Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham

Comparison of non-volatile compounds of Penaeus vannemei with different drying treatments via multidimensional infrared spectroscopy

To analyze the effect of various drying treatments (microwave drying (MD), hot air drying (HAD), vacuum drying (VD), and vacuum freeze drying (VFD)) on taste compounds in Penaeus vannamei, relevant indicators such as free amino acids, 5'-nucleotides, and organic acids were performed. Multidimensional infrared spectroscopy (MM-IR) results found that there were notable variations in taste properties of P. vannamei. There were 18 autocorrelation peaks in 3400-900 cm-1 were screened using second-derivative infrared spectroscopy (SD-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR). Variations in functional groups were the major contributors to taste profiles. The TAV of glutamic acid (Glu), guanine (GMP), and inosinemonphosphate (IMP) were greater than one and had notable impacts on taste profiles. VD had the highest equivalent umami value, followed by VFD, HAD, and MD. This study may provide a theoretical guide for the production of dried P. vannamei products on an industrial scale.

Near-Infrared Spectroscopy Procedure for Online Determination of Sodium and Potassium Content in Low-Salt Cured Hams

This paper reports the development of a near-infrared spectroscopy (NIRS) calibration procedure for the determination of sodium and potassium content in cured ham samples. Sliced samples of hams treated with different salts in different percentages were included in the study. Calibration models developed using partial least squares regression were cross-validated and predictive models were tested using the samples of cured ham with low sodium content. The results showed that the developed NIRS procedure is capable of directly measuring the potassium content of packaged dry-cured ham slices with low sodium content with a fitting accuracy of 91.44%, and that it can indirectly determine the sodium content by applying a correction factor to the values obtained for potassium. The prediction error between the calculated and actual sodium values determined using inductively coupled plasma atomic emission spectrophotometry (ICP-AES) was 0.004%, and this confirms that the NIRS procedure is a viable option for the determination of sodium and potassium content in this type of sample.

Evaluation of Dual-Band Near-Infrared Spectroscopy and Chemometric Analysis for Rapid Quantification of Multi-Quality Parameters of Soy Sauce Stewed Meat

The objective of this study was to evaluate the performance of near-infrared spectroscopy (NIRS) systems operated in dual band for the non-destructive measurement of the fat, protein, collagen, ash, and Na contents of soy sauce stewed meat (SSSM). Spectra in the waveband ranges of 650-950 nm and 960-1660 nm were acquired from vacuum-packed ready-to-eat samples that were purchased from 97 different brands. Partial least squares regression (PLSR) was employed to develop models predicting the five critical quality parameters. The results showed the best predictions were for the fat (Rp = 0.808; RMSEP = 2.013 g/kg; RPD = 1.666) and protein (Rp = 0.863; RMSEP = 3.372 g/kg; RPD = 1.863) contents, while barely sufficient performances were found for the collagen (Rp = 0.524; RMSEP = 1.970 g/kg; RPD = 0.936), ash (Rp = 0.384; RMSEP = 0.524 g/kg; RPD = 0.953), and Na (Rp = 0.242; RMSEP = 2.097 g/kg; RPD = 1.042) contents of the SSSM. The quality of the content predicted by the spectrum of 960-1660 nm was generally better than that for the 650-950 nm range, which was retained in the further prediction of fat and protein. To simplify the models and make them practical, regression models were established using a few wavelengths selected by the random frog (RF) or regression coefficients (RCs) method. Consequently, ten wavelengths (1048 nm, 1051 nm, 1184 nm, 1191 nm, 1222 nm, 1225 nm, 1228 nm, 1450 nm, 1456 nm, 1510 nm) selected by RF and eight wavelengths (1019 nm, 1097 nm, 1160 nm, 1194 nm, 1245 nm, 1413 nm, 1441 nm, 1489 nm) selected by RCs were individually chosen for the fat and protein contents to build multi-spectral PLSR models. New models led to the best predictive ability of Rp, RMSEP, and RPD of 0.812 and 0.855, 1.930 g/kg and 3.367 g/kg, and 1.737 and 1.866, respectively. These two simplified models both yielded comparable performances to their corresponding full-spectra models, demonstrating the effectiveness of these selected variables. The overall results indicate that NIRS, especially in the spectral range of 960-1660 nm, is a potential tool in the rapid estimation of the fat and protein contents of SSSM, while not providing particularly good prediction statistics for collagen, ash, and Na contents.

A quantitative on-line analysis of salt in cured ham by near-infrared spectroscopy and chemometrics

In this work, non-invasive near-infrared spectroscopy (NIRS) combined with chemometrics was evaluated as a possible online analytical technique to categorize pieces of cured ham on the industrial production line based on their maximum sodium content. Stifle muscle was selected for the development of the NIRS prediction models because it is the one with the highest sodium content and the easiest in terms of accessibility for spectral measurement. In the study, samples with varying thicknesses were taken. The suitability of this method is demonstrated when a 5 mm sample is used for the construction of the model, obtaining the best fit with an R²cv of 92% and a prediction error of 0.11% sodium that coincides with the error of the reference method. In conclusion, a method is proposed for the direct determination of sodium content on the production line which allows the different pieces of ham to be quickly categorized according to their salt content.

Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt

Due to the superiorities of Styrene butadiene styrene (SBS) modified asphalt, it is widely used in civil engineering application. Meanwhile, accurately predicting and obtaining performance parameters of SBS modified asphalt in unison is difficult. At present, it is essential to discover an accurate and simple method between the input and output data. ANNs are used to model the performance and behavior of materials in place of conventional physical tests because of their adaptability and learning. The objective of this study discussed the application of ANNs in determining performance of SBS modified asphalt, based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) tests. A total of 150 asphalt mixtures were prepared from three matrix asphalt, two SBS modifiers and five modifier dosages. With the most suitable algorithm and number of neurons, an ANN model with seven hidden neurons was used to predict SBS content, needle penetration and softening point by using infrared spectral data of different modified asphalts as input. The results indicated that ANN-based models are valid for predicting the performance of SBS modified asphalt. The coefficient of determination (R²) of SBS content, softening point and penetration prediction models with the same grade of asphalt exceeded 99%, 98% and 96%, respectively. It can be concluded that ANNs can provide well-satisfied regression models between the SBS content and infrared spectrum statistics sets, and the precision of penetration and softening point model founded by the same grade of asphalt is high enough to can meet the forecast demand.

Reduced Use of Nitrites and Phosphates in Dry-Fermented Sausages

Given consumer demand for foods with fewer artificial additives, the objective of this study was to investigate the effects of reduced use of nitrites and phosphates on dry-fermented sausage quality. Four sausage formulations were prepared: (1) control (using standard procedure with 0.2% phosphates and 110 mg/kg sodium nitrite) and formulations with (2) 50% less sodium nitrite, (3) 50% less sodium nitrite and sodium ascorbate (225 mg/kg), and (4) with standard nitrite but no phosphates. Weight loss and pH evolution were monitored during processing. The color, physicochemical (including oxidation), rheological, and sensory properties were evaluated on the finished product, as well as mold growth and microbiological status. Compared to control, nitrite reduction was associated with increased surface mold growth, reduced (3.0–4.4%) processing loss, and slightly higher oxidation (1.7 μg/kg more malondialdehyde) but without affecting instrumental color. The simultaneous addition of ascorbate reduced oxidation and improved color stability. The formulation without the phosphates resulted in increased oxidation (3.4 μg/kg more malondialdehyde) and changes in the instrumental color. The observed changes were relatively unimportant, as neither of the tested formulations influenced sensory traits or compromised microbial safety, implying that they can be used in production without any harm or even with some benefits.

Optimizing near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding

Simple Summary

The potential of near infrared reflectance spectroscopy (NIRS) to predict the nutritive value of chickpea straw was identified. Spectral data of 480 samples of chickpea straw (40 genotypes) were scanned with a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. The prediction equations were generated by multiple regression analysis. The NIRS prediction equations in the study accurately predicted the nutritive value of chickpea straw (R² of cross validation > 0.68; standard error of prediction < 1%). Chickpea straw nutritive value could be predicted using NIRS.

Abstract

Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of chickpea straw representing a nation-wide range of environments and genotypic diversity (40 genotypes) were scanned at a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. Multiple regression analysis was used to build the prediction equations. The prediction equation generated by the study accurately predicted the nutritive value of chickpea straw (R² of cross validation > 0.68; standard error of prediction < 1%). Breeding programs targeting improving food-feed traits of chickpea could use NIRS as a fast, cheap, and reliable tool to screen genotypes for straw nutritional quality.

Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

Rapid discrimination of Chinese dry-cured hams based on Tri-step infrared spectroscopy and computer vision technology

The aim of this study was to establish rapid and efficient methods based on a Tri-step infrared spectroscopy (Fourier transform infrared spectroscopy (FT-IR) integrated with second derivative infrared spectroscopy (SD-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR)) and computer vision technology to identify and evaluate the quality of three Chinese dry-cured hams (Jinhua, Xuanwei and Rugao hams). 9 dry-cured hams (3 different quality grades of each geographical origin) had similar IR spectra. Nevertheless, they could be further discriminated visually by SD-IR and 2DCOS-IR spectra. All samples can be separated by the computer vision technology incorporated with Principal Component Analysis (PCA) and Cluster analysis (CA). This study not only preliminarily verified the possibility of using Tri-step infrared spectroscopy and computer vision technology to discriminate the geographical origins and quality grades of Chinese dry-cured hams, but also provided prospects of the application of infrared spectroscopy and computer vision technology to authenticate other meat products.

A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

BACKGROUND

Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified.

RESULTS

Using two different NIR devices, ASD FieldSpec^®3 performed better than Polychromix Phazir™ in improving R² and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750-2500 nm), using a subset of wavelengths (1100-2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content.

CONCLUSIONS

In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.

Predicting the Quality of Meat: Myth or Reality?

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method-mainly, the sensorial quality-is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.

Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

The pet food industry is interested in performing fast analyses to control the nutritional quality of their products. This study assessed the feasibility of near-infrared spectroscopy to predict mineral content in extruded dry dog food. Mineral content in commercial dry dog food samples (n = 119) was quantified by inductively coupled plasma optical emission spectrometry and reflectance spectra (850-2500 nm) captured with FOSS NIRS DS2500 spectrometer. Calibration models were built using modified partial least square regression and leave-one-out cross-validation. The best prediction models were obtained for S (coefficient of determination; R² = 0.89), K (R² = 0.85), and Li (R² = 0.74), followed by P, B, and Sr (R² = 0.72 each). Only prediction models for S and K were adequate for screening purposes. This study supports that minerals are difficult to determine with NIRS if they are not associated with organic molecules.

Prediction of amino acids in freeze dried pork by near infrared reflectance spectroscopy

NIRS was used to predict the amino acid profile of freeze-dried pork samples. Samples (150; Longissimus thoracis et lumborum) of pork were used for analysis. After freeze drying, samples were analyzed using HPLC to find out the amino acid content. Samples were scanned and partial least squares (PLS) regression methods were used to predict the amino acid. The determination coefficient obtained by full cross-validated (80 as a sample for calibration set, 25 samples as a validation set) PLS models indicated that the NIR original spectra had an excellent ability to predict the contents of alanine, proline and methionine. Prediction of glutamic acid and glycine using standard normalized variate (SNV) pretreatment of spectral modeling was accurate. Similarly, prediction of arginine,tyrosine, valine, isoleucine, leucine, phenylalanine and lysine were accurate using SNV or multiplicative scattering correction (MSC) pre-processing spectra modeling. It was not possible to predict aspartic acid, serine, threonine, cystine, and histidine. These results indicated that the NIRS can be used for prediction of selected amino acids in the freeze dried pork.

Application of temperature and ultrasound as corrective measures to decrease the adhesiveness in dry-cured ham. Influence on free amino acid and volatile compound profile

The impact of low temperature treatment and its combination with ultrasound has been evaluated in order to correct texture defects in dry-cured hams. A total of 26 dry-cured hams, classified as high proteolysis index (PI>36%), were used. From these hams, ten slices from each ham sample were cut, vacuum packed and submitted to three different treatments: control (without treatment), conventional thermal treatments (CV) and thermal treatment assisted by power ultrasound (US). The impact of these treatments on instrumental adhesiveness, free amino acid and volatile compounds profile were assessed. Statistical analysis showed that both US and CV treatments, significantly (P < .001) decreased the instrumental adhesiveness of dry-cured hams from 85.27 g for CO to 40.59 and 38.68 g for US and CV groups, respectively. The total free amino acid content was significantly (P < .001) affected by both treatments, presenting higher values the samples from the US group (6691.5 vs. 6067.5 vs. 5278.2 mg/100 g dry matter for US, CV and CO groups, respectively). No significant differences were observed between US and CV treatments. All the individual free amino acids were influenced by ultrasound and temperature treatments, showing the highest content in sliced dry-cured ham submitted to ultrasounds at 50 °C, except for isoleucine which presented the highest level in samples from CV group. Similarly, significant differences (P < .05) were also detected in the total volatile compound content between CO and US groups, with a higher concentration in the CO batch (56,662.84 AU × 10³/g of dry-cured ham) than in the US treatment (45,848.47 AU × 10³/g of dry-cured ham), being the values in the CV treatment intermediate (48,497.25 AU × 10³/g of dry-cured ham). Aldehydes, ethers and esters, carboxylic acids and sulphur compounds were more abundant in the CO group, while CV group showed higher concentrations of ketones, alcohols and nitrogen compounds.

Assessing the influence of temperature on NIRS prediction models for the determination of sodium content in dry-cured ham slices

Temperature fluctuations are a key factor in the development of prediction models using near infrared spectroscopy (NIRS). In the present study, this influence has been investigated and a methodology has been proposed to reduce the effect of sample temperature on NIRS model prediction of the sodium content in dry-cured ham slices. Spectra were taken directly from the slices using a remote measurement probe (for non-contact analysis) at three different temperature ranges: -12 °C to -5°C, -5°C to 10 °C and 10 °C to 20 °C. Local and global temperature compensation methods were established. Partial-least squares (PLS) regression was used as a chemometrics tool to perform the calibrations. The results showed that local models were sensitive to changes in temperature, while a global temperature model using sample spectra over the entire temperature range showed good prediction ability, reducing the error caused by temperature fluctuations to acceptable levels for practical applications.

Analyzing μ-Calpain induced proteolysis in a myofibril model system with vibrational and fluorescence spectroscopy

Degree of post-mortem proteolysis influences overall meat quality (e.g. tenderness and water holding capacity). Degradation of isolated pork myofibril proteins by μ-Calpain for 0, 15 or 45 min was analyzed using four spectroscopic techniques; Raman, Fourier transform infrared (FT-IR), near infrared (NIR) and fluorescence spectroscopy. Sodium dodecyl sulfate polyacrylamide gel electrophoresis was used to determine degree of proteolysis. The main changes detected by FT-IR and Raman spectroscopy were degradation of protein backbones manifested in the spectra as an increase in terminal carboxylic acid vibrations, a decrease in CN vibration, as well as an increase in skeletal vibrations. A reduction in β-sheet secondary structures was also detected, while α-helix secondary structure seemed to stay relatively unchanged. NIR and fluorescence were not suited to analyze degree of proteolysis in this model system.

Determination of hemicellulose, cellulose and lignin content using visible and near infrared spectroscopy in Miscanthus sinensis

Lignocellulosic components including hemicellulose, cellulose and lignin are the three major components of plant cell walls, and their proportions in biomass crops, such as Miscanthus sinensis, greatly impact feed stock conversion to liquid fuels or bio-products. In this study, the feasibility of using visible and near infrared (VIS/NIR) spectroscopy to rapidly quantify hemicellulose, cellulose and lignin in M. sinensis was investigated. Initially, prediction models were established using partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function neural network (RBF_NN) based on whole wavelengths. Subsequently, 23, 25 and 27 characteristic wavelengths for hemicellulose, cellulose and lignin, respectively, were found to show significant contribution to calibration models. Three determination models were eventually built by PLS, LS-SVM and ANN based on the characteristic wavelengths. Calibration models for lignocellulosic components were successfully developed, and can now be applied to assessment of lignocellulose contents in M. sinensis.

A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products

Consumer demand for quality and healthfulness has led to a higher need for quality assurance in meat production. This requirement has increased interest in near-infrared (NIR) spectroscopy due to the ability for rapid, environmentally friendly, and noninvasive prediction of meat quality or authentication of added-value meat products. This review includes the principles of NIR spectroscopy, pre-processing methods, and multivariate analyses used for quantitative and qualitative purposes in the meat sector. Recent advances in portable NIR spectrometers that enable new online applications in the meat industry are shown and their performance evaluated. Discrepancies between published studies and potential sources of variability are discussed, and further research is encouraged to face the challenges of using NIRS technology in commercial applications, so that its full potential can be achieved.

Metabolomic Profiling as a Possible Reverse Engineering Tool for Estimating Processing Conditions of Dry-Cured Hams

Dry-cured hams are popular among consumers. To increase the attractiveness of the product, objective analytical methods and algorithms to evaluate the relationship between observable properties and consumer acceptability are required. In this study, metabolomics, which is used for quantitative profiling of hundreds of small molecules, was applied to 12 kinds of dry-cured hams from Japan and Europe. In total, 203 charged metabolites, including amino acids, organic acids, nucleotides, and peptides, were successfully identified and quantified. Metabolite profiles were compared for the samples with different countries of origin and processing methods (e.g., smoking or use of a starter culture). Principal component analysis of the metabolite profiles with sensory properties revealed significant correlations for redness, homogeneity, and fat whiteness. This approach could be used to design new ham products by objective evaluation of various features.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.

Dry-cured ham tissue characterization by fast field cycling NMR relaxometry and quantitative magnetization transfer

Fast field cycling (FFC) and quantitative magnetization transfer (qMT) NMR methods are two powerful tools in NMR analysis of biological tissues. The qMT method is well established in biomedical NMR applications, while the FFC method is often used in investigations of molecular dynamics on which longitudinal NMR relaxation times of the investigated material critically depend. Despite their proven analytical potential, these two methods were rarely used in NMR studies of food, especially when combined together. In our study, we demonstrate the feasibility of a combined FFC/qMT-NMR approach for the fast and nondestructive characterization of dry-curing ham tissues differing by protein content. The characterization is based on quantifying the pure quadrupolar peak area (area under the quadrupolar contribution of dispersion curve obtained by FFC-NMR) and the restricted magnetization pool size (obtained by qMT-NMR). Both quantities correlate well with concentration of partially immobilized, nitrogen-containing and proton magnetization exchanging muscle proteins. Therefore, these two quantities could serve as potential markers for dry-curing process monitoring. Copyright © 2016 John Wiley & Sons, Ltd.