Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk

BACKGROUND

Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk.

RESULTS

We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy.

CONCLUSION

This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy

The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.

Determination of quality traits and possible adulteration of molasses using FT-IR spectroscopy: A study from Turkish market

We aimed to develop portable Fourier transform infrared (FT-IR) spectroscopy-based prediction algorithms for the key quality characteristics (soluble solids, water activity, pH, sucrose, glucose, fructose, fructose/glucose, hydroxymethylfurfural) of various types of molasses, establish their legitimacy, and create a model to separate them based on their botanical origin. Samples labeled as carob (n = 27), grape (n = 24), Juniper (n = 13), and mulberry (n = 12) were purchased from different local markets in Turkey. Labeling issues were revealed in five carob and seven grape molasses, and those samples classified as non-authentic by the FT-IR algorithms were corroborated by reference analysis. Partial least squares regression models generated to predict the key quality traits of Turkish molasses demonstrated excellent correlation with reference analysis (R²_Val ≥ 0.96) and low standard error of prediction (SEP ≤ 2.88). The FT-IR sensor provided a feasible approach for molasses testing to assess its quality through manufacturing and storage, also provided a powerful tool to -ensure proper product labeling.

Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk

Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.

Screening of Acrylamide of Par-Fried Frozen French Fries Using Portable FT-IR Spectroscopy

Current assays for acrylamide screening rely heavily on LC-MS/MS or GC-MS, techniques that are not suitable to support point of manufacturing verification because it can take several weeks to receive results from a laboratory. A portable sensor that can detect acrylamide levels in real-time would enable in-house testing to safeguard both the safety of the consumer and the economic security of the agricultural supplier. Our objective was to develop a rapid, accurate, and real-time screening technique to detect the acrylamide content in par-fried frozen French fries based on a portable infrared device. Par-fried French fries (n = 70) were manufactured at times ranging from 1 to 5.5 min at 180 °C to yield a wide range of acrylamide levels. Spectra of samples were collected using a portable FT-IR device operating from 4000 to 700 cm⁻¹. Acrylamide was extracted using QuEChERS and quantified using uHPLC-MS/MS. Predictive algorithms were generated using partial least squares regression (PLSR). Acrylamide levels in French fries ranged from 52.0 to 812.8 µg/kg. The best performance of the prediction algorithms required transformation of the acrylamide levels using a logarithm function with models giving a coefficient of correlation (Rcv) of 0.93 and RPD as 3.8, which means the mid-IR model can be used for process control applications. Our data corroborate the potential of portable infrared devices for acrylamide screening of high-risk foods.

The Combination of Vibrational Spectroscopy and Chemometrics for Analysis of Milk Products Adulteration

Milk products obtained from cow, goat, buffalo, sheep, and camel as well as fermented forms such as cheese, yogurt, kefir, and butter are in a category of the most nutritious foods due to their high contents of high protein contributing to total daily energy intake. For certain reasons, high price milk products may be adulterated with low-quality ones or with foreign substances such as melamine and formalin which are added into them; therefore, a comprehensive review on analytical methods capable of detecting milk adulteration is needed. The objective of this narrative review is to highlight the use of vibrational spectroscopies (near infrared, mid infrared, and Raman) combined with multivariate analysis for authentication of milk products. Articles, conference reports, and abstracts from several databases including Scopus, PubMed, Web of Science, and Google Scholar were used in this review. By selecting the correct conditions (spectral treatment, normal versus derivative spectra at wavenumbers region, and chemometrics techniques), vibrational spectroscopy is a rapid and powerful analytical technique for detection of milk adulteration. This review can give comprehensive information for selecting vibrational spectroscopic methods combined with chemometrics techniques for screening the adulteration practice of milk products.

FTIR spectroscopy with chemometrics for determination of tylosin residues in milk

BACKGROUND

Contamination of milk by antibiotic residues represents risks to the health of consumers; therefore they should be monitored. The objective of this study was to propose a methodology for the determination of tylosin residues directly in fluid milk based on mid-infrared spectroscopy associated with chemometrics, using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy associated with multilayer perceptron network (MLP) and partial least squares (PLS).

RESULTS

MLP was shown to be adequate for the discrimination of milk samples contaminated with tylosin below or equal to or above the maximum residue limit (MRL), with an accuracy greater than 99%, using FTIR spectra data. PLS was shown to be appropriate for the prediction of the very low concentrations (0-100 μg L^-1 ) of tylosin residues in milk using FTIR spectra data. PLS models with high correlation coefficients (R > 0.99) were generated.

CONCLUSION

FTIR with chemometrics proved to be a non-destructive, efficient and low-cost method for the investigation and quantification of tylosin residues directly in fluid milk. © 2020 Society of Chemical Industry.

The recent advances of near-infrared spectroscopy in dairy production-a review

One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances.

Detection of Lipid Oxidation in Infant Formulas: Application of Infrared Spectroscopy to Complex Food Systems

 Fish- or algal oils have become a common component of infant formula products for their high docosahexaenoic acid (DHA) content. DHA is widely recognized to contribute to the normal development of the infant, and the European Commission recently regulated the DHA content in infant formulas. For many manufacturers of first-age early life nutrition products, a higher inclusion level of DHA poses various challenges. Long-chain polyunsaturated fatty acids (LC-PUFAs) such as DHA are very prone to oxidation, which can alter the organoleptic property and nutritional value of the final product. Traditional methods for the assessment of oxidation in complex systems require solvent extraction of the included fat, which can involve harmful reagents and may alter the oxidation status of the system. A rapid, efficient, non-toxic real-time method to monitor lipid oxidation in complex systems such as infant formula emulsions would be desirable. In this study, infrared spectroscopy was therefore chosen to monitor iron-induced oxidation in liquid infant formula, with conjugated dienes and headspace volatiles measured with GC-MS as reference methods. Infrared spectra of infant formula were recorded directly in mid- and near-infrared regions using attenuated total reflectance Fourier-transform (ATR-FTIR) and near-infrared (NIRS) spectrophotometers. Overall, good correlation coefficients (R2 > 0.9) were acquired between volatiles content and infrared spectroscopy. Despite the complex composition of infant formula containing proteins and sugars, infrared spectroscopy was still able to detect spectral changes unique to lipid oxidation. By comparison, near-infrared spectroscopy (NIRS) presented better results than ATR-FTIR: prediction error ATR-FTIR 18% > prediction error NIRS 9%. Consequently, NIRS demonstrates great potential to be adopted as an in-line or on-line, non-destructive, and sustainable method for dairy and especially infant formula manufacturers.

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples

Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Predicting milk mid-infrared spectra from first-parity Holstein cows using a test-day mixed model with the perspective of herd management

The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems.

Paper-Based SERS Platform for One-Step Screening of Tetracycline in Milk

Throughout the last decade, the expansion of food testing has been gradually moving towards ordinary high throughput screening methods performed on-site. The demand for point-of-care testing, able to distinguish molecular signatures with high accuracy, sensitivity and specificity has been significantly increasing. This new requirement relies on the on-site detection and monitorization of molecular signatures suitable for the surveillance of food production and processing. The widespread use of antibiotics has contributed to disease control of livestock but has also created problems for the dairy industry and consumers. Its therapeutic and subtherapeutic use has increased the risk of contamination in milk in enough concentrations to cause economic losses to the dairy industry and have a health impact in highly sensitive individuals. This study focuses on the development of a simple Surface-Enhanced Raman Spectroscopy (SERS) method for fast high throughput screening of tetracycline (TET) in milk. For this, we integrate a paper-based low-cost, fully recyclable and highly stable SERS platform, with a minimal sample preparation protocol. A two-microliter sample of milk solutions spiked with TET (from 0.01 to 1000 ppm) is dried on a silver nanoparticle coated cardboard substrate and measured via a Raman spectrophotometer. The SERS substrate showed to be extremely stable with a shelf life of several months. A global spectrum principal component analysis approach was used to test all the detected vibrational modes and their correlation with TET concentration. Peak intensity ratios (455 cm-1/1280 cm-1 and 874 cm-1/1397 cm-1) were found to be correlated with TET concentrations in milk, achieving a sensitivity as low as 0.1 ppm. Results indicate that this SERS method combined with portable Raman spectrometer is a potential tool that can be used on-site for the monitoring of TET residues and other antibiotics.

The use of mid-infrared spectra to map genes affecting milk composition

The aim of this study was to investigate the feasibility of using mid-infrared (MIR) spectroscopy analysis of milk samples to increase the power and precision of genome-wide association studies (GWAS) for milk composition and to better distinguish linked quantitative trait loci (QTL). To achieve this goal, we analyzed phenotypic data of milk composition traits, related MIR spectra, and genotypic data comprising 626,777 SNP on 5,202 Holstein, Jersey, and crossbred cows. We performed a conventional GWAS on protein, lactose, fat, and fatty acid concentrations in milk, a GWAS on individual MIR wavenumbers, and a partial least squares regression (PLS), which is equivalent to a multi-trait GWAS, exploiting MIR data simultaneously to predict SNP genotypes. The PLS detected most of the QTL identified using single-trait GWAS, usually with a higher significance value, as well as previously undetected QTL for milk composition. Each QTL tends to have a different pattern of effects across the MIR spectrum and this explains the increased power. Because SNP tracking different QTL tend to have different patterns of effect, it was possible to distinguish closely linked QTL. Overall, the results of this study suggest that using MIR data through either GWAS or PLS analysis applied to genomic data can provide a powerful tool to distinguish milk composition QTL.

Determination of trace amounts with ATR FTIR spectroscopy and chemometrics: 5-(hydroxymethyl)furfural in honey

The feasibility of Attenuated Total Reflectance Fourier Transform Infrared (ATR FTIR) spectroscopy and chemometric modeling for analysis of trace compounds is studied on the example of 5-(hydroxymethyl)furfural (HMF) in honey up to a concentration of 110 ppm, and compared to results in literature. Randomized repeated measurements of the calibration samples were carried out to prevent analytical artifacts. A partial least squares (PLS) model was calculated for quantitative determination. The considered spectral range has significant influence on the PLS model. Furthermore, averaging of spectra can improve the model quality due to the elimination of statistical noise. A low root mean square error of prediction (RMSEP) of 13 ppm was achieved. Hence, the results indicate that trace compound determination with ATR FTIR spectroscopy is possible, although calibration and modeling efforts are higher compared to standard applications of ATR FTIR spectroscopy and chemometrics.

Spectroscopic and Chemometric Comparison of Local River Sands with the Aggregate Component in Mortars from Ancient Roman Buildings Located in the X Regio Between the Livenza and Tagliamento Rivers, Northeast Italy

A combined spectroscopic-chemometric approach is used to investigate the possible sources of material exploited by the Romans to build four domus and a public thermae complex (I century BC-II century AD) located in the X Regio portion between the rivers Livenza and Tagliamento in northeast Italy. The rounded grain shapes observed in the aggregates recovered from the mortar fragments suggest that the rivers flowing close to the archaeological sites are the most likely sources of material. Coarse-grained sediment samples from nine rivers and streams in this area are examined and their similarities to the aggregate samples evaluated. The diffuse reflection Fourier transform infrared (FT-IR) spectra of the sand samples in the 63-420 µm fraction, consisting mainly of carbonates and silicates, are studied and compared in suitable spectral ranges through principal components analysis. As an additional step, the relationship between the most significant scores plots and the composition of the samples is investigated using appropriate descriptive indexes obtained from the spectra. The analysis performed on the river sand spectra alone shows that the samples from the easternmost Lemene and Tagliamento rivers present a behavior distinct from that of the western rivers (Cosa, Livenza, Meduna, Meschio, and Noncello), in agreement with their present location and past history. All the aggregate spectra investigated are compatible with the spectral characteristics of the samples from the Tagliamento and Lemene rivers, except for those from the Torre di Pordenone site, which are more similar to the sand spectra of the western rivers.

Mid-infrared spectroscopy and multivariate analysis for determination of tetracycline residues in cow's milk

Mid-infrared spectroscopy and chemometric analysis were tested to determine tetracycline's residues in cow's milk. Cow's milk samples (n = 30) were spiked with tetracycline, chlortetracycline, and oxytetracycline in the range of 10-400 µg/l. Chemometric models to quantify each of the tetracycline's residues were developed by applying Partial Components Regression and Partial Least Squares algorithms. The Soft Independent Modeling of Class Analogy model was used to differentiate between pure milk and milk sample with tetracycline residues. The best models for predicting the levels of these antibiotics were obtained using Partial Least Square 1 algorithm (coefficient of determination between 0.997-0.999 and the standard error of calibration from 1.81 to 2.95). The Soft Independent Modeling of Class Analogy model showed well-separated groups allowing classification of milk samples and milk sample with antibiotics. The obtained results demonstrate the great analytical potential of chemometrics coupled with mid-infrared spectroscopy for the prediction of antibiotic in cow's milk at a concentration of microgram per litre (µg/l). This technique can be used to verify the safety of the milk rapidly and reliably.

Adulteration identification in raw milk using Fourier transform infrared spectroscopy

Adulteration of milk is a common practice that concerns regulatory agencies, industry, and the population. Despite the growing need for checking adulteration, the current methods employed generally have low performance and are highly dependent on manual labor. This study aims to calibrate and validate a compact equipment (MilkoScan FT1) that adopts a Fourier transform infrared spectroscopy methodology to monitor adulteration in raw milk. Almost 2500 milk samples were used for reference spectrum construction and 1650 samples were used to validate the identification of the following five most commonly used adulterants (at three different concentrations each): (1) cornstarch, (2) sodium bicarbonate, (3) sodium citrate, (4) formaldehyde, and (5) saccharose, plus the additions of two levels of water or whey. To define the calibration with the best performance in milk adulteration identification, 12 calibrations involving 8, 10, 12, 14, 16, or 18 factors, with one or two outlier eliminations, were developed. The results of sensitivity and specificity analyses, as well as Kruskal-Wallis and Dunn multiple comparison tests, revealed that the calibration that best identified the adulterants was the one involving 14 factors, with a single elimination of outliers, exhibiting for all adulterants simultaneously, 84% sensitivity and 100% specificity. The calibration showed excellent sensitivity to cornstarch (>98%), sodium bicarbonate (100%), sodium citrate (99%), and formaldehyde (>84%), indicating that this calibration has good capacity for adulteration detection. Thus, this methodology is a viable option for the dairy industry to identify adulteration of raw milk.

Near-Infrared Spectroscopy Assay of Key Quality-Indicative Ingredients of Tongkang Tablets

The objective of this paper is to develop an easy and fast near-infrared spectroscopy (NIRS) assay for the four key quality-indicative active ingredients of Tongkang tablets by comparing the true content of the active ingredients measured by high performance liquid chromatography (HPLC) and the NIRS data. The HPLC values for the active ingredients content of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin in Tongkang tablets were set as reference values. The NIRS raw spectra of Tongkang tablets were processed using first-order convolution method. The iterative optimization method was chosen to optimize the band for Cimicifuga glycoside and 5-O-methylvisamminol, and correlation coefficient method was used to determine the optimal band of calycosin glucoside and hesperidin. A near-infrared quantitative calibration model was established for each quality-indicative ingredient by partial least-squares method on the basis of the contents detected by HPLC and the obtained NIRS spectra. The correlation coefficient R ² values of the four models of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin were 0.9025, 0.8582, 0.9250, and 0.9325, respectively. It was demonstrated that the accuracy of the validation values was approximately 90% by comparison of the predicted results from NIRS models and the HPLC true values, which suggested that NIRS assay was successfully established and validated. It was expected that the quantitative analysis models of the four indicative ingredients could be used to rapidly perform quality control in industrial production of Tongkang tablets.

Big Data Analytics in Chemical Engineering

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.

Graphene laminated gold bipyramids as sensitive detection platforms for antibiotic molecules

Atomic layers of graphene were optomechanically laminated onto gold bipyramids (length of ∼95 ± 3 nm and sharp tip radius less than 10 nm) using laser induced shock pressure. The fabricated graphene-gold bipyramid hybrids were employed as surface enhanced Raman scattering (SERS)-active substrates for the detection of tetracycline, an antibiotic, at very low concentrations.

Characterization of large amyloid fibers and tapes with Fourier transform infrared (FT-IR) and Raman spectroscopy

Amyloids are self-assembled protein structures implicated in a host of neurodegenerative diseases. Organisms can also produce "functional amyloids" to perpetuate life, and these materials serve as models for robust biomaterials. Amyloids are typically studied using fluorescent dyes, Fourier transform infrared (FT-IR), or Raman spectroscopy analysis of the protein amide I region, and X-ray diffraction (XRD) because the self-assembled β-sheet secondary structure of the amyloid can be easily identified with these techniques. Here, FT-IR and Raman spectroscopy analyses are described to characterize amyloid structures beyond just identification of the β-sheet structure. It has been shown that peptide mixtures can self-assemble into nanometer-sized amyloid structures that then continue to self-assemble to the micrometer scale. The resulting structures are flat tapes of low rigidity or cylinders of high rigidity depending on the peptides in the mixture. By monitoring the aggregation of peptides in solution using FT-IR spectroscopy, it is possible to identify specific amino acids implicated in β-sheet formation and higher order self-assembly. It is also possible to predict the final tape or cylinder morphology and gain insight into the structure's physical properties based on observed intermolecular interactions during the self-assembly process. Tapes and cylinders are shown to both have a similar core self-assembled β-sheet structure. Soft tapes also have weak hydrophobic interactions between alanine, isoleucine, leucine, and valine that facilitate self-assembly. Rigid cylinders have similar hydrophobic interactions that facilitate self-assembly and also have extensive hydrogen bonding between glutamines. Raman spectroscopy performed on the dried tapes and fibers shows the persistence of these interactions. The spectroscopic analyses described could be generalized to other self-assembling amyloid systems to explain property and morphological differences.

Use of FTIR for rapid authentication and detection of adulteration of food

Fourier transform infrared (FTIR) spectroscopy is an appealing technology for the food industry because simple, rapid, and nondestructive measurements of chemical and physical components can be obtained. Advances in FTIR instrumentation combined with the development of powerful multivariate data analysis methods make this technology ideal for large volume, rapid screening and characterization of minor food components down to parts per billion (ppb) levels. Because of the use of FTIR techniques in quality and process control applications, the food industry is already familiar with the technology and its potential to expand to monitoring for food adulteration. The aim of this review is to compile the current research on applications of near infrared (NIR) and mid-infrared (MIR) spectroscopy for rapid authentication and detection of adulteration in food.

Nondestructive determination of curcuminoids from turmeric powder using FT-NIR

Nondestructive method of measuring curcuminoid content of turmeric powder was developed by fourier transform near infrared (FT-NIR) spectrometer. Also, the calibration models describing the relationship between curcuminoid content and the NIR spectra of the turmeric powder were developed and evaluated. To develop the model, NIR reflectance spectra were acquired for each turmeric powder sample. Spectra were pre-processed by applying different spectra correction algorithms. The relationship between spectrometric curcuminoid content and FT-NIR spectra of turmeric powder was examined through partial least square (PLS) regression method in the wave number range of 6102 to 4246 cm(-1). The root mean square error of cross-validation and test set validation were 0.104 and 0.201 whereas the determination coefficient (R(2)) for both the validations were 97.64 and 87.53, respectively. A low value of root mean square error and high value of R(2) indicates a high correlation between the concentration and spectral data. Thus the FT-NIR offers a viable alternative to spectroscopic procedures as well as the method is time and cost effective for determination of curcuminoid content of turmeric.

Predicting the fatty acid composition of milk: a comparison of two Fourier transform infrared sampling techniques

In the present study a novel approach for Fourier transform infrared (FT-IR) characterization of the fatty acid composition of milk based on dried film measurements has been presented and compared to a standard FT-IR approach based on liquid milk measurements. Two hundred and sixty-two (262) milk samples were obtained from a feeding experiment, and the samples were measured with FT-IR as dried films as well as liquid samples. Calibrations against the most abundant fatty acids, CLA (i.e., 18:2cis-9, trans-11), 18:3cis-9, cis-12, cis-15, and summed fatty acid parameters were obtained for both approaches. The estimation errors obtained in the dried film calibrations were overall lower than the corresponding liquid sample calibrations. Similar and good calibrations (i.e., R(2) ranges from 0.82 to 0.94 (liquid samples) and from 0.88 to 0.97 (dried films)) for short-chain fatty acids (6:0-14:0), 18:1cis-9, SAT, MUFA, and iodine value were obtained by both approaches. However, the dried film approach was the only approach for which feasible calibrations (i.e., R(2) ranges from 0.78 to 0.93) were obtained for the major saturated fatty acids 16:0 and 18:0, the minor fatty acid features 4:0, CLA (i.e., 18:2cis-9, trans-11), PUFA, and the summed 18:1 trans isomers. For the dried film approach, logical spectral features were found to dominate the respective fatty acid calibration models. The preconcentration step of the dried film approach could be expected to account for a major part of the prediction improvements going from predictions in liquid milk to predictions in dried films. The dried film approach has a significant potential for use in high-throughput applications in industrial environments and might also serve as a valuable supplement for determination of genetic and breeding factors within research communities.

Changes on enological parameters of white wine packaged in bag-in-box during secondary shelf life

This study investigated the effects of temperature (22, 35, and 45 degrees C), storage time (48, 30, and 15 d), and packaging type on the quality of white wine in bag-in-box (BIB) during the secondary shelf life. Several enological parameters (color and contents of free and total SO2, total aldehyde, and total phenol) were monitored and correlated with oxygen transmission rate (OTR) and Fourier transform infrared (FTIR) spectral data. Time and temperature had significant effects on color development and SO2 depletion during storage. The increased absorbance at 420 nm was correlated with decreases of free SO2 and total SO2. Overall, total phenol content correlated negatively with total aldehyde content. The variance of the enological parameters can be correlated with the OTR data, indicating the barrier properties for the tested packages were different. FTIR-ATR spectra of the wine were analyzed chemometrically using PLS algorithm. The resulting models were able to predict the A(420), free SO2, total SO2, total phenol, total aldehyde, and storage time of the wines. This technique can potentially be used as an efficient tool to evaluate the quality of wine.

Rapid determination of Swiss cheese composition by Fourier transform infrared/attenuated total reflectance spectroscopy

There is a need for rapid and simple techniques that can be used to predict the quality of cheese. The aim of this research was to develop a simple and rapid screening tool for monitoring Swiss cheese composition by using Fourier transform infrared spectroscopy. Twenty Swiss cheese samples from different manufacturers and degree of maturity were evaluated. Direct measurements of Swiss cheese slices (approximately 0.5 g) were made using a MIRacle 3-reflection diamond attenuated total reflectance (ATR) accessory. Reference methods for moisture (vacuum oven), protein content (Kjeldahl), and fat (Babcock) were used. Calibration models were developed based on a cross-validated (leave-one-out approach) partial least squares regression. The information-rich infrared spectral range for Swiss cheese samples was from 3,000 to 2,800 cm(-1) and 1,800 to 900 cm(-1). The performance statistics for cross-validated models gave estimates for standard error of cross-validation of 0.45, 0.25, and 0.21% for moisture, protein, and fat respectively, and correlation coefficients r > 0.96. Furthermore, the ATR infrared protocol allowed for the classification of cheeses according to manufacturer and aging based on unique spectral information, especially of carbonyl groups, probably due to their distinctive lipid composition. Attenuated total reflectance infrared spectroscopy allowed for the rapid (approximately 3-min analysis time) and accurate analysis of the composition of Swiss cheese. This technique could contribute to the development of simple and rapid protocols for monitoring complex biochemical changes, and predicting the final quality of the cheese.

Effect of the phospholipid head group in antibiotic-phospholipid association at water-air interface

We studied the interactions of tetracycline antibiotics, TCs, with phospholipid monolayers with the two-fold aim of elucidating the mechanism of action of TCs and to provide a first step for the realization of bio-mimetic sensor for such drugs by means of the Langmuir-Blodgett technique. Preliminary surface tension studies demonstrated that surface activity of tetracycline is moderate and dependent on the pH of the subphase. We selected three phospholipids having hydrophobic chains of the same length but differing in the polar head structures, i.e. dipalmitoylphosphatidylcholine, dipalmitoylphosphatidylethanolamine, and dipalmitoylphosphatidic acid. Surface pressure- and surface potential- area isotherms were employed to investigate the behavior of the phospholipid monolayers at the water-air interface when tetracycline was added to the aqueous subphase. Analysis of the results indicated that the electrostatic interaction is the driving force for migration of tetracycline towards the interface where localized adsorption to the head groups occurs. Nevertheless, such interactions appear to be insufficient to promote penetration of tetracycline through the hydrophobic layer.