Multivariate near-infrared and Raman spectroscopic quantifications of the crystallinity of lactose in whey permeate powder

The use of near-infrared (NIR) and Fourier transform Raman spectroscopy for quantification of crystalline lactose content in whey permeate powder was investigated using chemometric methods. Sample sets consisting of binary mixtures of crystalline (50.0–98.0%) and amorphous lactose and process whey permeate samples with different amounts of crystalline lactose (75.0–95.5%) added were analyzed. The best results for quantification of crystallinity were obtained by partial least squares (PLS) regression on NIR data in five selected intervals in the range 1100–2498 nm. Data analysis on the total sample set of 35 samples yielded a prediction error (root mean square error of cross validation) of 0.627%. The corresponding result for Raman spectroscopy in the range 3500–100 cm⁻¹ was 1.62%. Interval-PLS regression was used for the selection of relevant spectral intervals as well as for improving the spectral interpretation. Alternating regression was used to show that the amorphous lactose preparation contained only a negligible amount of crystalline lactose.     

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n = 121) and Riesling (n = 91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classification of juice samples of different varieties.     

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.     

Feature Selection and Recognition from Nonspecific Volatile Profiles for Discrimination of Apple Juices According to Variety and Geographical Origin

Abstract: Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid‐phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography‐mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. Practical Application: This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS‐SPME‐GC‐MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product.     

Nondestructive determination of soluble solids content and pH in tomato juice using NIR transmittance spectroscopy

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The r _c, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices.     

Determination of Calcium Content in Powdered Milk Using Near and Mid-Infrared Spectroscopy with Variable Selection and Chemometrics

Near infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were evaluated to determine calcium content in powdered milk. A hybrid spectral variable selection algorithm combined with uninformation variable elimination (UVE) and successive projections algorithm (SPA) selected 11 NIR and 15 MIR variables from full 2,756 NIR and 3,727 MIR variables, respectively. Predicted results of least-squares support vector machine models for the samples in the prediction set show that the 15 MIR variables obtained much better results (0.930 for coefficient of determination (r ²), 3.703 for residual predictive deviation (RPD), 30.162 for root mean square error of prediction set (RMSEP) and 5.22% for relative errors of prediction (RSEP)) than 11 NIR variables did (0.636 for r ², 1.587 for RPD, 78.815 for RMSEP, and 13.40% for RSEP). The overall results indicate that MIR spectroscopy could be applied as a precision and rapid method to determine calcium content in powdered milk. The good performance shows a potential application using UVE-SPA to select NIR and MIR effective variables.     

Thermal resistance of Alicyclobacillus acidoterrestris spores in different heating media

The aim of this work was to study the influence of temperature (85, 90, 95 and 100 °C), total soluble solids (SS: 10 and 20°Brix or % by weight of sucrose) and pH (3.5 and 4.0) on decimal reduction time ( D- value) of the Alicyclobacillus acidoterrestris strain DSM2498 spores in apple juice, orange juice and malt extract broth (MEB). The effects of SS and pH on D -values and z- values in each media were insignificant ( P  > 0.05). In apple juice, orange juice and MEB, z- values of A. acidoterrestris for pH 3.5 and pH 4.0 were 12.2 ± 1.3–14.2 ± 3.2 °C, 11.2 ± 0.3–9.4 ± 0.0 °C and 11.9 ± 0.8–10.3 ± 0.4 °C, respectively. z- values of apple juice, orange juice and MEB samples with SS = 10°Brix and SS = 20°Brix were 14.1 ± 3.2–12.2 ± 1.3 °C, 10.2 ± 0.7–10.5 ± 1.1 °C and 11.3 ± 1.5–10.9 ± 0.2 °C, respectively. However, D -values of all samples were affected by temperature significantly ( P  < 0.01). Average D -values of apple juice, orange juice and MEB were 101.2 ± 14.7, 34.4 ± 7.9, 20.3 ± 4.9 and 4.3 ± 1.3 min for 85, 90, 95 and 100 °C. This study demonstrated that A. acidoterrestris spores exhibited high resistance to thermal processing applications. pH and SS of the media did not affect thermal resistance.     

Variety‐based discrimination of apple juices by an electronic nose and gas chromatography–mass spectrometry

The variety of raw material plays a crucial role in the quality and authenticity of fruit juices and juice products. To characterise and classify apple juices according to variety on the basis of their volatile compounds, electronic nose (EN) and gas chromatography–mass spectrometry (GC‐MS) were applied to detect the apple juices prepared by eight different varieties. The EN was used to analyse the mixture of volatile compounds as a whole and enabled rapid classification of juice samples when coupled with linear discriminant analysis (LDA). LDA showed a perfect discrimination of apple juices based on varieties. GC‐MS was utilised to illustrate the differences of volatile compounds among juice samples. Identification of volatile compositions and their contents provides useful access to differentiate juices from different varieties.     

Optimization of processing parameters for cloudy ginkgo (Ginkgo biloba Linn.) juice

Response Surface Methodology (RSM) was employed to optimize the hydrolysis conditions of ginkgo cloudy juice using enzymatic hydrolysis. The independent variables were hydrolysis time (20–100 min), -Amylase dosage (2.60–8.18 U/g kernel) and Alcalase protease dosage (0.87–8.94 U/g kernel). The coefficient of determination, R² values for suspension stability, browning indexes and juice yield were greater than 0.900. The combined effect of these variables on suspension stability, browning indexes and juice yield were investigated. The results showed suspension stability and juice yield varied curvilinearly with increase of -Amylase dosage, Alcalase dosage and hydrolysis time and browning indexes increased linearly. Based on response surface and contour plots, the optimum conditions for the enzymatic treatment for ginkgo cloudy juice were: hydrolysis time 73 min, -amylase dosage 7.39 U/g kernel and Alcalase dosage 6.43 U/g kernel.     

Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices

Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R² value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R² value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.     

近红外光谱法快速检测梨汁中柠檬酸和L-苹果酸含量

利用近红外光谱技术对梨汁中柠檬酸和L-苹果酸含量进行快速定量检测,并通过6种光谱预处理及3种数学校正方法对柠檬酸和L-苹果酸含量预测模型进行优化。本研究采用近红外光谱仪在1 100~1 650 nm光谱范围内共采集70个梨汁样本的近红外光谱图,并利用高效液相色谱法测定柠檬酸和L-苹果酸的含量。首先对样本原始光谱图经过多元散射校正(MSC)、一阶导数、二阶导数等方法进行预处理,然后通过主成分分析(PCR)、修正的最小二乘法(MPLS)及偏最小二乘法(PLS)等分别建立梨汁中柠檬酸和L-苹果酸含量的预测模型,并对该模型进行优化。优化后,柠檬酸的最佳模型为PLS结合MSC;L-苹果酸的最佳模型为MPLS结合SNV且去散射;最佳预测模型的预测相关系数最高可达0.985,预测标准差最低为0.039%,相对分析误差最大为3.46。结果表明,近红外光谱可作为一种可靠、快速、无损、简单的方法用于梨汁中柠檬酸和L-苹果酸含量的测定。该方法为生产企业及监管部门开展梨汁的掺伪鉴别提供了依据。     

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.     

Feasibility of Discriminating Dried Dairy Ingredients and Preheat Treatments Using Mid-Infrared and Raman Spectroscopy

This study investigated the feasibility of mid-infrared (MIR) and Raman spectroscopy for (i) discrimination of three dried dairy ingredients, namely skim milk powder (SMP), whey protein concentrate (WPC) and demineralised whey protein (DWP) powder, and (ii) discrimination of preheat treatments of dried dairy ingredients using partial least squares discriminant analysis (PLS-DA). PLS1-DA models developed using MIR ranges of 800–1800 and 1200–1800 cm^?1 yielded the best discrimination (correct identification of 97.2% for SMP discrimination and 100% for WPC and DWP discrimination). The best PLS2-DA model using MIR spectroscopy was developed over the spectral range of 800–1800 cm^?1 and produced correct identification of 100% for dairy ingredient discrimination. Models developed using Raman 800–1800 and 1200–1800 cm^?1 spectral ranges correctly discriminated (100% correctly identified) each dairy ingredient. Although all PLS1-DA and PLS2-DA models developed using both spectral technologies for preheat treatment discrimination had good discrimination accuracy (86–100%), they employed a high number of factors (8–9 for the best model). The use of the Martens uncertainty test successfully reduced the number of factors employed (3–4 for the best models) and improved the performance of PLS1-DA models for preheat treatment discrimination (all 100% correctly identified). This feasibility study demonstrates the potential of both MIR and Raman spectroscopy for rapid characterisation of dried dairy ingredients.     

A comparison and joint use of NIR and MIR spectroscopic methods for the determination of some parameters in European Emmental cheese

The determination of winter cheese chemical properties, namely, fat, sodium chloride (NaCl), pH, non protein nitrogen (NPN), total nitrogen (TN) and water soluble nitrogen (WSN) was done using spectroscopic technologies with different wavelength zones. The Emmental cheeses provided from different European countries were studied. A total of 91 cheeses produced during the winter time in Austria (n=4), Finland (n=6), Germany (n=13), France (n=30) and Switzerland (n=38) were analysed by near infrared (NIR) and mid infrared (MIR) spectroscopies. The combination of these two spectral regions (sum of their spectra) was also studied. The partial least square (PLS) regression with the leave one-out cross validation technique was used to build up calibration models using data set designated as calibration set. These models were validated with another data set designated as validation set. The obtained results suggest the use of the NIR for the determination of fat and TN contents, and the MIR for NaCl and NPN contents as well as for the pH. Similar results were obtained for WSN using the two techniques together. The combined spectra of both NIR and MIR did improve the results, while providing comparable results to those obtained from either the NIR or MIR spectroscopy.     

苹果品种及损伤苹果的FT-NIR鉴别研究

用傅里叶近红外光谱技术(FT-NIR)对不同品种的苹果以及损伤嘎啦和完好嘎啦进行快速、无损检测,比较不同判别方法对所建立的区分苹果品种及苹果损伤模型的影响。结果表明:损伤嘎啦和完好嘎啦的近红外图谱经小波分析预处理后,用12000～4000cm-1波数范围的前5个主成分分别结合多层感知神经网络、径向基神经网络、Fisher判别3种方法所建立的判别模型对未知样本的正确判别率分别为97.8%、87.2%和84.8%,基于权重法用多元线性回归(MLR)所选择的特征波长所建立的Fisher判别模型对未知样本的正确判别率为89.1%;用偏最小二乘判别(PLS-DA)所建立的判别模型对未知样本的正确判别率为100%,由于PLS-DA模型对训练集和验证集的正确判别率均为100%,因此PLS-DA模型优于其他模型。不同品种苹果的光谱经平滑预处理后,用全波数范围12000～4000cm-1的前6个主成分所建立的判别模型优于经验波数范围8000～4500cm-1所建立的判别模型,其较优模型对建模集和验证集的正确判别率分别为90.9%和92.1%。近红外光谱技术结合化学计量学可以快速、无损鉴别苹果是否有损伤以及不同品种的苹果。     

Application of Portable and Handheld Infrared Spectrometers for Determination of Sucrose Levels in Infant Cereals

Sucrose coating of breakfast cereals is used to enhance the flavor and attractiveness of the final product but there is a need for monitoring its levels to meet consumer health concerns associated with sugar consumption. Our objective was to evaluate the use of portable (mid-infrared, MIR) and handheld (near-infrared, NIR) systems for rapid, simple and reliable determination of sucrose content in breakfast cereal products. Cereal-based and sucrose-coated samples were provided by an Ohio snack food company. Samples were ground and spectra were collected using portable ATR-MIR (Cary 630) and handheld NIR (microPHAZIR) spectrometers. Reference sucrose levels were determined by high-performance liquid chromatography (HPLC). Partial least squares regression (PLSR) was used to develop calibration regression models for prediction of sucrose levels in breakfast cereals based on spectral data. Sucrose levels in uncoated (n?=?28) and coated (n?=?62) cereal samples were on average of 1.2?±?0.7 and 11.8?±?3.5 g/100 g, respectively. Similar calibration (n?=?85) model performances were obtained for determination of sucrose content by using the portable MIR and handheld NIR instruments with standard error of cross-validation (SECV) of 1.45 %. However, superior predictive ability was obtained with the portable MIR unit using a validation set (n?=?20, SEP?=?1.27 % and RPD?=?4.41). Regression models using NIR spectrum of the cereal through a polyethylene bag resulted in reduction of the model goodness of fit and RPD values. Results support the application of handheld NIR and portable MIR spectrometers for close-to-real-time analysis of sucrose levels in breakfast cereals providing simple, rapid and reliable prediction for quality assurance.     

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.     

Effect of variety, vintage and winery on the prediction by visible and near infrared spectroscopy of the concentration of glycosylated compounds (G-G) in white grape juice

The use of visible (Vis) and near infrared (NIR) spectroscopy was explored as a rapid, simple and low cost measurement of the concentration of total glycosylated compounds in white grape juice. The effects of variety (Chardonnay, Riesling and Sauvignon Blanc), winery and vintage (2004 to 2006) on the Vis-NIR spectra were also examined. Juice samples from South Australian wineries were scanned in transmittance mode on a FOSS NIRSystems6500 instrument and subjected to laboratory analyses for the measurement of the concentration of total glycosylated compounds (G-G), total soluble solids (TSS), pH and total phenolics (TP). Partial least squares (PLS) regression method was used to relate the G-G reference data to the Vis-NIR spectra. For all samples, PLS regression resulted in a coefficient of determination in calibration ( R ²_cal) and standard error of cross validation (SECV) of 0.82 and 49.15 μM, respectively. Splitting the sample set by variety, winery or vintage improved the PLS calibrations for the variety sets. The results show that Vis-NIR spectroscopy has potential for use as a rapid, semi-quantitative technique to predict G-G concentration in white grape juices as 'low', 'medium' or 'high'. This method will be valuable when taking decisions at the winery during vintage to allocate juices according to their aroma potential. Further studies are in progress to validate the robustness and accuracy of the calibration models.     

Effects of age on composition and quality of muscle Longissimus thoracis of the Omani Arabian camel (Camelus dromedaries)

The aim of this study was to determine the effects of age on chemical composition and quality characteristics of the Arabian one-humped camel’s meat. Samples of longissimus thoracis (between the 10th and the 13th rib of the left side) were randomly collected from 21 Omani intact male camels of three different age groups: group 1 (1–3 years), group 2 (3–5 years) and group 3 (6–8 years). Samples were chilled (1–3 °C) for 48 h. Moisture, crude protein, fat and ash were determined on freeze dried ground muscle. Mineral contents were determined using an Inductively coupled plasma emission spectrometer (ICP). Meat quality including ultimate muscle pH, Warner–Bratzler shear force, sarcomere length, myofibrillar fragmentation index, expressed juice, cooking loss percent, and colour L^*, a^*, b^* were measured using standard methods.

The moisture, protein, fat and ash ranged from 64.4% to 76.7%; 18.6% to 25.0%, 1.1% to 10.5% and 1.0% to 1.4% on dry matter basis, respectively. The Ca, Mg, Na, K, P, Cad, Cr, Ni, Pb, Co, Mo, Be and V ranged from, 9.2 to 46.6, 24.7 to 57.3, 104.7 to 257.0, 471.4 to 1053.0, 249.9 to 584.0, 0.005 to 0.024, 0.020 to 0.410, 0.016 to 0.187, 0.010 to 0.299, 0.010 to 0.018, 0.050 to 0.470, 0.005 to 0.030 and 0.013 to 0.141 mg/100 g on dry matter basis, respectively. The percentage of protein decreased and that of fat increased with increasing camel age. The ultimate pH, shear force, sarcomere length, fragmentation index, expressed juice, cooking loss, lightness (L^*), redness (a^*) and yellowness (b^*) ranged from 5.46 to 6.64, 4.25 to 17.82, 0.96 to 2.50, 55.91 to 94.81,19.50 to 33.63, 13.18 to 29.88, 27.86 to 43.21, 10.46 to 22.81, and 4.63 to 10.11, respectively. Muscles of younger camels (group 1) had significantly (P < 0.05) lower shear force value, ultimate pH and higher sarcomere length, fragmentation index, expressed juice, cooking loss, and lightness color (L^*) by 48%, 3.4%, 43%, 25%, 28%, 14%, and 16% than those collected from older camels (group 3), respectively. Values of middle age camels (group 2) camels were in-between. This study confirmed that camel meat is healthy and nutritious as it contains low fat as well as being a good source of minerals. Age is an important factor in determining meat quality and composition.     

Polyphenol content and antioxidative activity in apple purées with rhubarb juice supplement

The purpose of the present study was to evaluate the effect of rhubarb juice addition and peeling in apple purée production and storage on its phenolic composition, antioxidant activity and colour. The apple material used in this study was of two varieties: Sampion and Idared. The apple purées prepared in three variants (control purées, peeling purées and purées with 5% of rhubarb juice) were stored for 3 and 6 months at 30 °C. Apple purées were investigated for their antioxidant activity, change of colour and contents of phenolic compounds plus the polymer procyanidins and degree of polymerization (DP). The preparation of purées from apples without peeling and the addition of rhubarb juice during purée preparation had significant influence on polyphenol content. The purée prepared from peeled apples of Idared variety had phenolic content 2.2 times lower than the control sample (non‐peeled apples). The highest level of total polyphenols was found in Idared + rhubarb sample (129.82 mg 100 g^?1 purée). The content of this compound in Idared control sample was more than 2.8 times lower than in Idared purées with rhubarb. The use of non‐peeled Idared apples and 5% of rhubarb juice in purée preparation significantly increased polymeric procyanidins from 11.68 to 75.20 mg 100 g^?1 and other phenolics from 9.23 to 54.62 mg/100 g. This effect for Sampion apples was smaller. The procyanidins during purées storage were more stable in samples with rhubarb juice addition. The purée samples with the addition of rhubarb juice had higher antioxidant activity measured using ABTS (2,2′azinobis‐(3‐ethylbenzthiazoline‐6‐sulphonic acid)) than purée samples without the addition (two times higher for Idared variety) of rhubarb juice. After 3 and 6 months of storage at temperature 30 °C, the antioxidant capacity decreased in all purées. Rhubarb juice had only high and positive effect on L* value in the case of Idared variety. The results showed that all Sampion apple purées had higher L* values than Idared.