Analysis Of Adulteration Research Articles

A modified version of the sensory Pivot technique as a possible tool for the analysis of food adulteration: A case of coffee

AbstractThe objective of this study was to validate the Pivot sensory technique for sensory analysis of adulterated coffees. The Pivot technique was modified (instructions and determination of the values of the unadulterated sample considered Pivot). A total of 130 consumers evaluated five pairs of samples (Pivot vs. five coffee samples adulterated with black bean coffee). A sensory vocabulary was generated for the construction of confidence ellipses and the calculation of sample classification percentage between the Pivot sample and the adulterated samples. A total of 52 sensory attributes were generated (28 aromas, 5 basic tastes, and 19 flavors). As adulteration increased, a decrease in the number of aromas, high discrimination, and a classification percentage of 68% between the Pivot sample and the adulterated samples. The Pivot technique can be a tool to study the effect of adulteration in coffees.Practical ApplicationsThe use of the modified Pivot technique in combination with the determination of the values of the Pivot (unadulterated sample) sample can be very useful for the identification of the sensory effect caused by the adulteration of different foods, including those that are protected by some quality seal for quality or for quality control. The scope of this research may be of interest to the industry, producers, and academia.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis.

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique’s principle and focused on said technologies’ applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

Adulteration of Gabus (Channa striata) fish oil with corn oil and palm oil: the use of FTIR spectra and chemometrics

The adulteration practice in fats and oils industry can be in the form of addition or substitution high quality oils such as Gabus Fish oil (GFO) with lower price oils. This research highlighted the application of FTIR spectroscopy combined with multivariate calibrations and discriminant analysis (DA) for quantitative analysis and classification of oil adulterants of palm oil (PO) and corn oil (CO) in GFO. The methods involved preparation of training/calibration and validation samples, scanning of samples using FTIR spectrophotometer using attenuated total reflectance, development of calibration and validation models for quantitative analysis of oil adulterants assisted with multivariate calibrations and classification between genuine GFO and GFO adulterated with PO and CO using discriminant analysis. The quantitative analysis of PO in ternary mixtures with CO as an adulterant in GFO was carried out using first derivative spectra at wavenumbers of 3200-600 cm-1 assisted with partial least square (PLS), while quantitative analysis of CO in ternary mixture with PO and GFO was performed using first derivative spectra at wavenumbers region of 3200-2700 cm-1 assisted with principle component regression (PCR). The R2 values for the correlation between actual and predicted values of PO and CO in ternary mixtures either in calibration or prediction samples were of > 0.97 with low errors. In addition, DA using the same wavenumbers region as used in the quantitative analysis could classify or discriminate genuine GFO and GFO mixed/adulterated with PO and CO with an accuracy level of 100%. FTIR spectroscopy using suitable wavenumbers region combined with PLS, PCR and DA could be proposed as analytical tools for quantification and classification of oil adulterants in GFO.

Analysis of Pork Adulteration In Recycled Frying Oils Using Raman Spectroscopy

Abstract Unscrupulous food business operators may use recycled frying oil to save costs. Of particular concern is the recycled frying oil is usually taken from nonhalal food premises which should not be used by halal food premises, and indeed may posing health treats to consumers. Hence, the objective of this paper is to analyse pork adulteration in recycled frying oils by using the combination of Raman spectroscopy and Principal Component Analysis (PCA). Samples of frying oils from homemade fried pork, fried chicken, fried fish and fried banana were analyzed. Spiked samples were prepared by adding frying oil from homemade fried pork ranging from 10% to 50% (v/v) to frying oils from homemade fried chicken, fried fish and fried banana. The results found that Raman spectroscopy and PCA are able to differentiate adulterated frying oil and unadulterated frying oils. However, it could not distinguish the percentage of pork adulteration in the spiked samples. This method would beneficial to ensure food integrity in the frying oils.

Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine learning

Growing interest in food quality and traceability by regulators as well as consumers demands advances in more rapid, versatile and cost-effective analytical methods. Milk, as most food matrices, is a heterogeneous mixture composed of metabolites, lipids and proteins. One of the major challenges is to have simultaneous, quantitative detection (profiling) of this panel of biomolecules to gather valuable information for assessing food quality, traceability and safety. Here, for milk analysis, atmospheric pressure matrix-assisted laser desorption/ionization employing homogenous liquid sample droplets was used on a Q-TOF mass analyzer. This method has the capability to produce multiply charged proteinaceous ions as well as highly informative profiles of singly charged lipids/metabolites. In two examples, this method is coupled with user-friendly machine-learning software. First, rapid speciation of milk (cow, goat, sheep and camel) is demonstrated with 100% classification accuracy. Second, the detection of cow milk as adulterant in goat milk is shown at concentrations as low as 5% with 92.5% sensitivity and 94.5% specificity.

Simultaneous analysis of multiple adulterants in milk using microfluidic paper-based analytical devices.

This study reports the simultaneous colorimetric detection of urea, H2O2, and pH in milk samples using microfluidic paper-based analytical devices (μPADs) fabricated through a craft cutter printer. Paper-based devices were designed to contain three detection zones interconnected to a sampling zone by microfluidic channels. Colorimetric analysis was performed using images digitalized through an office scanner. The volumes of chromogenic and sample solutions were optimized, and the best colorimetric performance was achieved by adding 0.5 and 10 μL into detection and sampling zones, respectively. Simultaneous assays were then carried out, and the recorded responses revealed a linear behavior in the concentration ranges from 0-30.0 mmol L-1, 0-10.0 mmol L-1 and 6.0-9.0 for urea, H2O2 and pH, respectively. The limit of detection values obtained for urea and H2O2 were 2.4 mmol L-1 and 0.1 mmol L-1, respectively. For pH measurements, colorimetric assay allowed the monitoring of solution pH with a resolution of 0.25 units. The use of μPADs to detect target adulterants exhibited suitable reproducibility (RSD ≤ 6.0%), accuracy (91-102%) and no cross-reaction occurrence. When compared to reference techniques, colorimetric assays did not reveal a significant difference at a confidence level of 95%. As a proof-of-concept, the feasibility of the proposed approach was successfully demonstrated through the analysis of potential adulterants in sixteen milk samples, which were tested without any pretreatment requirement. Based on the achievements, μPADs in conjunction with colorimetric measurements emerge as a powerful tool for rapid screening of potential adulterants in milk.

Assessment of Adulteration of Gasoline (MSP) and Diesel (AGO), in Selected Fuel Stations in Kisii County

The major transport fuels in Kenya are gasoline and diesel. A complaint on adulteration of these fuels at the point of sale or during transportation is a common occurrence in the country. The present studies on analysis of petroleum adulteration does not give a scope of what is happening at the newly and upcoming petrol stations outside the capital of city. Purposeful sampling was carried out on investigating gasoline and diesel adulteration sold at selected fuel stations in in a case study of Kisii County, and whether these products are within the standards set by Kenya bureau of Standards (KEBs). Samples of gasoline and diesel were collected from selected five fuel stations and two laboratory testing methods of ASTM D86 (Distillation) and ASTM D1298 (Density determination) were conducted at Vivo Energy Company laboratory in Nairobi. Currently, the number of fuel stations selling Gasoline and diesel is enormous in Kisii region. They involve four major oil companies namely, Shell, Total, Kenol/Kobil, Oil Libya, and now KNOCK which dominate the market as well as locally owned ones. Therefore, the sampling method that was employed to select randomly five specific filling stations comprising of one major and four Minor companies representing the population with parameters of interest, for analysis in this study.

GC-MS analysis of Adulterants in Captagon Tablet

In secret laboratories various synthetic or semi-synthetic amphetamine-type stimulants (ATS) are produced and processed. Stimulants of the form of amphetamine (ATS) are the second most common illegal drugs used worldwide, following cannabis. ATS production has increased worldwide, particularly from the Middle East. Fenethylline (CaptagonTM), a derivative of amphetamine, is commonly marketed as a street drug. Objective: this research aimed at applying gas chromatography / mass spectrometry (GC / MS) for fenethylline profiling of confiscated samples from the Saudi market. Methodology: The study of GC / MS was conducted on a general-purpose column (30 mm 0.25 mm i.d) filled with 0.25 mm cross bond, 5% diphenyl dimethyl polysiloxane (Rtx-5MS). The mass was worked in the process of impacting the electron. Results and discussion: Analyzed samples showed positive results for amphetamine in concentrations ranging from 0.07 to 43.02 percent of the substance in 60 percent of the samples. Caffeine was also present in 60 per cent of the samples at levels between 0.61 and 60.31 per cent. In the MS of the other active compounds were recorded in different proportions. Lastly, the presence of other substances in fenethylline samples may contribute to intoxication.

Analysis of pork adulteration in the corned products using FTIR associated with chemometrics analysis

Meat-based foods such as beef corned became one of the targets of counterfeiting with pork because relatively cheaper. This becomes a serious problem for Muslims, especially in Indonesia. One method that can be used to detect fat was Fourier transform infrared (FTIR) spectrophotometry. The purpose of this study was to quantitatively analyze and a group of corned beef and corned pork using FTIR spectrophotometry combined with chemometrics. Reference samples corned pork-beef made of 7 various concentration (0%, 25%, 35%, 50%, 65%, 75%, 100%) and 6 product samples purchased in the Umbulharjo, Yogyakarta. Extraction was carried out by the soxhlet apparatus using n-hexane technical solvent for 4 – 5 hours at 69 – 70 °C. Fat analyzed using FTIR spectrophotometry for generating infrared spectral data then processed with Partial least square (PLS) chemometrics for quantitative analysis and Principal component analysis (PCA) for grouping. Results of quantitative analysis chemometrics PLS, selected areas fingerprints for analysis corned pork-beef was 1180 – 730 cm-1 with R2 0.9833; RMSEC 2.06%; RMSEP 1.65% and RMSECV 2.22%. The results of PCA showed groupings in different quadrants between corned pork 100% and corned beef 100%. Results showed that FTIR spectrophotometry combined with chemometrics can be used for quantitative analysis and grouping of pork corned and beef corned on the market but it can not identify pork in corned after choking process.

Simultaneous determination of fructose, glucose and sucrose by solid phase extraction-liquid chromatography-tandem mass spectrometry and its application to source and adulteration analysis of sucrose in tea

A solid phase extraction-liquid chromatography-tandem mass spectrometry (SPE-LC–MS/MS) method was developed for simultaneous determination of fructose, glucose and sucrose in tea. Tea samples were ultrasonically extracted with ultrapure water and cleaned up by Oasis PRIME HLB SPE cartridge. The separation of the analytes was performed on XBridge BEH Amide column (2.1 × 150 mm, 5 μm) with isocratic elution. Detection of the target compounds were carried out under the multiple reaction monitoring (MRM) mode with negative electrospray ionization (ESI−). The qualitative screening was achieved with retention time and qualitative ion, then confirmation analysis with peak area and quantitative ion. The limits of detection (LODs, S/N ≥ 3) of fructose, glucose and sucrose were 0.02 mg/L, 0.03 mg/L and 0.08 mg/L, respectively. The average recoveries varied from 92.1 %–108.5 % for three levels. Linearity was obtained with the correlation coefficients greater than 0.992 for three target compounds. The established method was rapid, simple and accurate which was suitable for batch determination of fructose, glucose and sucrose in tea. A total of 213 tea samples from 7 countries were analysed and the results statistically analyzed using MATLAB software. Moreover, source and adulteration analysis of sucrose in tea was carried out. Sucrose in tea is mainly derived from photosynthesis during growth, hydrolysis of polysaccharides during the processing and artificial adulteration, once the sucrose content in tea exceeds 24.3 g/kg, it may involve artificial adulteration.

Effect of chlorophyll fluorescence quenching on quantitative analysis of adulteration in extra virgin olive oil

Extra virgin olive oil (EVOO) is both edible oil and health care product. Adulteration in high quality vegetable oil is a ubiquitous fraud, especially in the market of EVOO. Spectroscopy is an effective means to realize the rapid detection of adulteration in EVOO, but the accuracy of quantitative analysis is the short board of spectral detection. Traditional Raman spectroscopy is used to detect the adulteration of EVOO by analyzing the content of monounsaturated fatty acids. However, high oleic acid content is not unique to EVOO. Confocal Raman and Fluorescence Spectroscopy (CRFS) was employed to characterize EVOO along with potential adulterant oils based on their Oleic acid and photosensitive substances content. Statistical analysis of these Oleic acid and photosensitive substances using Multiple Linear Regression (MLR) allowed for a rapid approach to determine EVOO authenticity. The quantitative analysis model of adulteration in EVOO was established using this approach, and the RMSE was 0.0068, and the R-Squaredof external Prediction was 0.9996. In addition, Fluorescence quenching which interfered with the quantitative analysis of chlorophyll was found in the adulteration experiment of EVOO. Compared to traditional Raman methods, CRFS with MLR involves minimal sample preparation combined with fast analysis.

A Novel Chemometrics Method for Halal Authentication of Gelatin in Food Products

The issue of halal authenticity of food and pharmaceutical products has become a concern to Muslims due to fraud and unknown sources of ingredients. The most common non-halal ingredient found in some food products and also in capsule medicine is porcine gelatin. Other sources of gelatin are bovine and fish. Numerous methods have been developed to identify between bovine, porcine, and fish gelatins due to the concern on the halal authenticity issue. Fourier transform infrared (FTIR) spectroscopy is one of the established techniques in food adulteration analysis. However, further analysis using chemometrics method is required due to the large similarities of the gelatin spectra. This paper introduces a novel chemometrics method using Fuzzy Autocatalytic Set (FACS) to determine the FTIR spectra of bovine, porcine, and fish gelatins. The unique dominant regions for these gelatins were identified. The bovine, porcine, and fish gelatins possessed different dominant region when compared to each other. The FACS method offers simple and rigorously computational procedure to differentiate bovine, porcine, and fish gelatins for halal authentication purposes.

Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs).

Geographical discrimination and adulteration analysis play significant roles in edible oil analysis. A novel method for discrimination and adulteration analysis of edible oils were proposed in this study. The two-dimensional correlation spectra of edible oils were obtained by solvents perturbation and the convolutional neural networks (CNNs) were constructed to analyze the synchronous and asynchronous correlation spectra of the edible oils. The differences for geographical origins of oils or oil types could be amplificated through the networks. For different networks, the layer sequences and the filter number of convolutional layers may affect the analysis results. A group of sesame oils from different geographical origins and a group of olive oils adulterated by other vegetable oils were adopted to evaluate the proposed method. The results show that the proposed method may provide an alternative method for edible oil discrimination and adulteration analysis in practical applications. For the two datasets, the prediction accuracy could be 97.3% and 88.5%, respectively.

Simultaneous detection of trace adulterants in food based on multi-molecular infrared (MM-IR) spectroscopy

Simultaneously rapid detection of trace adulterants in the complex systems of food without extraction is considered highly challenging. Herein, a high-throughput and rapid methodology, multi-molecular infrared (MM-IR) spectroscopy was developed for simultaneous detection of multiple trace adulterants in food. Flour was applied to demonstrate the capabilities of MM-IR with spatial resolution, spectral qualitative and quantitative analysis. Signals of 5 trace adulterants (rongalit, potassium bromate, borax, aluminum potassium sulfate and fluorescent brighter) were spatially revealed by IR hyperspectral imaging with group-peak matching, and further unveiled spectrally with second derivative two dimensional correlation infrared (SD-2DCOS IR) spectroscopy for higher resolution. Moreover, quantitative analysis of trace adulterants was conducted with partial least squares (PLS) modeling in ppm level. Composed of the above techniques and a series of resolution enhancement techniques (MW-2DCOS IR, 2T-2DCOS IR, etc.), MM-IR presented significant advantages on simultaneous detection of trace adulterants in food and therefore possessed the potential for food comprehensive analysis.

Rapid Determination of Saffron Grade and Adulteration by Thin-Layer Chromatography Coupled with Raman Spectroscopy

Saffron, one of the most expensive spices around the world, is highly vulnerable to economic adulteration in its powder form. Plant materials and artificial colorants are commonly used as adulterants to reduce saffron concentration as well as maintaining color strength in powdered saffron products. However, current analytical methods lack the ability to test samples accurately onsite. Therefore, the objective of this study is to develop a fast screening and quantification method combining thin-layer chromatography (TLC) and Raman spectroscopy for saffron powder analysis, including quantifying the main colorant crocin content and identifying possible adulterants out of a lab setting. A droplet of aqueous extract of saffron powder was deposited on a TLC plate, exhibiting a uniform yellow round pattern on the TLC plate. The content of crocin was analyzed using the ISO 3632 method and Raman spectroscopy separately. A quantitative model based on Raman spectra was established and validated using partial least square (PLS) with R of 0.9955 and 0.9929 for calibration and validation of the PLS plot, respectively. Possible adulterants such as powders of safflower, turmeric powder, red 40 (allura red), and yellow 5 (tartrazine) were mixed with saffron powder at various degrees. Their water extracts exhibited unique patterns under bright ambient light and UV light that can be discriminated from that of pure saffron samples through visual observation and principal component analysis (PCA) based on the L*a*b* values. We also estimated the lowest adulteration degrees that can be detected were 2.59% for Allura red, 4.15% for tartrazine, 31.01% for safflower, and 41.98% turmeric, respectively, using the PLS analysis. Further identification was evaluated using Raman spectroscopy, which provided fingerprint spectra for all adulterants except safflower. Combining TCL pattern and Raman spectroscopy, samples with identical optical properties to pure saffron but adulterated with multiple adulterants were analyzed and successfully detected. This study demonstrates the potential practical application of this method in rapid analysis of saffron quality and adulteration.

Milk quality along dairy farming systems and associated value chains in Kenya: An analysis of composition, contamination and adulteration

Poor milk safety constitutes a persistent public health risk in Kenya. Poor milk composition, microbial contamination and adulteration is a constraint to dairy sector development. We hypothesise that variation in milk quality and safety depends on variation between farming systems. We argue that this variation between farming systems is associated with spatial location which affects the agro-ecological conditions and the availability of labour and land.We used a spatial framework based on the distance to urban markets to distinguish the following farming systems: relatively intensive dairy systems in urban locations (UL), semi-intensive dairy systems in mid-rural locations (MRL) and extensive dairy systems in extreme rural locations (ERL). We aimed to investigate the variation in the quality of raw milk in these dairy farming systems and associated value chains in central Kenya. For this reason, we combined several methods such as participatory rural appraisal, participant observation, and milk physicochemical and microbiological analyses to collect data. Milk samples were collected at the informal and informal value chain nodes - farms, informal collection centres, informal retailing centres including milk vending machines, and formal bulking centres - where milk changes hands between value chain actors. Milk quality was compared to standards recommended by the Kenya Bureau of Standards (KeBS).There were no differences in the quality of raw milk between locations or between nodes. The overall milk physicochemical composition means (standard error) of the milk were within KeBS standards: fat 3.61 (0.05), protein 3.46 (0.06), solid-not fats 9.18 (0.04), density 1.031 (0.0002) and freezing point −0.597 (0.019). The protein percentage was below KeBS standards at all value chain nodes, except at the formal bulking node. There was significant contamination of milk samples: 16.7% of samples had added water, 8.8% had somatic cell count SCC above 300,000, 42.4% had E. coli, 47.9% had Pseudomonas spp., 3.3% had Staphylococcus spp. and 2.9% tested positive for brucellosis antibodies. Unsanitary milk handling practices were observed at farms and all value chains nodes. Milk physicochemical composition except for protein content meets the KeBS Standard. High levels of microbial contamination of milk pose a public health risk to consumers and show that urgent action is needed to improve milk quality.

A simple design for the validation of a FT-NIR screening method: Application to the detection of durum wheat pasta adulteration

The demand for the development of fast, easy-to-use and low-cost analytical methods for food adulteration analysis has being increasing in the last years. Although infrared spectroscopic techniques offer these advantages, the validation of screening methods requiring the application of multivariate data treatment is less frequently described in literature thus limiting their use as routine tools in control laboratories for food fraud monitoring. In this paper, an EU-validation procedure for screening methods was successfully applied to a multivariate FT-NIR spectroscopic method for the screening of durum wheat pasta samples adulterated with common wheat at the screening target concentration of 3%. Good results in terms of the cut-off value (2.32% mass fraction of soft wheat) and false suspect rates (0.1% for blanks; 13% at 1% mass fraction) demonstrated that the present validation approach would be a proof-of-strategy to be used for multivariate infrared methods applied for screening purposes.

Reliability of Peanut Oil Authenticity Identification Based on Fatty Acids and Establishment of Adulteration Analysis Model

In order to establish a highly reliable, fast and effective method for identification of adulterated peanut oil, this paper studies authenticity identification based on fatty acids. A comparison of the credibility using three different methods was conducted, namely the method of GB/T 1534 Peanut Oil, Principal Component Analysis (PCA) and Partial Least Squares analysis (PLS). Firstly, 120 samples were prepared by mixing peanut oil with corn oil, soybean oil or palm oil in different proportions. Afterwards, adulteration models were developed respectively using Principal Component Analysis (PCA) and Partial Least Squares analysis (PLS). The results show that according to the quality standard, the adulteration could be identified only when there was more than 50% soybean oil, 45% corn oil or 15% palm oil; the reliability was low. Both PCA and PLS models were more credible than the quality standards; furthermore, PLS model was more effective than PCA model, it could not only identify the adulteration by 100% when palm oil, soybean oil or corn oil was solely mixed, but also distinguish well the type of oil adulterated. The results also indicate that when peanut oil adulterated with palm oil, the content of palmitic acid and myristic acid had highly positive correlation; when that mixed with soybean oil, the content of linoleic acid and linolenic acid were highly positively correlated; when that mixed with corn oil, the content of heptachenoic acid was positively correlated, but negatively correlated with the content of behenic acid, arachidonic acid and arachidonic acid.

Detection of pork in meatballs using probe TaqMan Real-time Polymerase Chain Reaction

This study aimed to develop a TaqMan Real-Time Polymerase Chain Reaction method, using a novel primer for detection of pork adulteration in meatballs. The study is important as it described a TaqMan method for product adulteration analysis. TaqMan is known to have a more specific result compared to SYBR green analytical method. Assay in the study combined species-specific primers and TaqMan probes to targeting 153 bp fragment of D-loop mitochondrial region of pork. A specificity test was conducted on fresh tissues of pork, beef, chicken, wild boar, dog, and mouse. Meatballs as samples were prepared from a mixture of pork-beef and wild boar-beef with concentrations as follows: 5%, 10%, 25%, 75%, 90%, and 100%. The linearity and sensitivity of the method were performed by measuring the amplification curve from the dilution series, namely 1000, 200, 100, 10, 5, 1, and 0.5 pg/μL of DNA, extracted from 100% pork meatballs. A repeatability test was conducted as many as six repetitions on 100% pork and 100% wild boar meatballs. This study showed that mitochondrial D-loop species-specific primers and TaqMan probes could identify the DNA of pork and wild boar on the fresh tissues. Additionally, it also resulted in a threshold cycle (Ct) of 17.02 and 17.95 for pork, 22.22 for wild boar, while the negative result for others. The detection limit has shown 5 pg in the meatball formulation. The Relative Standard Deviation (RSD) of repeatability was 1.936% for pork, while 2.140% for wild boar. The developed method was also applied to analyzing commercial meatballs. A TaqMan real-time PCR analytical method using specific primer targeting on 153 bp fragment of the D-loop mitochondrial region could be applied as a standard method for identifying pork and wild boar in food samples intended for halal authentication studies

Quantitative analysis of honey adulteration by spectrum analysis combined with several high-level data fusion strategies

Illegal addition of syrup to honey products would refdeduce the natural nutritional value of honey. It is essential to develop convenient and accurate analytical methods to rapidly monitor products quality. This study investigated the potential usability of combining mid infrared(MIR) and Raman for the quantification of HFGS (high fructose syrup) in bass honey blends. Where in the partial least squares (PLS) model was established to predict the concentration of the adulterant. Three data fusion strategies (low, mid and high-level) were applied to take advantage of the synergistic effect of chemical information from MIR and Raman, and adopted three high-level data fusion strategies to improve the prediction accuracy of the quantitative model. After data fusion, the RMSEP of high-level (1–3) were better than MIR (1.93) and Raman (2.30). Besides, calibration coefficient of determination (R2) and the RMSECV of high-level data fusion (1–3)also outperformed Raman (0.80, 3.38) and MIR (0.95,1.63).Results showed that: (1) The chemical information obtained from MIR and Raman have complementary advantages. (2) The three high-level fusion strategies can be used as a reliable tool for quantitative analysis.