Rapid Determination of Spinal Cord Content in Ground Beef by Near-Infrared Spectroscopy

The potential of near‐infrared spectroscopy to measure spinal cord content in ground beef was investigated. Near‐infrared spectra were obtained for ground beef adulterated with 0 to 100 ppm spinal cord. A partial least square model was developed with 2nd derivative near‐infrared spectral data (5400/cm to 10000/cm). The model showed a calibration of R_val= 0.94 and a validation of R_val= 0.90. The detection limit of the model was 21 ppm, and sensitivity to detect true positive spinal cord–adulterated samples was 87%. Near‐infrared spectroscopy has the potential to identify spinal cord–adulterated ground beef.     

Application of FTIR Spectroscopy for Monitoring the Stabilities of Selected Vegetable Oils During Thermal Oxidation

Fourier transform infrared spectroscopy has been developed for rapid monitoring of the oxidative stabilities of selected vegetable oils, namely corn oil, rice bran oil, soybean oil, and sunflower oil during thermal treatment at 160°C for 120 h. There were several absorbance changes between non-oxidized and oxidized vegetable oils during thermal oxidation. Peak intensities at 3470, 1655, and 967 cm^?1 were increased; meanwhile peak intensities at 3008 and 722 cm^?1 were decreased. The R² values for the correlation between the absorbance changes at 3008 cm^?1 and the specific absorptivities of conjugated dienes for corn oil, rice bran oil, soybean oil, and sunflower oil were 0.938, 0.845, 0.978, and 0.824, respectively. The absorbance changes of Fourier transform infrared spectra at 3008 cm^?1 were also correlated with the specific absorptivities of conjugated trienes and p-anisidine values with the acceptable R² values. Compared with the conventional technique, the use of Fourier transform infrared spectroscopy for measurement the thermal oxidative stability has some advantages, i.e., it is a rapid technique and no sample preparation. In addition, Fourier transform infrared spectroscopy reduces or eliminates solvents and chemical reagents that are hazardous to human health or to the environment; therefore, Fourier transform infrared spectroscopy can support the campaign of “green analytical chemistry.”     

Use of Fourier Transform Infrared Spectroscopy in Combination with Partial Least Square for Authentication of Black Seed Oil

Fourier transform infrared spectroscopy in combination with multivariate calibration of partial least square is intended for quantitative analysis of black seed oil in binary mixture with sunflower oil and walnut oil, as well as in ternary mixture with sunflower oil and walnut oil. The spectra of black seed oil, sunflower oil, walnut oil, and their mixture with certain concentration were scanned using attenuated total reflectance at mid infrared region of 4000–650 cm^?1. For quantitatve analysis, Fourier transform infrared spectral treatment (normal or derivatives) with the highest values of coefficient of determination (R²) and the lowest values of root mean square error of calibration was selected as optimal calibration model. Partial least square at whole mid infrared region of 4000–650 cm^?1 is well suited for quantitative analysis of black seed oil either in binary mixture or ternary mixture with walnut oil and sunflower oil. Furthermore, using absorbancies at frequency region of 3009–721 cm^?1, principal component analysis is succesfully used for classification of black seed oil and that mixed with sunflower oil and walnut oil. The developed method is rapid, no sample preparation needed, and is not involving the use of chemical reagents and solvents.     

Study of Fourier transform near infrared (FT‐NIR) spectra of ghee (anhydrous milk fat)

Ghee is chemically highly complex in nature. The authentication and characterisation of edible fats and oils by routine chemical methods are highly laborious and time‐consuming. Fourier transform near infrared (FT‐NIR) spectroscopy has emerged as the predominant analytical tool in the study of edible fats/oils. In order to assign absorption bands in the infrared (IR) spectrum, spectra of cow and buffalo ghee samples were acquired in the NIR region (10 000–4000 cm^?1). In the FT‐NIR spectrum, a total of nine peaks were obtained for cow and buffalo ghee, with almost equal intensity of absorption. The intensity of absorbance was higher for cow ghee compared to buffalo ghee.     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.     

Fourier transform infrared spectroscopy (FTIR) as a tool for discriminating <Emphasis Type="Italic">Salmonella typhimurium</Emphasis> contaminated beef

Detection of beef contamination from harmful pathogens will be helpful in protecting the consumer safety and controlling the outbreaks. In this paper, the potential of Fourier transform infrared spectroscopy (FTIR) was investigated to discriminate the Salmonella contaminated packed beef. A suitable headspace sampling system was designed and used to collect the headspace volatiles from the packed meat to the FTIR gas cell. Spectral signatures of headspace volatiles of meat packages were used to classify the packed meat samples as contaminated or not. FTIR spectrum was divided into several regions in order to reduce the dimensionality as well as to select the regions based on the absorbance properties of various volatiles present in headspace of meat package. Principal component analysis was performed on the entire spectrum (4000–500 cm⁻¹) as well as on the selected sub-regions of entire spectrum. Two statistical classification techniques (linear and quadratic discriminate analysis) were used to develop classification models. The statistical models were validated using bootstrap cross validation technique. The total average classification accuracies were evaluated in terms of coefficient of variance (% CV). Based on the mean of total average classification accuracies and its % CV calculated from five similarly conducted experiments, it was found that the statistical models developed on a part of the spectra (500–850 cm⁻¹) and full spectra (4000–500 cm⁻¹) can be used as potential classification models for non-destructive discrimination of Salmonella contaminated packed beef samples from uncontaminated ones. These results need to be further validated on dataset with larger sample size.     

The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts

Visible and near infrared reflectance (Vis-NIR, 350 to 1800 nm), and near infrared transmittance (NIT, 850 to 1050 nm) spectroscopy were used to predict beef quality traits of intact and ground meat samples. Calibration equations were developed from reference data (n = 312) of pH, color traits (L*, a*, and b*), ageing loss (%), cooking loss (%), and Warner–Bratzler shear force (WBSF, N) using partial least squares regressions. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R²_CV) and root mean square error of cross-validation. Quality traits were better predicted on intact than on ground samples, and the best results were obtained using Vis-NIR spectroscopy. Predictions were good (R²_CV = 0.62 to 0.73) for pH, L*, and a*, hardly sufficient (R²_CV = 0.34 to 0.60) for b*, cooking loss, and WBSF, and unsatisfactory for ageing loss (R²_CV = 0.15). Vis-NIR spectroscopy might be used to predict some physical beef quality traits on intact meat samples.     

Comparison of FTIR, FT-Raman, and NIR Spectroscopy in a Maple Syrup Adulteration Study

ABSTRACT: Maple syrup is prone to adulteration with cheaper sugars, such as corn syrup, due to its simplicity in chemical composition. The adulterated samples were characterized by Fourier Transform infrared (FTIR) spectroscopy in the region of 400 to 4000 cm_-1. Other techniques used for detection and in characterization of samples were the near infrared (NIR; 600 to 1700nm) and Fourier Transform-Raman (FT-Raman; 400 to 4000cm_-1) spectroscopy. Quantifying and classifying adulterants using chemometrics shows that all spectroscopic methods adopted were efficient, but FTIR and FT-Raman were superior to NIR in quantitative characterization of adulterants in maple syrup.     

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.      

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

Mid-infrared spectroscopy as a tool for rapid determination of internal quality parameters in tomato

The objective of this study was to investigate the possibility of predicting the quality parameters of tomato by mid-infrared spectroscopy. For 2 years, tomato samples, representing a large variability in the chemical composition, were scanned using the attenuated total reflectance accessory of a Fourier transform spectrometer in the wavenumber region between 4000 and 400 cm⁻¹_. Calibration models were developed using partial least squares (PLS) regression method and were tested with internal validation sample set in the first year. Different spectral preprocessing techniques were investigated and different spectral regions were selected to optimise the calibration models. In addition, the models obtained in 2007 were used to predict the soluble solids, dry matter and total acidity in tomato harvested in 2008.     

FTIR spectroscopic characterization of soy proteins obtained through AOT reverse micelles

Fourier transform infrared (FTIR) method was used to study the conformations of soy protein obtained through aqueous solution and bis(2-ethylhexyl) sodium sulfosuccinate (AOT) reverse micelle extraction. The results showed that there were changes in signal intensity and/or position of IR bands at 4000–400 cm⁻¹ when the soy proteins were separated by two extraction methods. The FTIR spectral changes were subsequently assessed using the second derivative spectroscopy in the amide I region (1700–1600 cm⁻¹). The contents of α-helix, β-sheet, turn and irregular conformations for soy proteins using aqueous solution extraction were 11.6%, 32.8%, 44.3% and 11.3%, respectively; while using AOT reverse micelle extraction 13.1%, 41.6%, 32.5% and 12.9%, respectively. The amount change of these structures might affect functional properties of soy proteins.     

FTIR-ATR Spectroscopy Based Metabolite Fingerprinting as A Direct Determination of Butter Adulterated With Lard

Adulteration of butter with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy was developed in order to determine the lard content in butter. The multivariate calibration of partial least square model for the prediction of adulterant was developed for quantitative measurement. The model yielded the highest regression with the correlation coefficient (R²) = 0.999, its lowest root mean square error estimation = 0.0947, and its root mean square error prediction = 0.0687, respectively. Cross validation testing evaluates the predictive power of the model. Partial least square model to be effective as their intercept of R²Y and Q²Y were 0.08 and –0.34, respectively.     

Rapid Differentiation of Listeria monocytogenes Epidemic Clones III and IV and Their Intact Compared with Heat‐Killed Populations Using Fourier Transform Infrared Spectroscopy and Chemometrics

The objectives of this study were to determine if Fourier transform infrared (FT‐IR) spectroscopy and multivariate statistical analysis (chemometrics) could be used to rapidly differentiate epidemic clones (ECs) of Listeria monocytogenes, as well as their intact compared with heat‐killed populations. FT‐IR spectra were collected from dried thin smears on infrared slides prepared from aliquots of 10 μL of each L. monocytogenes ECs (ECIII: J1‐101 and R2‐499; ECIV: J1‐129 and J1‐220), and also from intact and heat‐killed cell populations of each EC strain using 250 scans at a resolution of 4 cm^?1 in the mid‐infrared region in a reflectance mode. Chemometric analysis of spectra involved the application of the multivariate discriminant method for canonical variate analysis (CVA) and linear discriminant analysis (LDA). CVA of the spectra in the wavelength region 4000 to 600 cm^?1 separated the EC strains while LDA resulted in a 100% accurate classification of all spectra in the data set. Further, CVA separated intact and heat‐killed cells of each EC strain and there was 100% accuracy in the classification of all spectra when LDA was applied. FT‐IR spectral wavenumbers 1650 to 1390 cm^?1 were used to separate heat‐killed and intact populations of L. monocytogenes. The FT‐IR spectroscopy method allowed discrimination between strains that belong to the same EC. FT‐IR is a highly discriminatory and reproducible method that can be used for the rapid subtyping of L. monocytogenes, as well as for the detection of live compared with dead populations of the organism.     

Detection of clenbuterol in beef meat,liver and kidney by mid‐infrared spectroscopy (FT‐Mid IR) and multivariate analysis

Mid‐infrared spectroscopy (FT‐Mid IR) coupled with multivariate analysis was used to predict clenbuterol in beef meat, liver and kidney. A SIMCA model was also developed to discriminate between pure (beef meat, liver and kidney) and spiked with clenbuterol samples (beef meat‐clenbuterol, liver‐clenbuterol and kidney‐clenbuterol). The best models to predict clenbuterol concentrations were obtained using the partial least squares algorithm (PLS) with a R² > 0.9 and SEC and standard error of prediction <0.296 and 0.324, respectively. The SIMCA model used to discriminate pure and spiked with clenbuterol samples showed 100% correct classification rate. Methods detection limit was 2 μg kg^?1. FT‐Mid IR coupled with chemometrics could be a simple and rapid screening tool for monitoring clenbuterol in beef meat, liver and kidney implicated in food poisoning. This method could be use for screening purposes.     

Authentication of Extra Virgin Olive Oil from Sesame Oil Using FTIR Spectroscopy and Gas Chromatography

The presence of sesame oil in extra virgin olive oil has been investigated using Fourier transform infrared spectroscopy and gas chromatography. Frequencies of 1207–1018, 1517–1222, and 3050–2927 cm^?1 were chosen for quantification of sesame oil in extra virgin olive oil. Using Fourier transform infrared normal spectra coupled with a partial least square model, the root mean standard error of calibration and root mean standard error of prediction obtained were relatively low, i.e., 0.331 and 1.01% (vol/vol), respectively. Using fatty acid profiles as determined by gas chromatography, the levels of palmitic and oleic acids were decreased linearly with R² of 0.969 and 0.934, meanwhile the levels of stearic and linoleic acids were increased with R² of 0.930 and 0.959, respectively, with the increasing levels of sesame oil. From level 10% sesame oil (vol/vol), all these fatty acids are significantly different (p < 0.05).     

Use of Fourier Transform Infrared (FTIR) Spectroscopy and Chemometrics for Analysis of Lard Adulteration in “Rambak” Crackers

“Rambak” crackers are one of the traditional foods consumed among Indonesian people made from various kinds of animal skin. The present study highlights the analysis of lard obtained from extraction of “rambak” crackers using Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of partial least square and principle component analysis. FTIR spectroscopy at wavenumber regions of 1200–1000 cm^–1 was successfully used for quantification and classification of lard in “rambak” crackers. The relationship between actual value of lard and Fourier transform infrared predicted value has R² value of 0.946 with low errors in calibration and validation models. Furthermore, the chemometrics principle component analysis can be successfully used for determination of pig skin through analysis of lard in commercial “rambak” crackers. The developed method (FTIR spectroscopy coupled with chemometrics) is rapid and reliable for quantification and classification of lard in “rambak” crackers.     

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.     

Evaluation of Fatty Acid Profile of Wagyu Beef by ATR-FTIR Spectroscopy

Japanese black Wagyu beef has its characteristics of fatty well-marbled texture, flavor, and tenderness which are affected by fatty acid composition. The aim of this study was to develop an analytical method for evaluating the fatty acid profile of Wagyu beef by Fourier transform infrared (FTIR) spectroscopy. In the current study, attenuated total reflection–FTIR (ATR-FTIR) spectroscopy and gas chromatography (GC) were applied to the fat tissues, and the solvent-extracted fats which were sampled from subcutaneous, inter- and intramuscular fat tissues. Results of GC analysis showed that monounsaturated fatty acids (MUFA) content became larger in the order of intramuscular, intermuscular, and subcutaneous fats, and saturated fatty acids (SFA) became smaller in the same order. Subcutaneous fat could be discriminated from inter- and intramuscular fats on the basis of fatty acid composition by principal component analysis. The ATR-FTIR analysis revealed that the shift of the peak positions of alkene C–H stretching vibration at around 3,006 cm⁻¹ occurred depending on the unsaturation degree of fatty acids in beef fat. Partial least squares (PLS) regression analysis with leave-one-out cross-validation was applied to the combined regions of 2,800–3,050 and 1,000–1,500 cm⁻¹ for the fat tissues and the extracted fats. The correlation coefficients of the PLS validation models predicting the content of the MUFA and SFA for solvent-extracted fats were higher than those for fat tissues, and the coefficients (R ²) of determination more than 0.873 were obtained for solvent-extracted fats and 0.522 for fat tissues.     

Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques

Food adulteration is a profit‐making business for some unscrupulous manufacturers. Maple syrup is a soft target of adulterators owing to its simplicity of chemical composition. In this study the use of Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy to detect adulterants such as cane and beet invert syrups as well as cane and beet sugar solutions in maple syrup was investigated. The FTIR spectrum of adulterated samples was characterised and the regions 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The region between 1100 and 1660 nm in the NIR spectrum was used for analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for discriminant analysis, while partial least squares (PLS) and principal component regression (PCR) were used for quantitative analysis. FTIR was more accurate in predicting adulteration using two different regions (R² > 0.93 and >0.98) compared with NIR (R² > 0.93). Classification and quantification of adulterants in maple syrup show that NIR and FTIR can be used for detecting adulterants such as pure beet and cane sugar solutions, but FTIR was superior to NIR in detecting invert syrups. © 2003 Society of Chemical Industry