Spectroscopic techniques coupled with chemometric tools for structure and texture determinations in dairy products

Synchronous Front-Face Fluorescence Spectra: A Review of Milk Fluorophores

Milk is subjected to different industrial processes, provoking significant physicochemical modifications that impact milk's functional properties. As a rapid and in-line method, front-face fluorescence can be used to characterize milk instead of conventional analytical tests. However, when applying fluorescence spectroscopy for any application, it is not always necessary to determine which compound is responsible for each fluorescent response. In complex matrixes such as milk where several variables are interdependent, the unique identification of compounds can be challenging. Thus, few efforts have been made on the chemical characterization of milk' fluorescent spectrum and the current information is dispersed. This review aims to organize research findings by dividing the milk spectra into areas and concatenating each area with at least one fluorophore. Designations are discussed by providing specific information on the fluorescent properties of each compound. In addition, a summary table of all fluorophores and references cited in this work by area is provided. This review provides a solid foundation for further research and could serve as a central reference.

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses

The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950-1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses.

Application of ATR-FTIR Incorporated with Multivariate Data Analysis for Discrimination and Quantification of Urea as an Adulterant in UHT Milk

Urea is naturally present in milk, yet urea is added intentionally to increase milk's nitrogen content and shelf life. In this study, a total of 50 Ultra heat treatment (UHT) milk samples were spiked with known urea concentrations (0-5 w/v%). Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with principal component analysis (PCA), discriminant analysis (DA), and multiple linear regression (MLR) were used for the discrimination and quantification of urea. The PCA was built using 387 variables with higher FL > 0.75 from the first PCA with cumulative variability (90.036%). Subsequently, the DA model was built using the same variables from PCA and demonstrated the good distinction between unadulterated and adulterated milk, with a correct classification rate of 98% for cross-validation. The MLR model used 48 variables with p-value < 0.05 from the DA model and gave R2 values greater than 0.90, with RMSE and MSE below 1 for cross-validation and prediction. The DA and MLR models were then validated externally using a test dataset, which shows 100% correct classification, and the t-test result (p > 0.05) indicated that the MLR could determine the percentage of urea in UHT milk within the permission limit (70 mg/mL). In short, the wavenumbers 1626.63, 1601.98, and 1585.5534 cm-1 are suitable as fingerprint regions for detecting urea in UHT milk.

Manufacturing and characterization of whey and stevia-based popsicles enriched with concentrated beetroot juice

The beet-root (Beta vulgaris) and whey powder together, can potentially use as a multifunctional ingredient in the manufacturing of the "Popsicles", due to their biochemical composition that can enhance the concentration of bioactive compounds. In the present study, beet-root juice concentrates were prepared at different time/temperature treatments viz 45 °C, 55 °C, and 65 °C for 120, 80 and 45 min. The effect of different time/temperature treatments on physicochemical composition, colour, antioxidant activity (%), bioactive compounds, spectral data and sensory acceptance were evaluated. The physicochemical parameters of popsicles (PTI, PT2, PT3) including protein, total phenols, betalain, radical scavenging activity %, colour and melting values were significantly affected (p ≤ 0.05) by the different time/temperature treatments. The concentration of betalain and protein in all the popsicles ranged from 1134 to 1299 mg/L and 1.92 to 1.54 g/100 g respectively. The reduction of bioactive components viz betacyanins, betaxanthins, betanin, oxalic and syringic acid was also observed in popsicle (PTI) as compared to control. Furthermore, popsicle (PT1) was prepared with beet-root juice concentrated at 45 °C showed maximum sensory acceptance. The physicochemical and organoleptic attributes of processed popsicles encourage the commercial usage of whey powder and concentrated beetroot juice.

A review combining emerging techniques with classical ones for the determination of biscuit quality: advantages and drawbacks

The production of biscuit and biscuit-like products has faced many challenges due to changes in consumer behavior and eating habits. Today's consumer is looking for safe products not only with fresh-like and pleasant taste, but also with long shelf life and health benefits. Therefore, the potentiality of the use of healthier fat and the incorporation of natural antioxidant in the formulation of biscuit has interested, recently, the attention of researchers. The determination of the biscuit quality could be performed by several techniques (e.g., physical, chemical, sensory, calorimetry and chromatography). These classical analyses are unfortunately destructive, expensive, polluting and above all very heavy, to implement when many samples must be prepared to be analyzed. Therefore, there is a need to find fast analytical techniques for the determination of the quality of cereal products like biscuits. Emerging techniques such as near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopy (FFFS), coupled with chemometric tools have many potential advantages and are introduced, recently, as promising techniques for the assessment of the biscuit quality.

FT-MIR Analysis of Water-Soluble Extracts during the Ripening of Sheep Milk Cheese with Different Phospholipid Content

The purpose of this work was to study the suitability of the water-soluble extracts (WSE) of semi-hard sheep milk cheese for analysis by diffuse reflectance Fourier transform mid-infrared spectroscopy (FT-MIR) and the development of classification models using discriminant analysis and based on cheese age or phospholipid content. WSE was extracted from three types of sheep milk cheeses (full-fat, reduced-fat and reduced-fat fortified with lyophilized sweet sheep buttermilk) at various stages of ripening from six to 168 days and lyophilized. The first model used 1854–1381 and 1192–760 cm−1 regions of the first-derivative spectra and successfully differentiated samples of different age, based on changes in the water-soluble products of ripening biochemical events. The second model used the phospholipid absorbance spectral regions (3012–2851, 1854–1611 and 1192–909 cm−1) to successfully discriminate cheeses of markedly different phospholipid content. Cheese WSE was found suitable for FT-MIR analysis. According to the results, a fast and simple method to monitor cheese ripening based on water-soluble substances has been developed. Additionally, the results indicated that a considerable amount of phospholipids migrates to the cheese WSE and that FT-MIR can be a useful tool for their assessment.

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

BACKGROUND

Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.

RESULTS

Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.

CONCLUSIONS

This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing

The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.

Application of front-face fluorescence spectroscopy as a tool for monitoring changes in milk protein concentrate powders during storage

This study investigated the feasibility of front-face fluorescence spectroscopy (FFFS) to predict the solubility index and relative dissolution index (RDI) of milk protein concentrate (MPC) powders during storage. Twenty MPC powders with varying protein contents from 4 different commercial manufacturers were used in this study. The MPC powders were stored at 2 temperatures (25 and 40°C) for 0, 1, 2, 4, 8, and 12 wk. The front-face fluorescence spectra of tryptophan and Maillard products were recorded and analyzed with chemometrics to predict solubility of MPC powders. The similarity maps showed clear discrimination of the MPC samples stored at 25 and 40°C. Partial least squares regression models were developed using the fluorescence spectra of tryptophan and Maillard products to predict the solubility index and RDI measurements of MPC powders, and the prediction models were validated using an independent test set. Coefficients of determination (R²) of 0.76, 0.84, and 0.68 were obtained between fluorescence spectra (tryptophan emission, Maillard emission, and Maillard excitation, respectively) and solubility index. The R² values for the RDI predictions were 0.58 and 0.60 for the data set of tryptophan emission and Maillard emission, respectively. The ratio of prediction error to standard deviation was >2 for Maillard emission fluorescence spectra and solubility index measurements, indicating good practical utility of the partial least squares regression prediction models. The results indicated that the solubility and dissolution behavior of MPC powders were related to their protein content and storage conditions that could be measured using FFFS. Hence, FFFS can be used as a rapid nondestructive analytical technique to predict the solubility and dissolution characteristics of MPC powders.

Study on feasibility of determination of glucosamine content of fermentation process using a micro NIR spectrometer

N-acetyl-d-glucosamine (GlcNAc) is a microbial fermentation product, and NIR spectroscopy is an effective process analytical technology (PAT) tool in detecting the key quality attribute: the GlcNAc content. Meanwhile, the design of NIR spectrometers is under the trend of miniaturization, portability and low-cost nowadays. The aim of this study was to explore a portable micro NIR spectrometer with the fermentation process. First, FT-NIR spectrometer and Micro-NIR 1700 spectrometer were compared with simulated fermentation process solutions. The R_c², R_p², RMSECV and RMSEP of the optimal FT-NIR and Micro-NIR 1700 models were 0.999, 0.999, 3.226 g/L, 1.388 g/L and 0.999, 0.999, 1.821 g/L, 0.967 g/L. Passing-Bablok regression method and paired t-test results showed there were no significant differences between the two instruments. Then the Micro-NIR 1700 was selected for the practical fermentation process, 135 samples from 10 batches were collected. Spectral pretreatment methods and variables selection methods (BiPLS, FiPLS, MWPLS and CARS-PLS) for PLS modeling were discussed. The R_c², R_p², RMSECV and RMSEP of the optimal GlcNAc content PLS model of the practical fermentation process were 0.994, 0.995, 2.792 g/L and 1.946 g/L. The results have a positive reference for application of the Micro-NIR spectrometer. To some extent, it could provide theoretical supports in guiding the microbial fermentation or the further assessment of bioprocess.

Fluorescence spectroscopy coupled with independent components analysis to monitor molecular changes during heating and cooling of Cantal-type cheeses with different NaCl and KCl contents

BACKGROUND

Reduction of NaCl content of cheeses has received considerable attention by research during the past decades because of its health effects. Nonetheless, NaCl reduction is a challenge since it plays an important role in cheese quality, such as structure, texture and functional properties. Several methods were used to evaluate the effect of NaCl on these attributes. In this study, Cantal-type cheeses with different salts (NaCl and KCl) were analyzed for their structure at a molecular level and rheological properties during heating (20-60 °C) and cooling (60-20 °C). The structure was investigated by synchronous fluorescence spectroscopy (SFS) and the rheological properties by small-amplitude oscillatory test.

RESULTS

Independent components analysis (ICA) gave three independent components that were attributed to coenzyme/Maillard reaction products (IC1), tryptophan (IC2) and vitamin A (IC3). Signal proportions of each IC depicted information regarding the changes in those fluorophores with salts, heating and cooling. In addition, canonical correlation analysis (CCA) of the IC proportions and rheological measurements related modifications at a molecular level evaluated by fluorescence to cheese texture (0.34 < R² < 0.99).

CONCLUSION

This study demonstrated that SFS can monitor and characterize modification of Cantal-type cheeses at a molecular level, based on the analysis of the fluorescence spectra by ICA. The nature of correlation between signal proportions and the rheological parameters depicted that rheological attributes of cheeses observed at the macroscopic level can be derived from fluorescence spectra. © 2017 Society of Chemical Industry.

Rennet-induced coagulation of raw and heated camel and cow milk gels determined by instrumental techniques: effects of added calcium and phosphate

BACKGROUND

The potentiality of the front-face fluorescence spectroscopy and rheological measurements combined with chemometric tools to characterise the structure evolution during coagulation of raw and preheated camel and cow milk at 50 and 70 °C with/without added calcium and phosphate was evaluated. Tryptophan and vitamin A fluorescence spectra were collected during the gelation of milk at 0, 10, 20, 30, 40, and 45 min after the addition of rennet-induced coagulation. In parallel, the storage modulus (G'), loss modulus (G″) and tan delta (tan δ) were determined using low amplitude oscillation shear analysis.

RESULTS

The principal component analysis (PCA) applied to the normalised tryptophan spectra allowed the gels made with camel milk to be differentiated from those of cow milk on the one hand, and to monitor protein structure modifications during the gelation, on the other hand. The common components and specific weights analysis (CCSWA) applied jointly to the fluorescence and rheological data sets permitted a clear separation of raw milk gels from those preheated at 50 and 70 °C.

CONCLUSION

The front-face fluorescence spectroscopy method coupled with multi-variate statistical analyses showed a high capacity for studying changes in the micelle structure throughout the rennet-induced coagulation process. © 2017 Society of Chemical Industry.

Determination of fat, protein and moisture in ricotta cheese by near infrared spectroscopy and multivariate calibration

Near-infrared reflectance spectroscopy (NIRS) with partial least squares regression (PLSR) was used to determine levels of fat, protein and moisture in ricotta cheese without complex sample preparation. Spectra of 19 conventional and low-fat ricotta samples from different manufacturers were collected in duplicate, with 33 of the 38 spectra used as a calibration set and the remaining 5 spectra used as an external validation set. The best results were obtained by processing the spectral region between 1,100 and 2,500 nm. Multivariate models with six latent variables (LVs) showed good prediction capability for fat and protein determinations, with average relative errors (Er) of 6.37 % and 5.95 %, respectively. For moisture, a more robust model was obtained with 4 LVs, showing better prevision capacity and Er of 1.91 %.

Monitoring changes in sponge cakes during aging by front face fluorescence spectroscopy and instrumental techniques

In the present study, sponge cakes, produced at the pilot scale, were monitored during aging (i.e., 1, 3, 6, 9, 16, and 20 days) by three different analytical techniques. For the texture analyzer, the hardness and elasticity of crumb cakes were found to significantly increase and decrease, respectively, throughout aging. Color parameters (L*, a*, and b*) showed only slight change throughout aging, and a high correlation (R(2) = 0.88) was observed between the whiteness and the yellowness. Tryptophan fluorescence spectra (excitation, 290 nm; emission, 305-490 nm) recorded on cakes exhibited three maxima located at 382, 435, and 467 nm that were attributed to maximum emission of tryptophan (382 nm) and fluorescent Maillard reaction products (435 and 467 nm). The principal component analysis (PCA) applied to the tryptophan spectra allowed a clear discrimination of cakes aged for 1, 3, and 6 days from those aged for 9, 16, and 20 days. Finally, canonical correlation analysis (CCA) performed on the textural and tryptophan fluorescence spectral data sets showed that the two groups of variables were highly correlated because the squared canonical coefficients for canonical variates were 0.99, indicating that cake texture determined at the macroscopic level by texture analyzer is a reflection of its structure at the molecular level determined by fluorescence spectroscopy.

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of approximately +/-10% is acceptable.

Effects of mild heating and acidification on the molecular structure of milk components as investigated by synchronous front-face fluorescence spectroscopy coupled with parallel factor analysis

This paper reports the potential of synchronous front-face fluorescence spectroscopy in the characterization at the molecular level of milk changes during mild heating from 4 to 50 degrees C and acidification in the pH range of 6.8 to 5.1. Synchronous fluorescence spectra were collected in the 250-550 nm excitation wavelength range using offsets of 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, and 240 nm between excitation and emission monochromators. The potential of parallel factor (PARAFAC) analysis in the decomposition of the whole synchronous fluorescence data set into the contribution of each of the fluorescent compounds present in milk has been investigated for heating and acidification data sets. Models were fitted from 1 to 7 components. Considering the core consistency values, PARAFAC models with three components have been considered. The first three components explained 94.43% and 94.13% of the total variance for heating and acidification data sets, respectively. The loading profiles of the first and second components derived from PARAFAC analysis performed on heating and acidification data sets corresponded quite well with the characteristics of tryptophan and vitamin A fluorescence spectra, respectively. The third component corresponded to the riboflavin fluorescence spectrum. Considering the heating experiment, the profile of the concentration mode for the second component showed large variations according to the temperature, which were assigned to the melting of triglycerides between 4 and 50 degrees C. For the acidification experiment, drastic changes in the concentration modes of the three components were observed for pH below 5.6, in agreement with structural changes in casein micelles.

Evaluation of Chemical Parameters in Soft Mold-Ripened Cheese During Ripening by Mid-Infrared Spectroscopy

The suitability of mid-infrared spectroscopy (MIR) to follow the evolution throughout ripening of specific physicochemical parameters in Camembert-type cheeses was evaluated. The infrared spectra were obtained directly from raw cheese samples deposited on an attenuated total reflectance crystal. Significant correlations were observed between physicochemical data, pH, acid-soluble nitrogen, nonprotein nitrogen, ammonia (NH4+), lactose, and lactic acid. Dry matter showed significant correlation only with lactose and nonprotein nitrogen. Principal components analysis factorial maps of physicochemical data showed a ripening evolution in 2 steps, from d 1 to d 7 and from d 8 to d 27, similar to that observed previously from infrared spectral data. Partial least squares regressions made it possible to obtain good prediction models for dry matter, acid-soluble nitrogen, nonprotein nitrogen, lactose, lactic acid, and NH4+ values from spectral data of raw cheese. The values of 3 statistical parameters (coefficient of determination, root mean square error of cross validation, and ratio prediction deviation) are satisfactory. Less precise models were obtained for pH.

Supervised pattern recognition in food analysis

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.