Rapid evaluation and discrimination of γ‐irradiated carbohydrates using FT‐Raman spectroscopy and canonical discriminant analysis

Chemical changes induced by gamma irradiation of selected sugar systems—honey and fructose—were investigated through their molecular fingerprint using Fourier transform Raman spectroscopy (FT‐Raman). Generalized two‐dimensional (2‐D) correlation spectroscopy was applied to FT‐Raman spectra of the control and 17 kGy irradiated fructose to elucidate changes in the chemical structure upon irradiation. The irradiation induced changes in the ring (below 700 cm^?1) and conformational structure (800–1500 cm^?1) of fructose were identified by means of a 2‐D FT‐Raman correlation spectroscopy. The irradiation damage depicted from the C? H stretch region (2800–3000 cm^?1) of the FT‐Raman spectra of honey was used to develop a pattern recognition model for classifying honey based on the irradiation dose. A hybrid partial least squares (PLS)–canonical variate analysis (CVA) with the optimum number of factors from PLS was used for rapid discrimination of honeys irradiated at 1, 5, 10 or 17 kGy. The present study demonstrated that FT‐Raman spectroscopy, together with chemometrics, could be a rapid tool for classification of foodstuffs with high sugar content and provides a viable option to explore radiation‐induced modifications to sugar systems subjected to irradiation processing. Copyright © 2007 Society of Chemical Industry     

Short communication: Sensitive detection of norbixin in dried dairy ingredients at concentrations of less than 1 part per billion

Norbixin is the water-soluble carotenoid in annatto extracts used in the cheese industry to color Cheddar cheese. The purpose of norbixin is to provide cheese color, but norbixin is also present in the whey stream and contaminates dried dairy ingredients. Regulatory restrictions dictate that norbixin cannot be present in dairy ingredients destined for infant formula or ingredients entering different international markets. Thus, there is a need for the detection and quantification of norbixin at very low levels in dried dairy ingredients to confirm its absence. A rapid method for norbixin evaluation exists, but it does not have the sensitivity required to confirm norbixin absence at very low levels in compliance with existing regulations. The current method has a limit of detection of 2.7 μg/kg and a limit of quantification of 3.5 μg/kg. The purpose of this study was to develop a method to extract and concentrate norbixin for quantification in dried dairy ingredients below 1 μg/kg (1 ppb). A reverse-phase solid-phase extraction column step was applied in the new method to concentrate and quantify norbixin from liquid and dried WPC80 (whey protein concentrate with 80% protein), WPC34 (WPC, 34% protein), permeate, and lactose. Samples were evaluated by both methods for comparison. The established method was able to quantify norbixin in whey proteins and permeates (9.39 μg/kg to 2.35 mg/kg) but was unable to detect norbixin in suspect powdered lactose samples. The newly developed method had similar performance to the established method for whey proteins and permeates but was also able to detect norbixin in powdered lactose samples. The proposed method had a >90% recovery in lactose samples and a limit of detection of 28 ppt (ng/kg) and a limit of quantification of 94 ppt (ng/kg). The developed method provides detection and quantification of norbixin for dairy ingredients that have a concentration of <1 ppb.     

The Distribution of Fat in Dried Dairy Particles Determines Flavor Release and Flavor Stability

Dried dairy ingredients are utilized in various food and beverage applications for their nutritional, functional, and sensory properties. Dried dairy ingredients include milk powders of varying fat content and heat treatment and buttermilk powder, along with both milk and whey proteins of varying protein contents. The flavor of these ingredients is the most important characteristic that determines consumer acceptance of the ingredient applications. Lipid oxidation is the main mechanism for off‐flavor development in dried dairy ingredients. The effects of various unit operations on the flavor of dried dairy ingredients have been investigated. Recent research documented that increased surface free fat in spray dried WPC80 was associated with increased lipid oxidation and off‐flavors. Surface free fat in spray‐dried products is fat on the surface of the powder that is not emulsified. The most common emulsifiers present in dried dairy ingredients are proteins and phospholipids. Currently, only an association between surface free fat and lipid oxidation has been presented. The link between surface free fat in dried dairy ingredients and flavor and flavor stability has not been investigated. In this review, some hypotheses for the role of surface free fat on the flavor of dried dairy ingredients are presented along with proposed mechanisms.     

Influence of particle size on the physicochemical properties and stickiness of dairy powders

The compositional and physicochemical properties of different whey permeate (WPP), demineralised whey (DWP) and skim milk powder (SMP) size fractions were investigated. Bulk composition of WPP and DWP was significantly (P < 0.05) influenced by powder particle size; smaller particles had higher protein and lower lactose contents. Microscopic observations showed that WPP and DWP contained both larger lactose crystals and smaller amorphous particles. Bulk composition of SMP did not vary with particle size. Surface composition of the smallest SMP fraction (<75 μm) showed significantly lower protein (−9%) and higher fat (+5%) coverage compared with non-fractionated powders. For all powders, smaller particles were more susceptible to sticking. Hygroscopicity of SMP was not affected by particle size; hygroscopicity of semi-crystalline powders was inversely related to particle size. This study provides insights into differences between size fractions of dairy powders, which can potentially impact the sticking/caking behaviour of fine particles during processing.     

Classification and characterisation of Spanish red wines according to their appellation of origin based on chromatographic profiles and chemometric data analysis

Chromatographic profiles of wines have been used as a fingerprint for the discrimination of Spanish wines based on oenological practices. In order to extract information of different families of phenolic compounds, profiles of different UV-vis absorption wavelengths (280, 310, 370 and 520nm) and fluorescence (ex=260nm; em=360nm) were analysed. A total of thirteen phenolic compounds which allowed the discrimination of wines of three different Spanish appellations (Penedes, Rioja and Ribera del Duero) were selected by means of principal component analysis (PCA). Afterwards, these compounds were used to build partial least squares discriminant analysis (PLS1-DA and PLS2-DA) models which allowed the discrimination of wines according to their appellation with classification rates for independent test sets higher than 96% and 93% for PLS1-DA and PLS2-DA models respectively. Finally, characteristic compounds of each appellation were tentatively identified by means of liquid chromatography-mass spectrometry (LC-MS) analysis. Thus, ten out of thirteen compounds (i.e., gallic acid for Penedes, trans-coumaroyltartaric and trans-caffeoyltartaric acids for Rioja and myricetin for Ribera del Duero wines) have been proposed.     

Use of Fourier transform-infrared spectroscopy to predict spoilage bacteria on aerobically stored chicken breast fillets

In this research work we explored the potential of mid infrared (MIR) spectroscopy to determine spoilage microorganism on the surface of chicken breast fillets that were kept aerobically at 5 °C for 0, 1, 2, 3, 5 and 8 days, and at 15 °C for 0, 0.5, 1, 2, 3 and 5 days. It is shown that MIR spectroscopy (4000 – 1000 cm⁻¹ range) coupled with attenuated total reflectance (ATR) accessory can be used directly on the surface of meat samples to produce fingerprints. Culture dependent methods were used to determine total viable count (TVC), Pseudomonas, Enterobacteriaceae and Brochothrix thermosphacta on chicken breast fillets at each step of the 2 kinetics. In parallel, MIR spectra were recorded. For each kinetic, partial least square discriminant analysis (PLSDA) results showed 100% of good classifications for the six investigated storage times using 4 PLS factors. PLS regression was carried out to predict the microbial counts from the MIR spectral data. Using PLS model with four factors, good correlation (R² = 0.99) and very small root mean squares error of validation (between 0.01 and 0.97 log cfu/cm²) showed a strong correlation between MIR spectral data sets and the results obtained using traditional methods.     

Feasibility study of discriminating the manufacturing process and sampling zone in ripened soft cheeses using attenuated total reflectance MIR and fiber optic diffuse reflectance VIS–NIR spectroscopy

The use of visible–near infrared (VIS–NIR) and mid infrared (MIR) spectroscopies for rapid characterisation of 15 traditional and stabilised retail soft cheeses, manufactured with different cheese making procedures was described. A fiber-type, VIS–NIR spectrophotometer (Zeiss Corona 45 VIS–NIR) in a measurement range of 315–1700 nm and a Fourier transform spectrometer (IFS 66V/S, Bruker, Belgium) in a measurement range between 3000 and 900 cm⁻¹ were used to scan spectra in reflectance mode at the external (E) and central (C) zones of the investigated cheeses. The principal component analysis (PCA) applied to the normalised spectral data set (VIS–NIR and MIR) did not provide a good discrimination of cheeses. Therefore, the factorial discriminant analysis (FDA) was applied separately to the first 5 principal components (PCs) of the PCA performed on the VIS–NIR and MIR data sets. Regarding the MIR spectra, the percentage of samples correctly classified into six groups (three for the E and three for the C zones) by the FDA was 64.8% and 33.3% for the calibration and validation samples, respectively. Better classification was obtained from the VIS–NIR spectra since the percentage of samples correctly classified was 85.2% and 63.2% for the calibration and validation samples, respectively. Finally, a concatenation technique was applied on the first 5 PCs of the PCA performed on the VIS–NIR and MIR data sets. This technique allowed a quite satisfactory classification of the investigated cheeses according to their manufacturing process and their sampling zone. In this case, correct classifications (CC) of 90.7% and 80.6% were obtained for the calibration and the validation samples, respectively.     

Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics

Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken breast muscle packed under aerobic conditions and stored at 4° for 14 days were collected and investigated for their potential use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between days 0 and 4 samples (total viable count (TVC) days 0 and 4 = 5.23 and 6.75 log₁₀ cfu g⁻¹) which had bacterial loads smaller than the accepted levels (8 log₁₀ cfu g⁻¹) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14 = 9.61 and 10.37 log₁₀ cfu g⁻¹). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become useful, rapid, non-destructive tools for spoilage detection.     

拉曼光谱法快速检测猪肉脯中的掺伪鸡肉

目的 建立拉曼光谱法快速、准确、无损地检测猪肉脯样品中掺假鸡肉的方法。方法 制备33份猪肉中掺入不同比例鸡肉的肉脯样品,采集拉曼光谱数据,分别采用标准正态变换、多元散射校正、卷积平滑、归一化、一阶导数等5种不同预处理方法,对原始光谱数据进行预处理,采用连续投影算法、竞争性自适应重加权算法及随机蛙跳算法对光谱数据进行特征波长筛选,建立偏最小二乘法(partial least squares,PLS)模型对猪肉脯进行定性定量判别。结果 拉曼光谱数据经过多元散射校正处理的效果最佳,竞争性自适应重加权算法竞筛选效果更佳,构建猪肉脯中猪肉含量的PLS定量模型,其预测集决定系数和预测均方根误差分别为0.9762、7.2998。建立的PLS判别模型的校正集和预测集总判别正确率分别为100.00%和98.33%。结论 拉曼光谱分析技术可有效用于定性鉴别猪肉脯是否掺伪及定量分析猪肉肉脯中掺入鸡肉的比例,为肉脯掺假的快速无破坏性检测的应用提供支持。     

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.     

Properties of Mixed and Filled-type Dairy Gels

Skim milk powder (SMP), a whey protein concentrate (WPC) and fat globules with modified membranes were used for the fabrication of mixed and filled dairy gels. Firmness and syneresis of pure gels varied broadly with pH and total solids (TS). Mixing SMP and WPC at 10% TS showed synergistic effects on gel firmness, particularly for 50:50 blends, and addition of fat provided further reinforcement. Syneresis of gels and their appearance changed from high and clear to low and turbid, respectively, as the WPC content was increased. Microstructural studies revealed the porous structure of the parent gels, compatability between both protein sources and between fat globules and the protein matrix.     

Prediction of fatty acid profiles in cow,ewe, and goat milk by mid-infrared spectrometry

Mid-infrared (MIR) spectrometry was used to estimate the fatty acid (FA) composition in cow, ewe, and goat milk. The objectives were to compare different statistical approaches with wavelength selection to predict the milk FA composition from MIR spectra, and to develop equations for FA in cow, goat, and ewe milk. In total, a set of 349 cow milk samples, 200 ewe milk samples, and 332 goat milk samples were both analyzed by MIR and by gas chromatography, the reference method. A broad FA variability was ensured by using milk from different breeds and feeding systems. The methods studied were partial least squares regression (PLS), first-derivative pretreatment + PLS, genetic algorithm + PLS, wavelets + PLS, least absolute shrinkage and selection operator method (LASSO), and elastic net. The best results were obtained with PLS, genetic algorithm + PLS and first derivative + PLS. The residual standard deviation and the coefficient of determination in external validation were used to characterize the equations and to retain the best for each FA in each species. In all cases, the predictions were of better quality for FA found at medium to high concentrations (i.e., for saturated FA and some monounsaturated FA with a coefficient of determination in external validation >0.90). The conversion of the FA expressed in grams per 100 mL of milk to grams per 100 g of FA was possible with a small loss of accuracy for some FA.     

Effects of spray drying conditions and the addition of surfactants on the foaming properties of a whey protein concentrate

Whey is the main waste by-product from dairy industry and at the same time is the major source of globular proteins. These proteins are concentrated mainly through spray drying, but high temperatures affect the foaming properties of globular protein. The addition of surfactants can have a protective role against thermal effects. The aim of this work was to optimize the spray-drying condition and surfactant concentration to obtain a whey protein concentrate (WPC) to be used in hot beverages according to the industry criteria for foaming stability. Three temperatures and three surfactant concentrations were applied, and the optimization was conducted using response surface analysis. Sensory analysis was applied to the WPC obtained under optimal conditions. The results showed that the foaming stability according to industrial criteria was attained when the spray drying was performed at 210 °C with surfactant concentration of 1.50 g/100 g. This resulted in foaming capacity of 3.80 mL, moisture content of 1.82 g/100 g and apparent density of 0.181 g/cm³. The sensory analysis suggested that aroma was related to dairy, cooked and whey and taste was related to sweet and dairy notes. In conclusion, temperature and surfactant concentration played an important role in the foaming capacity and stability of WPC.     

Texturized Dairy Proteins

ABSTRACT: Dairy proteins are amenable to structural modifications induced by high temperature, shear, and moisture; in particular, whey proteins can change conformation to new unfolded states. The change in protein state is a basis for creating new foods. The dairy products, nonfat dried milk (NDM), whey protein concentrate (WPC), and whey protein isolate (WPI) were modified using a twin-screw extruder at melt temperatures of 50, 75, and 100 °C, and moistures ranging from 20 to 70 wt%. Viscoelasticity and solubility measurements showed that extrusion temperature was a more significant (P < 0.05) change factor than moisture content. The degree of texturization, or change in protein state, was characterized by solubility (R²= 0.98). The consistency of the extruded dairy protein ranged from rigid (2500 N) to soft (2.7 N). Extruding at or above 75 °C resulted in increased peak force for WPC (138 to 2500 N) and WPI (2.7 to 147.1 N). NDM was marginally texturized; the presence of lactose interfered with its texturization. WPI products extruded at 50 °C were not texturized; their solubility values ranged from 71.8% to 92.6%. A wide possibility exists for creating new foods with texturized dairy proteins due to the extensive range of states achievable. Dairy proteins can be used to boost the protein content in puffed snacks made from corn meal, but unmodified, they bind water and form doughy pastes with starch. To minimize the water binding property of dairy proteins, WPI, or WPC, or NDM were modified by extrusion processing. Extrusion temperature conditions were adjusted to 50, 75, or 100 °C, sufficient to change the structure of the dairy proteins, but not destroy them. Extrusion modified the structures of these dairy proteins for ease of use in starchy foods to boost nutrient levels. Practical Application: Dairy proteins can be used to boost the protein content in puffed snacks made from corn meal, but unmodified, they bind water and form doughy pastes with starch. To minimize the water binding property of dairy proteins, whey protein isolate, whey protein concentrate, or nonfat dried milk were modified by extrusion processing. Extrusion temperature conditions were adjusted to 50, 75, or 100 °C, sufficient to change the structure of the dairy proteins, but not destroy them. Extrusion modified the structures of these dairy proteins for ease of use in starchy foods to boost nutrient levels.     

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 for adulterators owing to its simplicity of chemical composition. The use of infrared spectroscopic techniques such as Fourier transform infrared (FTIR) and near‐infrared (NIR) as a tool 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 spectra of adulterated samples were characterised and the regions of 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The NIR spectral region between 1100 and 1660 nm 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 the 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 both 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. © 2002 Society of Chemical Industry     

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     

Influence of storage, heat treatment, and solids composition on the bleaching of whey with hydrogen peroxide

The residual annatto colorant in liquid whey is bleached to provide a desired neutral color in dried whey ingredients. This study evaluated the influence of starter culture, whey solids and composition, and spray drying on bleaching efficacy. Cheddar cheese whey with annatto was manufactured with starter culture or by addition of lactic acid and rennet. Pasteurized fat-separated whey was ultrafiltered (retentate) and spray dried to 34% whey protein concentrate (WPC34). Aliquots were bleached at 60 °C for 1 h (hydrogen peroxide, 250 ppm), before pasteurization, after pasteurization, after storage at 3 °C and after freezing at -20 °C. Aliquots of retentate were bleached analogously immediately and after storage at 3 or -20 °C. Freshly spray dried WPC34 was rehydrated to 9% (w/w) solids and bleached. In a final experiment, pasteurized fat-separated whey was ultrafiltered and spray dried to WPC34 and WPC80. The WPC34 and WPC80 retentates were diluted to 7 or 9% solids (w/w) and bleached at 50 °C for 1 h. Freshly spray-dried WPC34 and WPC80 were rehydrated to 9 or 12% solids and bleached. Bleaching efficacy was measured by extraction and quantification of norbixin. Each experiment was replicated 3 times. Starter culture, fat separation, or pasteurization did not impact bleaching efficacy (P > 0.05) while cold or frozen storage decreased bleaching efficacy (P < 0.05). Bleaching efficacy of 80% (w/w) protein liquid retentate was higher than liquid whey or 34% (w/w) protein liquid retentate (P < 0.05). Processing steps, particularly holding times and solids composition, influence bleaching efficacy of whey. PRACTICAL APPLICATION: Optimization of whey bleaching conditions is important to reduce the negative effects of bleaching on the flavor of dried whey ingredients. This study established that liquid storage and whey composition are critical processing points that influence bleaching efficacy.     

Development of chemometric models using Vis-NIR and Raman spectral data fusion for assessment of infant formula storage temperature and time

This study evaluated the potential of Vis-NIR and Raman spectral data fusion combined with PLS and SVM chemometric models developed using a large dataset (n = 1700) of commercial infant formula (IF) samples to (i) discriminate between different IF storage temperature (20, 37 °C) and (ii) predict IF storage time (0–12 months). Three interval-based PLS variable selection methods (forward interval PLS (FiPLS), backward interval PLS (BiPLS) and synergy interval PLS (SiPLS)) and SVM-recursive feature elimination (SVM-RFE) methods were compared for model development. The best IF storage temperature discrimination model was developed using SVM classification (SVMC) and Vis-NIR spectra (400–2498 nm) (Accuracy_CV = 99.82%, Accuracy_P = 100%). SVM regression (SVMR) models developed using medium level data fusion (features selected by SVM-RFE) had the lowest root mean square error (RMSE) values for IF samples stored at either temperature, 20 °C or 37 °C (RMSE_CV = 0.7–0.8, RMSE_P = 0.6–0.9).Industrial relevanceSpectroscopic technologies, including Vis-NIR and Raman spectroscopy have been widely applied for process analysis and increasingly for on-line process monitoring in areas of chemicals, food processing, agriculture and pharmaceuticals, etc. Due to their rapid measurement and minimal or no sample preparation, they are highly suitable for in-line process monitoring. This study demonstrates that Vis-NIR and Raman process analytical tools either individually or combined may be employed for quality assessment and process control of IF manufacture.     

Quantitation of Lamb Content in Mixtures with Raw Minced Beef Using Visible, Near and Mid-Infrared Spectroscopy

Samples of beef (32), lamb (33) and 5%, 10% and 20% (w/w) lamb-in-beef mixtures (33 each) were minced and reflectance scanned in the visible, near and mid-infrared spectral regions. Partial least squares (PLS) regression models were developed to predict percentage lamb content using each spectral region alone and combinations of all three. The most accurate models combined mid-IR (800-2000 cm^-1) and near IR (1100-2498 nm) spectral data following 2^nd derivatization; standard errors of prediction of 0.91% (0–20% range in lamb content) and 4.1% (0–100% range in lamb) were obtained. This technique may be useful for screening such meat mixtures.     

Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000–2800, 1700–1500 and 1500–900 cm⁻¹), the classification rate was not satisfactory. Therefore, the first principal component (PCs) scores (corresponding to 3, 10 and 10 for, respectively, the 3000–2800, 1700–1500 and 1500–900 cm⁻¹) of the principal component analysis (PCA) extracted from each of the data sets were pooled (concatenated) into a single matrix and analysed by FDA. Correct classification amounting to 71.7% was obtained. Finally, the same procedure was applied to the MIR and fluorescence data sets and 98% of milk samples were found to be correctly classified. Milk samples belonging to control and soybean groups were 100% correctly classified. Regarding milk samples originating from the scotch bean group, only 2 out of 33 samples were misclassified. It was concluded that concatenation of the data sets collected from the two spectroscopic techniques is an efficient tool for authenticating milk samples according to their feeding systems, regardless of the lactation stage.