The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of 2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients of determination (R²) were found to be 0.89–0.99 depending on the mathematical algorithm used, the data pre-processing applied and the sample type used. The corresponding values for the root mean square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively, again depending on the chemometric treatment applied to the data and sample type. In addition, adopting a qualitative approach with the spectral data and applying PCA, it was possible to discriminate between the four samples types and also, by generation of Cooman’s plots, possible to distinguish between adulterated and non-adulterated samples.     

FTIR Spectroscopy Combined with Chemometric for Analysis of Sesame Oil Adulterated with Corn Oil

Sesame oil is an edible vegetable oil derived from the sesame seed that has been used as a flavor enhancer in Southeast Asian cuisine. This highly valuable oil can be subjected to adulterations with lower price oils in order to gain economical profit. Among 10 vegetable oils evaluated using fatty acid profiles with principal component analysis, corn oil has the closest similarity in fatty acids combined together with sesame oil; therefore, corn oil is a potential adulterant in sesame oil. FTIR spectra at 1072?935 cm^?1 was chosen for quantitative analysis with acceptable values of coefficient determination (R²), root mean square errors of calibration and prediction. These combined methods using first derivative FTIR spectra in partial least square showed well quantified corn oil in sesame oil with R² (0.992), root mean square errors of calibration (0.53% v/v) and root mean square errors of prediction (1.31% v/v) values. Moreover, the Coomans plot based on Mahalanobis distance were able to discriminate between sesame oil with adulterated oils such as corn oil, grape seed oil, and rice bran oil.     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.     

A Fourier Transform Infrared Spectroscopy Method for Analysis of Palm Oil Adulterated with Lard in Pre-Fried French fries

Fourier transform infrared spectroscopy with attenuated total reflectance accessory was used to detect the presence of lard in French fries pre-fried in palm oil adulterated with lard. A Fourier transform infrared calibration model was obtained using partial least squares for prediction of lard in a blend mixture of lard and palm oil. The coefficient of determination (R²) of 0.9791 was obtained with 0.5% of detection limit. The error in calibration expressed with root mean square error of calibration was 0.979%. In addition, the error obtained during cross validation was 2.45%. A discriminant analysis test was able to distinguish between fries samples adulterated with lard and samples, which were pre-fried with palm oils. Fourier transform infrared spectroscopy is a fast and powerful technique for quantification of lard present in French fries.      

Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil

Fourier transform infrared (FTIR) spectroscopy has been developed for analysis of extra virgin olive oil (EVOO) adulterated with palm oil (PO). Measurements were made on pure EVOO and that adulterated with varying concentrations of PO (1.0–50.0% wt./wt. in EVOO). Two multivariate calibrations, namely partial least square (PLS) and principle component regression (PCR) were optimized for constructing the calibration models, either for normal spectra or its first and second derivatives. The discriminant analysis (DA) was used for classification analysis between EVOO and that adulterated with PO and the other vegetable oils (palm oil, corn oil, canola oil, and sunflower oil). Frequencies at fingerprint region, especially at 1500–1000 cm^?1, were exploited for both quantification and classification. Either PLS or PCR at first derivative spectra revealed the best calibration models for predicting the concentration of adulterated EVOO samples, with coefficient of determination (R²) of 0.999 and root mean standard error of cross validation (RMSECV) of 0.285 and 0.373, respectively. DA was able to classify pure and adulterated samples on the basis of their FTIR spectra with no misclassified group obtained. In addition, DA was also effective enough to classify EVOO samples as the distinct group from the evaluated other vegetable oils.     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.     

Identification and Detection of Adulterated Camellia Oleifera Abel. Oils by Near Infrared Transmittance Spectroscopy

Under the serious circumstances of Camellia oleifera adulteration, the accurate examination for quality trait of C. oleifera oil is extremely urgent. The use of near infrared transmittance spectroscopy as a rapid and cost-efficient classification technique for the authentication of Camellia oil was investigated. At the same time, the feasibility of near infrared transmittance spectroscopy for the rapid determination of soybean oil and maize oil adulterated in binary and ternary system Camellia oils was explored. The results showed that identifications was made based on the slight difference in raw near infrared transmittance spectra in Camellia oils, soybean oils, maize oils, and those adulterated with soybean and maize oil with discriminant equations techniques. Furthermore, the performance of near infrared transmittance spectroscopy models for binary and ternary system adulterated Camellia oils was satisfactory. Moreover, the near infrared transmittance spectroscopy calibration model of soybean oil (0–50%) in binary system adulterated Camellia oils was the best, and correlation coefficients of the cross-validation (R_cv) was 0.99999. For the near infrared transmittance spectroscopy calibration model of maize oil in binary system (0–50%) and ternary system (0–40%) adulterated Camellia oils, the R_cv were 0.99996 and 0.99961, respectively. In addition, the coefficients of external validation for three models were obtained (0.9998, 0.9999, and 0.9967, respectively). In all, near infrared transmittance spectroscopy could be conducted to identify Camellia oils and detect soybean oil and maize oil adulterated in binary and ternay system Camellia oils from the methodology.     

“Global” and “local” predictions of dairy diet nutritional quality using near infrared reflectance spectroscopy

The objective of the study was to evaluate performance of classic (global) and innovative (local) calibration techniques to monitor cattle diet, based on fecal near infrared reflectance spectroscopy (NIRS). A 3-yr on-farm survey (2005-2008) was carried out in Vietnam and La Reunion Island to collect animal, feed intake, and feces excretion data. Feed and feces were scanned by a Foss NIRsystem 5000 monochromator (Foss, Hillerød, Denmark) to estimate diet characteristics and nutrient digestibility. A data set including 1,322 diet-fecal pairs was built and used to perform global and local calibrations. Global equations gave satisfactory accuracy [coefficient of determination (R²) >0.8, 10% ≤ relative standard error of prediction (RSEP) ≤20%], whereas local equations gave good accuracy (R² >0.8, RSEP <10%) or excellent accuracy (R² >0.9, RSEP <10%) for the prediction of diet intake, quality, and digestibility. When validating the equations using the external individual data, both techniques were robust, with similar RSEP (8%) and R² (0.82) values. The predictive performance of global and local equations was improved (RSEP = 5% and R² = 0.90) when averaged animal data from farm, visit, and similar milk production were used. In particular, local equations reduced RSEP by 43% and increased R² by 15%, on average, compared with those obtained from individual data. The low RSEP (4%), high R² (0.96), and good ratio performance deviation (RPD = 5) illustrated the excellent accuracy and robustness of the local equations. Findings suggest the ability of fecal NIRS to successfully and more accurately predict diet properties (intake, quality, and digestibility) with local calibration techniques compared with classic global techniques, especially on an averaged data set. Local calibration techniques represent an alternative promising method and potentially a decision support tool to decide whether diets meet dairy cattle requirements or need to be modified.     

Discrimination of olive oil adulterated with vegetable oils using dielectric spectroscopy

The study focused on application of dielectric spectroscopy to identify the adulteration of olive oil. The dielectric properties of binary mixture of oils were investigated in the frequency range of 101 Hz–1 MHz. A partial least squares (PLS) model was developed and used to verify the concentrations of the adulterant. Furthermore, the principal component analysis (PCA) was used to classify olive oil sample as distinct from other adulterants based on their dielectric spectra. The results showed that the dielectric spectra of binary mixture of olive oil spiked with other oils increased with increasing concentration of soy, corn, canola, sesame, and perilla oils from 0% to 100% (w/w) over the measured frequency range. PLS calibration model showed a good prediction capability for the concentrations of the adulterant. For olive oil adulterated with soy oil, the results showed that the RMS was 0.053, sd(RMS), 0.017 and Q² value was 0.967. PCA classification plots for all oil samples showed clear performance in the differentiation for the different concentrations of the adulterant. Each of the oil samples could be easily grouped in different clusters using dielectric spectra. From the results obtained in this research, dielectric spectroscopy could be used to discriminate the olive oil adulterated with the different types of the oils at levels of adulteration below 5%.     

Are virgin olive oils obtained below 27 °C better than those produced at higher temperatures?

Within the European Union, indications of ‘first cold pressing’ and ‘cold extraction’ can only be used for virgin olive oil (VOO) obtained below 27 °C from mechanical processing. Three different malaxing temperatures (25, 35 and 45 °C) are here evaluated for the quality of the VOO obtained in a continuous industrial plant. The oils were stored at room temperature in the dark for 12 months. Initially, oil obtained from a blend of Frantoio/Leccino cultivars (F/L) had higher acidity and peroxide levels and lower phenolic content than a Coratina cultivar (Cor). The oxidative stability of the oils positively correlated with malaxation temperature (F/L, R² = 0.818; Cor, R² = 0.987) as the phenolic content was directly proportional to the temperature (F/L, R² = 0.887; Cor, R² = 0.992). Only oils obtained at 45 °C were rejected because of ‘heated or burnt’ off-flavour. Decarboxymethylation of secoiridoids and further hydrolysis of phenolic esters occurred during storage. The oxidation products of derivatives of tyrosol and hydroxytyrosol were detected after nine months in both the F/L and Cor samples. Thus, VOO obtained at a processing temperature lower than 27 °C does not show higher chemical and sensory qualities than VOO obtained at 35 °C.     

The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil

Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000–650 cm⁻¹ on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R²) of 0.999.     

Fourier Transform Infrared Spectroscopy Combined with Multivariate Calibrations for the Authentication of Avocado Oil

Avocado oil is one of the functional oils having high quality and high price in the market. This oil shows many benefits for the human health and is applied in many cosmetic products. The authentication of avocado oil becomes very important due to the possible adulteration of avocado oil with other lower priced oils, such as palm oil and canola oil. In this study, Fourier transform infrared spectroscopy using attenuated total reflectance in combination with chemometrics techniques of partial least squares and principal component regression is implemented to construct the quantification and classification models of palm oil and canola oil in avocado oil. Partial least squares at the wavenumbers region of 1260–900 cm^–1 revealed the best calibration models, having the highest coefficient of determination (R² = 0.999) and the lowest root mean square error of calibration, 0.80%, and comparatively low root mean square error of prediction, 0.79%, for analysis of avocado oil in the mixture with palm oil. Meanwhile, the highest R², root mean square error of calibration, and root mean square error of prediction values obtained for avocado oil in the mixture with canola oil at frequency region of 3025–2850 and 1260–900 cm^–1 were 0.9995, 0.83, and 0.64%, respectively.     

Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy

A hand held Raman probe was used to predict shear force (SF) of fresh lamb m. semimembranosus (topside). Eighty muscles were measured at 1 day PM and after a further 4 days ageing (5 days PM). At 1 day PM sarcomere length (SL) and particle size (PS) were measured and at 5 days PM, SF, PS, cooking loss (CL) and pH were also measured. SF values were regressed against Raman spectra using partial least squares regression and against traditional predictors (e.g. SL) using linear regression. The best prediction of SF used spectra at 1 day PM which gave a root mean square error of prediction (RMSEP) of 11.5 N (Null = 13.2) and the squared correlation between observed and cross validated predicted values (R²_cv) was 0.27. Prediction of SF based on the traditional predictors had smaller R² values than using Raman spectra justifying further study on Raman spectroscopy.     

Characterization and authentication of a novel vegetable source of omega-3 fatty acids,sacha inchi (Plukenetia volubilis L.) oil

Consumption of omega-3 fatty acids (ω-3’s), whether from fish oils, flax or supplements, can protect against cardiovascular disease. Finding plant-based sources of the essential ω-3’s could provide a sustainable, renewable and inexpensive source of ω-3’s, compared to fish oils. Our objective was to develop a rapid test to characterize and detect adulteration in sacha inchi oils, a Peruvian seed containing higher levels of ω-3’s in comparison to other oleaginous seeds. A temperature-controlled ZnSe ATR mid-infrared benchtop and diamond ATR mid-infrared portable handheld spectrometers were used to characterize sacha inchi oil and evaluate its oxidative stability compared to commercial oils. A soft independent model of class analogy (SIMCA) and partial least squares regression (PLSR) analyzed the spectral data. Fatty acid profiles showed that sacha inchi oil (44% linolenic acid) had levels of PUFA similar to those of flax oils. PLSR showed good correlation coefficients (R² > 0.9) between reference tests and spectra from infrared devices, allowing for rapid determination of fatty acid composition and prediction of oxidative stability. Oils formed distinct clusters, allowing the evaluation of commercial sacha inchi oils from Peruvian markets and showed some prevalence of adulteration. Determining oil adulteration and quality parameters, by using the ATR-MIR portable handheld spectrometer, allowed for portability and ease-of-use, making it a great alternative to traditional testing methods.     

Classification and analysis of citrus oils by NIR spectroscopy

A new NIRS method is introduced for the determination of valuable components in various citrus oils. Spectra of grapefruit, orange, mandarin, lemon and lime oils in the range from 1100 to 2500 nm have been registered. Applying principal component analysis to the spectral data a good separation of the different fruit oil types can be achieved. The application of multivariate statistics in conjunction with analytical reference data leads to good NIR calibration results. For the main components (e.g. limonene, γ-terpinene, sabinene) and general chemical–physical parameters (e.g. optical rotation value, aldehyde content) standard errors are in the range of the applied reference method. The multiple coefficients of determination (R²) for components with an amount of more than 1.5% are generally >0.95. Furthermore reliable in-process methods for the determination of the individual nootkatone and aldehyde contents during the isolation and purification process from grapefruit and orange oil are presented.     

Detection of the adulteration of olive oils by solid phase microextraction and multidimensional gas chromatography

The presence or absence of filbertone in 21 admixtures of olive oil with virgin and refined hazelnut oils obtained using various processing techniques from different varieties and geographical origins was evaluated by solid phase microextraction and multidimensional gas chromatography (SPME–MDGC). The obtained results showed that the sensitivity achievable with the proposed procedure was enough to detect filbertone and, hence, to establish the adulteration of olive oil of different varieties with virgin hazelnut oils in percentages of up to 7%. The very low concentrations in which filbertone occurs in some refined hazelnut oils made difficult its detection in specific admixtures. In any case, the minimum adulteration level to be detected depends on the oil varieties present in the adulterated samples. In the present study, the presence of R- and S-enantiomers of filbertone could be occasionally detected in olive oils adulterated with 10–20% of refined hazelnut oil.     

Rapid detection and quantification of soya bean oil and common sugar in bovine milk using attenuated total reflectance–fourier transform infrared spectroscopy

Attenuated total reflectance–Fourier transform infrared spectroscopy, along with chemometrics, were used to detect and quantify soya bean oil (SO) and sugar (CS) adulteration in milk. Bovine milk was artificially adulterated with SO (0.2–2.0%; v/v) and CS (1–10%; w/v) separately. Spectra revealed significant differences in specific wavenumber regions (SO: 1450–1250 cm^?1; CS: 1200–900 cm^?1). Soya bean oil adulteration was best predicted in wavenumber range of 1262–1164 cm^?1, using partial least square regression (coefficient of determination (R²: 0.90 and 0.88 for calibration and validation, respectively). Common sugar adulteration was best predicted in wavenumber range of 1010–910 cm^?1 (R²: 0.99 for calibration and validation) using partial least square.     

Adulteration detection of argan oil by inductively coupled plasma optical emission spectrometry

The singularity of the trace element profile of argan oil has been demonstrated by means of inductively coupled plasma optical emission measurement in combination with different chemometric approaches. The ability of multivariate analysis methods; such as hierarchical cluster analysis (HCA), principal component analysis (PCA), classification trees using Chi-squared Automatic Interaction Detector (CHAID) and discriminant analysis (DA) to achieve edible oils classification based on its type or variety from their elemental content have been investigated. The calculations were performed using 16 variables (contents of Na, Mg, Al, K, Ca, Ti, Fe, Co, Ni, Cu, Zn, Cd, Pr, Sm, Er and Bi at μg g⁻¹ level determined by ICP-OES). HCA is able to differentiate sunflower oil samples from the rest, however the discrimination of argan oil from olive, seeds and soya oils based on their different trace element composition is hard to achieve. The PCA analysis shows three different classes in the multidimensional space (PC1-3) representing sunflower, argan and a third group comprising olive, seeds and soya oils. CHAID method allows separating the entire vegetable oil dataset, providing a correct re-substitution rate of 94.12% for argan oil using only the concentration of K. DA performed using the same variables, provides also an acceptable average accuracy results of 93.65%, by the re-substitution method. DA has been successfully applied to the analysis adulterated argan oil by addition of cheaper vegetable oils.     

Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy

The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500 nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (R²) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (R²<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.     

Identification of Geographical Origin of Olive Oil Using Visible and Near-Infrared Spectroscopy Technique Combined with Chemometrics

A novel multivariate calibration method was developed to identify the geographical origin of olive oils using visible and near-infrared spectroscopy (Vis/NIRS) on the wavelength between 325 and 1,075 nm. Direct orthogonal signal correction (DOSC) preprocessing method was performed to reduce the influence of light scattering, background noise, and baseline shift during experiment. An optimization method of genetic algorithms (GAs) was used to select informative variables from the full spectrum, and 37 informative variables were selected for partial least squares (PLS) regression analysis. The prediction results indicated that the developed DOSC-GA-PLS model can be successfully employed to predict geographical origin of olive oils. Moreover, the use of GA simplified and improved the predictive ability of the model. The prediction statistical parameters were correlation coefficient ( R_\textP² R_{\text{P}}^2 ) of 0.987, relative deviation was 0.093, and the recognition ratio was 97%. It was concluded that Vis/NIRS combined with DOSC-GA-PLS method can be successfully used to determine the geographical origin of olive oils accurately and quickly.