was analysed using Gas Chromatography-Mass Spectrometry (GC/MS). The identification of phytoconstituents using GC/MS was based on the instrument library, peak area and retention time.

RESULT

The ethanol leaf extract of C. albidum showed a high content of potassium, calcium, magnesium and sodium, while relative low content of manganese, iron, copper, zinc, lead and nickel. Total of 30 peaks representing 30 identified compounds were recorded in the GC/MS analysis. These include a variety of heterocyclic compounds such as fatty acids, organic compounds, esters, and disaccharides etc. The major constituents of the extract were: Sucrose (37.45%), followed by 1,2,3-Propanetriol,1-acetate (7.86%), di-Glyceraldehyde dimer (5.70%), 1-(3-Benzyl-2-thioureido)-1-deoxy-beta-d glucopyranose 2,3,4,6-tetraacetate (4.53%), 4 H-Pyran-4-one, 2,3-dihydro-3, 5-dihydroxy-6-methyl- (4.49%), 3-Deoxy-d-Mannoic lactone (3.14%), Glycerine (3.04%) and minor compounds that are less than 3%.

CONCLUSION

The elemental composition of the leaf extract of C. albidum may be influenced by the environmental factors such as soil composition surrounding the plant's roots, while a variety of bioactive compounds with diverse biological activities were present. Hence, the plant have a potential pharmacological activities.

Colorimetric Sensing of the Peroxide Number of Milk Powder Using CsPbBr3 Perovskite Nanocrystals

In this study, a wavelength-shift-based colorimetric sensing approach for the peroxide number of milk powder using CsPbBr₃ perovskite nanocrystals (CsPbBr₃ NCs) has been developed. Through the fat extraction, REDOX reactions and halogen exchange, as well as the optimized experimental conditions, a colorimetric sensing method was established to determine the peroxide number of milk powder samples. The integrated process of milk powder fat extraction and the REDOX process greatly shortened the determination time. This colorimetric method has a good linear correlation in the range of the peroxide number from 0.02 to 1.96 mmol/kg, and the detection limit was found to be 3 μmol/kg. This study further deepens the application prospect of wavelength-shift-based colorimetric sensing using CsPbBr₃ NCs.

Temperature-perturbed two-dimensional generalized correlation characteristic slice spectra combined with multivariate method to identify adulterated milk

In order to improve the discrimination accuracy of adulterated milk, a detection method was proposed based on temperature-perturbed generalized two-dimensional (2D) correlation characteristic slice spectra. A total of 240 samples were prepared including three brands of 40 pure milk and 40 urea-tainted milk, respectively. The infrared attenuated total reflection spectra of each sample were collected at different temperatures. Synchronous 2D infrared correlation spectrum of each sample was calculated under the external perturbation of temperature. The characteristic slice spectra of each sample were extracted from synchronous 2D correlation spectrum at characteristic peaks of milk and adulterants. N-way partial least squares discriminant analysis (NPLS-DA) models of single brand and the fusion of three brands of adulterated milk were established based on 2D correlation characteristics slice spectra. For comparison, the discrimination models were established using synchronous 2D correlation spectra and one-dimensional (1D) infrared spectra at room temperature, respectively. For the three brand fusion models, the discrimination accuracies of unknown samples were 100%, 98.8% and 82.7% using 2D correlation characteristic slice spectra, 2D correlation spectra, and 1D spectra, respectively. The results showed that the proposed method not only compressed the data, but also effectively extracted the characteristic information, and improved the accuracy of discrimination.

Nontargeted detection and recognition of adulterants in milk powder using Raman imaging and neural networks

Raman imaging technology combined with targeted chemometrics can play a vital role in the rapid detection of milk powder adulteration, which threatens the lives of infants and other people. However, these methods always suffer from a narrow detection range. Nontargeted methods show a broader detection range but cannot recognize adulterants. Here, a novel nontargeted chemometric method, named as the adversarial discrimination neural network (ADNN), is proposed to detect and recognize adulterants simultaneously. The method comprises building a tight boundary in the feature space of Raman images to discriminate milk powder samples from the majority of adulterated cases. Then a first-order partial derivative of the ADNN is calculated to recognize different adulterants through a local approximation strategy. A validation set containing samples adulterated with various adulterants at concentrations ranging from 0.3% to 1.5% w/w was provided to challenge the proposed method. The validated detection accuracy of the proposed method for authentic and adulterated samples was 99.9% and 99.7% and the adulterants were recognized correctly. The ADNN-Raman represents a novel nontargeted and end-to-end tool for detecting and recognizing adulterants in milk powder simultaneously, providing new insights into nontargeted chemometric analysis.

Food inauthenticity: Authority activities, guidance for food operators, and mitigation tools

Historically, food fraud was a major public health concern which helped drive the development of early food regulations in many markets including the US and EU market. In the past 10 years, the integrity of food chains with respect to food fraud has again been questioned due to high profile food fraud cases. We provide an overview of the resulting numerous authoritative activities underway within different regions to counter food fraud, and we describe the guidance available to the industry to understand how to assess the vulnerability of their businesses and implement appropriate mitigation. We describe how such controls should be an extension of those already in place to manage wider aspects of food authenticity, and we provide an overview of relevant analytical tools available to food operators and authorities to protect supply chains. Practical Application: Practical Application of the provided information by the food industry in selecting resources (guidance document, analytical methods etc.).

Amino Acid Fingerprinting of Authentic Nonfat Dry Milk and Skim Milk Powder and Effects of Spiking with Selected Potential Adulterants

A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV) was used for quantitation of amino acids and to calculate their distribution. The performance of this rapid method of analysis was evaluated and was used to determine the amino acid fingerprint of authentic milk powders. The distribution of different amino acids and their predictable upper and lower tolerance limits in authentic NFDM/SMP samples were established as a reference. Amino acid fingerprints of NFDM/SMP were compared with selected proteins and nitrogen rich compounds (proteins from pea, soy, rice, wheat, whey, and fish gelatin) which can be potential economically motivated adulterants (EMA). The amino acid fingerprints of NFDM/SMP were found to be affected by spiking with pea, soy, rice, whey, fish gelatin and arginine among the investigated adulterants but not by wheat protein and melamine. The study results establish an amino acid fingerprint of authentic NFDM/SMP and demonstrate the utility of this method as a tool in verifying the authenticity of milk powders and detecting their adulteration.

Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

13C NMR detection of non-protein nitrogen substance adulteration in animal feed

Illegal adulteration of melamine in animal feed and food has been widely studied. However, the risk of using substitute non-protein nitrogen substances still exists. In this study, we developed the ¹³C NMR method for the detection of non-protein nitrogen substance adulteration in animal feed. Three compounds, i.e., urea, melamine, and biuret, were used for method development. We found that the chemical shifts of the characteristic peaks in the carbon spectra of high-nitrogen adulterants were all between 150 and 170 ppm, whereas the chemical shifts of real protein peptide bonds (-CO-NH-) were between 170 and 180 ppm, demonstrating a good distinction between non-protein nitrogen and authentic protein. The method for analyzing melamine, urea, and biuret was validated. The R² values were all above 0.99 within the calibration range of 0.05-2% (w/w). The limits of quantification of urea, melamine, and biuret were 0.0120%, 0.0660%, and 0.0806%, respectively. This method involves simple sample pretreatment and rapid detection while also providing high accuracy. All the sample information obtained by NMR detection does not require strict impurity removal. Compared with a previously reported ¹H NMR method, the developed ¹³C NMR method does not require strict moisture removal to avoid active hydrogen exchange, and the interfering peak overlap is mitigated.

Rapid Detection of Nonprotein Nitrogen Adulterants in Milk Powder Using Point-Scan Raman Hyperspectral Imaging Technology

To develop a rapid detection method for nonprotein nitrogen adulterants, this experiment sets up a set of point-scan Raman hyperspectral imaging systems to qualitatively distinguish and quantitatively and positionally analyze samples spiked with a single nonprotein nitrogen adulterant and samples spiked with a mixture of nine nonprotein nitrogen adulterants at different concentrations (5 × 10-3 to 2.000%, w/w). The results showed that for samples spiked with single nonprotein nitrogen adulterants, the number of pixels corresponding to the adulterant in the region of interest increased linearly with an increase in the analyte concentration, the average coefficient of determination (R 2) was above 0.99, the minimum detection concentration of nonprotein nitrogen adulterants reached 0.010%, and the relative standard deviation (RSD) of the predicted concentration was less than 6%. For the sample spiked with a mixture of nine nonprotein nitrogen adulterants, the standard curve could be used to accurately predict the additive concentration when the additive concentration was greater than 1.200%. The detection method established in this study has good accuracy, high sensitivity, and strong stability. It provides a method for technical implementation of real-time and rapid detection of adulterants in milk powder at the port site and has good application and promotion prospects.

The recent advances of near-infrared spectroscopy in dairy production-a review

One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances.

Reconstitution followed by non-targeted mid-infrared analysis as a workable and cost-effective solution to overcome the blending duality in milk powder adulteration detection

Mid-infrared analysis of reconstituted milk is proposed as a feasible solution for the detection of milk powder adulteration regardless of the blending practice. To challenge the concept, skim milk powders were spiked with three of the most reactive/unstable of potential milk adulterants: semicarbazide hydrochloride, ammonium sulfate and cornstarch. To create the wet-blended set, a fraction of each sample was reconstituted and re-spray dried at laboratory scale with a benchtop spray dryer. Dry and wet-blended adulterated samples were reconstituted prior to mid-infrared measurement and projected onto a one-class classifier SIMCA model for reconstituted skim milk. Quantitative sensitivities, determined from the normalized orthogonal distances, were compared. Although the non-industrial spray drying introduced a spectroscopic bias, as revealed by the control samples, the non-targeted mid-infrared model showed comparable sensitivities for both blending practices once the main bias-rich spectral regions were removed, validating thereby the proposed concept.

Opportunities and Challenges Using Non-targeted Methods for Food Fraud Detection

In recent years, non-targeted methods have been a popular "buzz" phrase in food fraud detection. Using analytical instrumentation techniques, non-targeted methods have been developed and applied in many food and agricultural situations. However, confusion and misstatements remain regarding how the methods are used. This perspective will discuss the definitions related to non-targeted testing, the procedure of developing and validating methods, the techniques and data analysis, and opportunities and challenges regarding the use of this class of analytical methods. The perspective seeks to provide readers with the latest information regarding recent advances in the use of non-targeted methods.

Review on Nanomaterial-Based Melamine Detection

Illegal adulteration of milk products by melamine and its analogs has become a threat to the world. In 2008, the misuse of melamine with infant formula caused serious effects on babies of China. Thereafter, the government of China and the US Food and Drug Administration (FDA) limited the use of melamine of 1 mg/kg for infant formula and 2.5 mg/kg for other dairy products. Similarly, the World Health Organization (WHO) has also limited the daily intake of melamine of 0.2 mg/kg body weight per day. Many sensory schemes have been proposed by the scientists for carrying out screening on melamine poisoning. Among them, nanomaterial-based sensing techniques are very promising in terms of real-time applicability. These materials uncover and quantify the melamine by means of diverse mechanisms, such as fluorescence resonance energy transfer (FRET), aggregation, inner filter effect, surface-enhanced Raman scattering (SERS), and self-assembly, etc. Nanomaterials used for the melamine determination include carbon dots, quantum dots, nanocomposites, nanocrystals, nanoclusters, nanoparticles, nanorods, nanowires, and nanotubes. In this review, we summarize and comment on the melamine sensing abilities of these nanomaterials for their suitability and future research directions.

Research on a Dual-Mode Infrared Liquid-Crystal Device for Simultaneous Electrically Adjusted Filtering and Zooming

A new dual-mode liquid-crystal (LC) micro-device constructed by incorporating a Fabry⁻Perot (FP) cavity and an arrayed LC micro-lens for performing simultaneous electrically adjusted filtering and zooming in infrared wavelength range is presented in this paper. The main micro-structure is a micro-cavity consisting of two parallel zinc selenide (ZnSe) substrates that are pre-coated with ~20-nm aluminum (Al) layers which served as their high-reflection films and electrodes. In particular, the top electrode of the device is patterned by 44 × 38 circular micro-holes of 120 μm diameter, which also means a 44 × 38 micro-lens array. The micro-cavity with a typical depth of ~12 μm is fully filled by LC materials. The experimental results show that the spectral component with needed frequency or wavelength can be selected effectively from incident micro-beams, and both the transmission spectrum and the point spread function can be adjusted simultaneously by simply varying the root-mean-square value of the signal voltage applied, so as to demonstrate a closely correlated feature of filtering and zooming. In addition, the maximum transmittance is already up to ~20% according the peak-to-valley value of the spectral transmittance curves, which exhibits nearly twice the increment compared with that of the ordinary LC-FP filtering without micro-lenses.

Non-targeted NIR spectroscopy and SIMCA classification for commercial milk powder authentication: A study using eleven potential adulterants

A non-targeted detection method using near-infrared (NIR) spectroscopy combined with chemometric modeling was developed for the rapid screening of commercial milk powder (MP) products as authentic or potentially mixed with known and unknown adulterants. Two benchtop FT-NIR spectrometers and a handheld NIR device were evaluated for model development. The performance of SIMCA classification models was then validated using an independent test set of genuine MP samples and a set of gravimetrically prepared mixtures consisting of MPs spiked with each of eleven potential adulterants. Classification models yielded 100% sensitivities for the benchtop spectrometers. Better specificity, which was influenced by the nature of the adulterant, was obtained for the benchtop FT-NIR instruments than for the handheld NIR device, which suffered from lower spectral resolution and a narrower spectral range. FT-NIR spectroscopy and SIMCA classification models show promise for the rapid screening of commercial MPs for the detection of potential adulteration.

Detection of melamine in milk powder using MCT-based short-wave infrared hyperspectral imaging system

Extensive research has been conducted on non-destructive and rapid detection of melamine in powdered foods in the last decade. While Raman and near-infrared hyperspectral imaging techniques have been successful in terms of non-destructive and rapid measurement, they have limitations with respect to measurement time and detection capability, respectively. Therefore, the objective of this study was to develop a mercury cadmium telluride (MCT)-based short-wave infrared (SWIR) hyperspectral imaging system and algorithm to detect melamine quantitatively in milk powder. The SWIR hyperspectral imaging system consisted of a custom-designed illumination system, a SWIR hyperspectral camera, a data acquisition module and a sample transfer table. SWIR hyperspectral images were obtained for melamine-milk samples with different melamine concentrations, pure melamine and pure milk powder. Analysis of variance and the partial least squares regression method over the 1000-2500 nm wavelength region were used to develop an optimal model for detection. The results showed that a melamine concentration as low as 50 ppm in melamine-milk powder samples could be detected. Thus, the MCT-based SWIR hyperspectral imaging system has the potential for quantitative and qualitative detection of adulterants in powder samples.

Effects of Wet-Blending on Detection of Melamine in Spray-Dried Lactose

During the development of rapid screening methods to detect economic adulteration, spray-dried milk powders prepared by dissolving melamine in liquid milk exhibited an unexpected loss of characteristic melamine features in the near-infrared (NIR) and Raman spectra. To further characterize this "wet-blending" phenomenon, spray-dried melamine and lactose samples were produced as a simplified model and investigated by NIR spectroscopy, Raman spectroscopy, proton nuclear magnetic resonance (¹H NMR), and direct analysis in real time Fourier transform mass spectrometry (DART-FTMS). In contrast to dry-blended samples, characteristic melamine bands in NIR and Raman spectra disappeared or shifted in wet-blended lactose-melamine samples. Subtle shifts in melamine ¹H NMR spectra between wet- and dry-blended samples indicated differences in melamine hydrogen-bonding status. Qualitative DART-FTMS analysis of powders detected a greater relative abundance of lactose-melamine condensation product ions in the wet-blended samples, which supported a hypothesis that wet-blending facilitates early Maillard reactions in spray-dried samples. Collectively, these data indicated that the formation of weak, H bonded complexes and labile, early Maillard reaction products between lactose and melamine contribute to spectral differences observed between wet- and dry-blended milk powder samples. These results have implications for future evaluations of adulterated powders and emphasize the important role of sample preparation methods on adulterant detection.