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.     

基于卷积神经网络的乳粉掺杂物拉曼光谱分类方法

提出一种基于卷积神经网络的乳粉掺杂物拉曼光谱分类方法。首先利用拉曼高光谱成像平台采集足量乳粉样品的原始光谱,然后利用离散小波变换对原始光谱进行预处理,将预处理后的光谱信号作为卷积神经网络输入构建模型,并分别比较光谱预处理前后的建模效果。结果表明,不合适的光谱预处理反而会降低卷积神经网络的分类效果,而原始拉曼光谱就能被卷积神经网络精准识别,所构建的原始光谱模型对实际未知样品的识别准确率为95.5%。结果表明,卷积神经网络具备光谱预处理与建模的一体化功能,可极大简化拉曼光谱分类识别的计算过程,对乳粉质量安全筛查具有重要意义。     

A Non-invasive Method for Screening Sodium Hydroxymethanesulfonate in Wheat Flour by Near-Infrared Spectroscopy

A rapid and non-invasive method, based on near infrared diffuse reflectance spectroscopy, was established for screening sodium hydroxymethanesulfonate in wheat flour. Successive projection algorithm was used for spectral variable selection. The selected variables were applied as inputs to partial least square discriminant analysis (PLS-DA) and advanced K-means dynamic clustering. The first two principal components extracted by PLS-DA had been applied as inputs to least squares support vector machine (LS-SVM). Three algorithms, including PLS-DA, advanced K-means dynamic clustering, and LS-SVM, were used to establish the calibration model. The results of LS-SVM outperformed that of the other two methods, with the classification accuracy of 92.0% for the validation and 94.7% for the prediction. The results of the study showed the potential of near-infrared spectroscopy as a non-invasive and environmentally acceptable method for the screening of sodium hydroxymethanesulfonate in wheat flour.     

Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data

This study focuses on the detection and quantification of extra-virgin olive oil adulteration with different edible oils using mid-infrared (IR) spectroscopy with chemometrics. Mid-IR spectra were manipulated with wavelet compression previous to principal component analysis (PCA). Detection limit of adulteration was determined as 5% for corn–sunflower binary mixture, cottonseed and rapeseed oils. For quantification of adulteration, mid-IR spectral data were manipulated with orthogonal signal correction (OSC) and wavelet compression before partial least square (PLS) analysis. The results revealed that models predict the adulterants, corn–sunflower binary mixture, cottonseed and rapeseed oils, in olive oil with error limits of 1.04, 1.4 and 1.32, respectively. Furthermore, the data were analysed with a general PCA model and PLS discriminant analysis (PLS-DA) to observe the efficiency of the model to detect adulteration regardless of the type of adulterant oil. In this case, detection limit for adulteration is determined as 10%.     

Verification of fresh grass feeding,pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk

In the present study, a total of 116 tank milk samples were collected from 30 farms located in The Netherlands and analysed by Fourier-transform infrared (FTIR) spectroscopy. Samples were collected in April, May and June 2011 and in February 2012. The samples differed in the time spent by the cows on pasture, presence/absence of fresh grass in the daily ration and the farming system (organic/biodynamic or conventional). Classification models based on partial least square discriminant analysis (PLS-DA) of FTIR spectra were developed for the prediction of fresh grass feeding, pasture grazing and organic farming. The PLS-DA model discriminated between milk from cows that had fresh grass in the daily ration and milk from cows that had not fresh grass with sensitivity and specificity values of 88% and 83% in external validation and all the samples from cows that had no fresh grass collected in spring were correctly classified. The PLS-DA model developed for the authentication of pasture grazing showed comparable accuracy when the whole sample set is considered but was less accurate on the spring samples (75% of samples from cows indoors in spring correctly classified). Discrimination of organic and conventional milk was also accomplished with acceptable accuracy with % correct classification of 80% and 94% respectively in external validation. The results suggest that milk FTIR spectra contain valuable information on cows' diet that can be used for authentication purposes.     

Development of a Raman chemical imaging detection method for authenticating skim milk powder

This research developed a Raman chemical imaging method for detecting multiple adulterants in skim milk powder. Ammonium sulfate, dicyandiamide, melamine, and urea were mixed into the milk powder as chemical adulterants in the concentration range of 0.1–5.0 %. A Raman imaging system with a 785-nm laser was used to acquire hyperspectral images in the wavenumber range of 102–2,538 cm^?1 for a 25 × 25 mm² area of each mixture. A polynomial curve-fitting method was used to correct for the fluorescence background in the Raman images. An image classification method was developed based on single-band fluorescence-free images at unique Raman peaks of the adulterants. Raman chemical images were created to visualize identification and distribution of the multiple adulterant particles in the milk powder. A linear relationship was found between adulterant pixel number and adulterant concentration, demonstrating the potential of this Raman chemical imaging method for quantitative analysis of adulterants in the milk powder.     

Comparison of three methods to determine the whey protein to total protein ratio in milk

Three methods for quantification of the ratio of whey protein in total protein [capillary electrophoresis (CE), sodium dodecyl sulfate capillary electrophoresis (SDS-CE), and UV fourth derivative absorption spectroscopy (UV-4th Der.)] were applied to raw (n = 21), pasteurized (n = 5) and UHT (n = 18) milk samples. All methods effectively measured the whey protein to total protein ratio independently of the heat treatment applied to the milk. Mean values obtained by CE, SDS-CE and UV-4th Der. were respectively, 17.1, 18.5, and 17.2% for raw milks, 16.6, 17.7, and 18.8% for pasteurized milks, and 16.8, 17.0, and 17.2% for UHT milks. (Key words: whey protein to total milk protein ratio, capillary electrophoresis, sodium dodecyl sulfate capillary electrophoresis, fourth derivative ultraviolet spectroscopy)     

Simultaneous Determination of Danofloxacin and Flumequine in Milk Based on Fluorescence Spectroscopy and Chemometrics Tools

The aim of this paper was to develop a rapid screening method to determine danofloxacin (DANO) and flumequine (FLU) in milk by fluorescence spectroscopy combined with three different chemometric tools. In this study, 2-D fluorescence data and multivariate calibration based on a partial least squares discriminant analysis (PLS-DA) regression were combined to simultaneously qualify and quantify DANO and FLU concentrations in commercial ultra-high-temperature (UHT) sterilized and pasteurized milk. Calibration sets based on the UHT whole milk from brand A were built and performed using a partial least squares (PLS) regression after deproteinization. Prediction sets based on 13 types of milk were analyzed using principal component analysis (PCA), principal PLS-DA, and PLS regression models. The multivariate calibration models were better able to determine the DANO and FLU concentrations than the univariate models, and these models could be applied to other types of milk. In contrast to the PLS-DA, which had good sensitivity and specificity, the PCA yielded less satisfactory results. In the quantitative analysis, the recoveries of the two analytes were reasonable and the root mean square error of prediction was within the acceptable range. The relative standard deviations of the predicted DANO and FLU concentrations on the various testing days were 9.2 and 6.2 %, respectively, demonstrating that the analytical method had a good reproducibility.     

Quantitative determination of saturated-, monounsaturated- and polyunsaturated fatty acids in pork adipose tissue with non-destructive Raman spectroscopy

The composition of dietary fat has received increased attention during the recent years because it influences human health. Seventy seven samples from pork adipose tissue and melted fat from the same tissue were measured with Raman spectroscopy. Gas chromatography analysis was conducted as reference. Iodine values (IV) ranged from 58.2 to 90.4 g iodine added per 100 g fat. Polyunsaturated fatty acids (PUFA) ranged from 7.8% to 31.7% and monounsaturated fatty acids (MUFA) from 35.2% to 51.5% of total fatty acids. When applied on pre-processed spectra of melted fat, partial least square regression (PLSR) with cross-validation gave a correlation coefficient (R) = 0.98, and root mean square error of cross-validation (RMSECV) = 1.4 for IV, using 3 PLS factors in the model. PUFA gave R = 0.98 and RMSECV = 1.0% of total fatty acids, using 5 PLS factors. MUFA were predicted with R = 0.96 and RMSECV = 1.0% of total fatty acids, using 9 PLS factors. On adipose tissue a model with 3 PLS factors gave R = 0.97 and RMSECV = 1.8 for IV. For PUFA, a model with 3 PLS factors gave R = 0.95 and RMSECV = 1.5% of total fatty acids. For MUFA a model with 6 PLS factors gave R = 0.91 and RMSECV = 1.5% of total fatty acids. The results indicate the feasibility to use Raman spectroscopy as a rapid and non-destructive method to determine IV, PUFA, MUFA and saturated fatty acids (SFA) measured directly on pork adipose tissue and in melted fat from the same tissue.     

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.     

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.     

Optimisation of ingredients for a low‐fat,Chhana‐based dairy spread using response surface methodology

The effect of ingredients, viz. chhana, skimmed milk powder (SMP), maltodextrin and whey powder, on sensory and texture responses of a low‐fat chhana‐based spreads was studied and its formulation was optimised using response surface methodology. In linear and quadratic terms, chhana and SMP significantly influenced all the responses. The interaction term between maltodextrin and chhana was the most effective in influencing texture parameters. Optimisation suggested 55.6% chhana, 6.2% SMP, 4% maltodextrin, 2% whey powder, 1.5% edible salt, 1.5% enzyme‐modified cheese, 0.5% sodium tripolyphosphate, 0.5% trisodium citrate, 0.3% glycerol monostearate and 28% water with desirability of 0.889, as the best levels of these ingredients in a formulation that also contained 1.5% edible salt, 1.5% enzyme‐modi ed cheese, 0.5% sodium tripolyphosphate, 0.5% trisodium citrate, 0.3% glycerol monostearate and 28% water on a w/w basis.     

Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys

Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was used to quantify three different adulterants (corn syrup, high fructose corn syrup and inverted sugar) in honeys of four different locations of México (Chiapas, Oaxaca, Estado de México and Morelos). The optimal calibrations for the three adulterants were developed with partial least squares (PLS). The developed models were validated with different independent data sets being the standard error of prediction (SEP) between 1.5 and 2.1 for corn syrup, 2.1–3.0 for high fructose corn syrup and 1.4–2.5 for inverted sugar, showing the applicability of these models to the detection and quantification of adulterants in honey bee. Classification of the Mexican honeys from the four different states was carried out with soft independent modeling class analogy giving 100% correct classification rate and no false positive results were obtained.     

Determination of whey protein to total protein ratio in UHT milk using fourth derivative spectroscopy

A fourth derivative spectroscopy method was applied for the quantification of whey protein to total protein ratio in UHT milks. Some analytical features such as model compounds, selection of wavelength, linearity, repeatability and interference of milk fat were studied. The effect of refrigerated storage of raw milk, UHT treatment, and storage of UHT milk at room temperature on whey protein to total protein ratio was evaluated. No significant (p<0.05) differences among samples were found in any case. The ratio of whey protein to total protein was also determined in batches of whole (n=28) and skimmed (n=27) commercial UHT milks from different Spanish geographic areas processed by direct or indirect UHT systems in different periods of the year. The mean value was 18.1% for both whole and UHT skimmed milks. The analysis of laboratory-made mixtures of UHT milk with acid and rennet whey (2.5–15% of whey in milk expressed in protein) indicated that adulterations of UHT milk with whey up from 5% could be detected by the proposed method.     

Applications of Raman spectroscopic techniques for quality and safety evaluation of milk: A review of recent developments

Milk is a complete nutrient source for humans. The quality and safety of milk are critical for both producers and consumers, thereby the dairy industry requires rapid and nondestructive methods to ensure milk quality and safety. However, conventional methods are time-consuming and laborious, and require complicated preparation procedures. Therefore, the exploration of new milk analytical methods is essential. This current review introduces the principles of Raman spectroscopy and presents recent advances since 2012 of Raman spectroscopic techniques mainly involving surface-enhanced Raman spectroscopy (SERS), fourier-transform (FT) Raman spectroscopy, near-infrared (NIR) Raman spectroscopy, and micro-Raman spectroscopy for milk analysis including milk compositions, microorganisms and antibiotic residues in milk, as well as milk adulterants. Additionally, some challenges and future outlooks are proposed. The current review shows that Raman spectroscopic techniques have the promising potential for providing rapid and nondestructive detection of milk parameters. However, the application of Raman spectroscopy on milk analysis is not common yet since some limitations of Raman spectroscopy need to be overcome before making it a routine tool for the dairy industry.     

A machine learning proposal method to detect milk tainted with cheese whey

Cheese whey addition to milk is a type of fraud with high prevalence and severe economic effects, resulting in low yield for dairy products, nutritional reduction of milk and milk-derived products, and even some safety concerns. Nevertheless, methods to detect fraudulent addition of cheese whey to milk are expensive and time consuming, and are thus ineffective as screening methods. The Fourier-transform infrared (FTIR) spectroscopy technique is a promising alternative to identify this type of fraud because a large number of data are generated, and useful information might be extracted to be used by machine learning models. The objective of this work was to evaluate the use of FTIR with machine learning methods, such as classification tree and multilayer perceptron neural networks to detect the addition of cheese whey to milk. A total of 520 samples of raw milk were added with cheese whey in concentrations of 1, 2, 5, 10, 15, 20, 25, and 30%; and 65 samples were used as control. The samples were stored at 7, 20, and 30°C for 0, 24, 48, 72, and 168 h, and analyzed using FTIR equipment. Complementary results of 520 samples of authentic raw milk were used. Selected components (fat, protein, casein, lactose, total solids, and solids nonfat) and freezing point (°C) were predicted using FTIR and then used as input features for the machine learning algorithms. Performance metrics included accuracy as high as 96.2% for CART (classification and regression trees) and 97.8% for multilayer perceptron neural networks, with precision, sensitivity, and specificity above 95% for both methods. The use of milk composition and freezing point predicted using FTIR, associated with machine learning techniques, was highly efficient to differentiate authentic milk from samples added with cheese whey. The results indicate that this is a potential method to be used as a high-performance screening process to detected milk adulterated with cheese whey in milk quality laboratories.     

Effects of polymerized goat milk whey protein on physicochemical properties and microstructure of recombined goat milk yogurt

Goat milk whey protein concentrates were manufactured by microfiltration (MF) and ultrafiltration (UF). When MF retentate blended with cream, which could be used as a starting material in yogurt making. The objective of this study was to prepare goat milk whey protein concentrates by membrane separation technology and to investigate the effects of polymerized goat milk whey protein (PGWP) on the physicochemical properties and microstructure of recombined goat milk yogurt. A 3-stage MF study was conducted to separate whey protein from casein in skim milk with 0.1-µm ceramic membrane. The MF permeate was ultrafiltered using a 10 kDa cut-off membrane to 10-fold, followed by 3 step diafiltration. The ultrafiltration-diafiltration-treated whey was electrodialyzed to remove 85% of salt, and to obtain goat milk whey protein concentrates with 80.99% protein content (wt/wt, dry basis). Recombined goat milk yogurt was prepared by mixing cream and MF retentate, and PGWP was used as main thickening agent. Compared with the recombined goat milk yogurt without PGWP, the yogurt with 0.50% PGWP had desirable viscosity and low level of syneresis. There was no significant difference in chemical composition and pH between the recombined goat milk yogurt with PGWP and control (without PGWP). Viscosity of all the yogurt samples decreased during the study. There was a slight but not significant decrease in pH during storage. Bifidobacterium and Lactobacillus acidophilus in yogurt samples remained above 10⁶ cfu/g during 8-wk storage. Scanning electron microscopy of the recombined goat milk yogurt with PGWP displayed a compact protein network. Results indicated that PGWP prepared directly from raw milk may be a novel protein-based thickening agent for authentic goat milk yogurt making.     

Effect of maltodextrin addition on moisture sorption properties of khoa

Khoa is a popular traditional Indian milk product. It is used as the base material for preparation of variety of popular sweetmeats. The shelf life of this product is limited due to its high water activity (> 0.96). In the present investigation, an attempt was made to lower the a_w of khoa (25.62% moisture, 31.25% fat, 18.21% protein, 3.65% total ash and 21.29% lactose) by using maltodextrin (DE‐16) as a humectant (concentration: 0, 2, 5 and 10%; wt/wt). It was observed that the desorption curves of the product exhibited a sigmoid shape corresponding to type II, typical of many foods. At higher concentrations of maltodextrin, the isotherms shifted to the left compared to control. Among the several models chosen to test the fitness of sorption data, Guggenheim, Anderson and De Boer (GAB) and modified Mizrahi models showed the best fit as indicated by the smallest percentage root mean square values. The monolayer moisture content increased with increasing maltodextrin level, the range being 2.85–3.29 and 3.03–3.81 g/100 g solids for the two models, respectively. Caurie's slope also increased from 0.38 to 0.44, whereas nonfreezable water decreased from 4.98 to 4.01% as the maltodextrin level increased from 2 to 10%. Other critical water sorption parameters such as number of adsorbed monolayers, density of sorbed water and surface area of sorption were also obtained.     

Front-face fluorescence spectroscopy combined with chemometrics to detect high proteinaceous matter in milk and whey ultrafiltration permeate

Proteinaceous matter can leak into the permeate stream during ultrafiltration (UF) of milk and whey and lead to financial losses. Although manufacturers can measure protein content in the finished permeate powders, there is currently no rapid monitoring tool during UF to identify protein leak. This study applied front-face fluorescence spectroscopy (FFFS) and chemometrics to identify the fluorophore of interest associated with the protein leak, develop predictive models to quantify true protein content, and classify the types of protein leak in permeate streams. Crude protein (CP), nonprotein nitrogen (NPN), true protein (TP), tryptone-equivalent peptide (TEP), α-lactalbumin (α-LA), and β-lactoglobulin (β-LG) contents were measured for 37 lots of whey permeate and 29 lots of milk permeate from commercial manufacturers. Whey permeate contained more TEP than did milk permeate, whereas milk permeate contained more α-LA and β-LG than did whey permeate. The types of protein leak were thus identified for predictive model development. Based on excitation-emission matrix (EEM) of high- and low-TP permeates, tryptophan excitation spectra were collected for predictive model development, measuring TP content in permeate. With external validation, a useful model for quality control purposes was developed, with a root mean square error of prediction of 0.22% (dry basis) and a residual prediction deviation of 2.8. Moreover, classification models were developed using partial least square discriminant analysis. These classification methods can detect high TP level, high TEP level, and presence of α-LA or β-LG with 83.3%, 84.8%, and 98.5% cross-validated accuracy, respectively. This method showed that FFFS and chemometrics can rapidly detect protein leaks and identify the types of protein leak in UF permeate. Implementation of this method in UF processing plants can reduce financial loss from protein leaks and maintain high-quality permeate production.     

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。