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

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Identifying Chemical Differences in Cheddar Cheese Based on Maturity Level and Manufacturer Using Vibrational Spectroscopy and Chemometrics

Cheese is a nutritious dairy product and a valuable commodity. Internationally, cheddar cheese is produced and consumed in large quantities, and it is the main cheese variety that is exported from Australia. Despite its importance, the analytical methods to that are used to determine cheese quality rely on traditional approaches that require time, are invasive, and which involve potentially hazardous chemicals. In contrast, spectroscopic techniques can rapidly provide molecular information and are non-destructive, fast, and chemical-free methods. Combined with partner recognition methods (chemometrics), they can identify small changes in the composition or condition of cheeses. In this work, we combined FTIR and Raman spectroscopies with principal component analysis (PCA) to investigate the effects of aging in commercial cheddar cheeses. Changes in the amide I and II bands were the main spectral characteristics responsible for classifying commercial cheddar cheeses based on the ripening time and manufacturer using FTIR, and bands from lipids, including β'-polymorph of fat crystals, were more clearly determined through changes in the Raman spectra.

Characterization of nettle leaves (Urtica dioica) as a novel source of protease for clotting dromedary milk by non-destructive methods

This study investigates the valorization of the nettle leaves (Urtica dioica) as a novel source of a protease for clotting dromedary milk. The aim of this work is to study the effect of extracting pH on the enzymatic activity of nettle leaves extracts. The extraction was achieved in phosphate citrate buffer at different pH values (from 3 to 6.5) and the obtained extracts were used to coagulate dromedary milk. The characterization of the obtained extracts was carried out using non-destructive methods namely FT-MIR, fluorescence spectroscopy and turbiscan instrument. The extract prepared at pH = 4 had the highest proteolytic activity. The fluorescence and turbiscan measurements revealed a substantial effect of the pH value on chlorophyll residues extraction and stability, respectively. At an acidic environment (pH range of 3 - 4), the enzymatic extracts were unstable (with turbiscan stability index (TSI) values ~ 20), while at a nearly neutral pH value (pH range of 5 - 6.5), they were found to be more stable as indicated by the low TSI values ~ 1. The maximum milk-clotting activity (MCA) (0.021 U/mL) was obtained for the extracts prepared at pH = 4.

Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese

Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (R_Pre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Spectroscopic techniques for monitoring changes in the quality of milk and other dairy products during processing and storage

The application of spectroscopic techniques can help in alleviating problems encountered during the processing of milk and dairy products. Indeed, traditional analytical methods (e.g., physicochemical measurements, sensory, chromatography) are relatively expensive, time-consuming, and require chemicals and sophisticated analytical equipment, and skilled operators. Hence, there is a need to develop faster and less costly methods for accurately monitoring changes in the quality of milk and other dairy products during processing and storage.Many nondestructive and noninvasive instrumental techniques are available for inline and online monitoring of food. These include fluorescence spectroscopy, mid-infrared (MIR), near-infrared (NIR), nuclear magnetic resonance (NMR), etc. These techniques are usually used in combination with chemometric tools a to explore the information present in spectral data.This review article will discuss the potential of the above-mentioned spectroscopic techniques for monitoring chemical modifications of dairy products and the prediction of their functional properties during processing. The advantages and disadvantages of each technique are also discussed in this review. Finally, some conclusions are drawn, and the future trends of these methods are presented.

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.

Comparison of Individual and Integrated Inline Raman, Near-Infrared, and Mid-Infrared Spectroscopic Models to Predict the Viscosity of Micellar Liquids

In many industries, viscosity is an important quality parameter which significantly affects consumer satisfaction and process efficiency. In the personal care industry, this applies to products such as shampoo and shower gels whose complex structures are built up of micellar liquids. Measuring viscosity offline is well established using benchtop rheometers and viscometers. The difficulty lies in measuring this property directly in the process via on or inline technologies. Therefore, the aim of this work is to investigate whether proxy measurements using inline vibrational spectroscopy, e.g., near-infrared (NIR), mid-infrared (MIR), and Raman, can be used to predict the viscosity of micellar liquids. As optical techniques, they are nondestructive and easily implementable process analytical tools where each type of spectroscopy detects different molecular functionalities. Inline fiber optic coupled probes were employed; a transmission probe for NIR measurements, an attenuated total reflectance probe for MIR and a backscattering probe for Raman. Models were developed using forward interval partial least squares variable selection and log viscosity was used. For each technique, combinations of pre-processing techniques were trialed including detrending, Whittaker filters, standard normal variate, and multiple scatter correction. The results indicate that all three techniques could be applied individually to predict the viscosity of micellar liquids all showing comparable errors of prediction: NIR: 1.75 Pa s; MIR: 1.73 Pa s; and Raman: 1.57 Pa s. The Raman model showed the highest relative prediction deviation (RPD) value of 5.07, with the NIR and MIR models showing slightly lower values of 4.57 and 4.61, respectively. Data fusion was also explored to determine whether employing information from more than one data set improved the model quality. Trials involved weighting data sets based on their signal-to-noise ratio and weighting based on transmission curves (infrared data sets only). The signal-to-noise weighted NIR-MIR-Raman model showed the best performance compared with both combined and individual models with a root mean square error of cross-validation of 0.75 Pa s and an RPD of 10.62. This comparative study provides a good initial assessment of the three prospective process analytical technologies for the measurement of micellar liquid viscosity but also provides a good basis for general measurements of inline viscosity using commercially available process analytical technology. With these techniques typically being employed for compositional analysis, this work presents their capability in the measurement of viscosity-an important physical parameter, extending the applicability of these spectroscopic techniques.

Potentiality of front-face fluorescence and mid-infrared spectroscopies coupled with partial least square regression to predict lipid oxidation in pound cakes during storage

The potentialities of front-face fluorescence (FFF) and mid-infrared (MIR) spectroscopies coupled with partial least square regression (PLSR) were compared to predict the lipid oxidation of pound cakes. The level of lipid oxidation in pound cakes determined using classical methods showed some changes. Similarly, the fluorescence emission (305-490 nm) and excitation (252-390 nm) spectra and MIR spectra scanned in the 4000-700 cm^-1 region showed some changes in pound cakes as a function of both storage time and the type of oil used in the formulation. The application of PLSR to the MIR spectra, provided excellent predictive results for free fatty acid (R² = 0.97) and peroxide values (R² = 0.87). Similar results were obtained from both tryptophan and MIR spectra for the prediction of TOTOX (R² > 0.86) demonstrating the efficiency of the MIR and FFF spectroscopies to qualify and quantify the level of lipid oxidation in pound cakes.

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.

Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy

This study investigated the potential application of mid-infrared spectroscopy (MIR 4,000-900 cm(-1)) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-infrared spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a(30), mm), TA (SH degrees/50 mL; SH degrees = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm(-1), 3,040 to 1,700 cm(-1), and 4,000 to 3,470 cm(-1). The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a(30), range 58 mm), 0.25 SH degrees/50 mL (TA, range 3.58 SH degrees/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R(2) = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R(2) = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.

Improving the screening process for the selection of potato breeding lines with enhanced polyphenolics content

Efficient selection of potato varieties with enhanced nutritional quality requires simple, rapid, accurate, and cost-effective assays to obtain tuber chemical composition information. Our objective was to develop simple protocols to determine phenolics, anthocyanins, and antioxidant capacity in polyphenolic extracts of potatoes using Fourier transform infrared spectroscopy combined with multivariate techniques. Lyophilized potato samples (23) were analyzed. Polyphenolic compounds were extracted from potatoes and applied directly applied onto a three-bounce ZnSe crystal for attenuated total reflectance measurements in the infrared region of 4000 to 700 cm (-1). Robust models were generated (r > or = 0.99) with standard error of cross-validation values of 4.17 mg gallic acid equivalent/100 g (total phenolics), 0.87 mg pelargonidin-3-glucoside/100 g (monomeric anthocyanins), and 130.8 mumol Trolox equivalent/100 g (antioxidant capacity) potato powder. In addition, classification models discriminated potato samples at the species and variety level. Application of a simple infrared spectroscopic protocol allowed simultaneous rapid quantification of specific nutritional components in potatoes and efficient selection of value-added potato varieties.

pH prediction and control in bioprocesses using mid-infrared spectroscopy

An on-line pH monitoring method based on mid-infrared spectroscopy relevant to bioprocesses is presented. This approach is non-invasive and does not require the addition of indicators or dyes, since it relies on the analysis of species of common buffers used in culture media, such as phosphate buffer. Starting with titrations of phosphoric and acetic acid solutions over almost the entire pH range (2-12), it was shown that the infrared spectra of all samples can be expressed as a linear combination of the molar absorbance of the acids and their deprotonated forms. In other words, pH had no direct influence on the molar infrared spectra themselves, but only on deprotonation equilibria. Accurate prediction (standard error of prediction for pH < 0.15 pH units) was achieved by taking into account the non-ideal behavior of the solutions, using the Debye-Hückel theory to estimate the activity coefficients. Batch cultures of E. coli were chosen as a case study to show how this approach can be applied to bioprocess monitoring. The discrepancy between the spectroscopic prediction and the conventional electrochemical probe never exceeded 0.12 pH units, and the technique was fast enough to implement a feedback controller to maintain the pH constant during cultivation.

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.