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.

Study of the influence of the reaction parameters on nutrients recovering from digestate by struvite crystallisation.

Struvite production by crystallisation is one of the most promising methods of phosphorus recovering from wastewater or livestock waste. Lab-scale batch experiments were carried out to study the effect of supersaturation, magnesium and phosphorous concentration, pH value and temperature on struvite crystallisation reaction using pig manure digestate from an anaerobic digestion plant as raw material. Taguchi methodology has been used as method to define the design of experiments and to analyse the results. In the design of experiments, three levels of each parameter have been studied: Mg/P ratio (1.0-2.0), N/P ratio (4.0-12.0), pH (9.0-12.0) and temperature (20-40 °C). The morphology of the crystals obtained was observed by scanning electron microscopy. The results show that the optimal values of Mg/P ratio, N/P ratio, pH and temperature for struvite crystallisation are 1.5; 4.0; 10.5 and 30 °C, respectively. High supersaturation levels should be avoided to obtain high yields in the process.

Evaluation by NIRS technology of curing process of ham with low sodium content.

NIR spectroscopy combined with chemometric methods has been used to develop a prediction models of the most influential parameters in curing process of two types of hams (140 hams) using different salting techniques, lean hams salted on a tray and fatty hams in a tub, in which sodium is partially replaced. Spectral data were examined by principal component analysis and cross-validated calibration equations were developed using partial-least squares regression. Calibration errors for each parameter, obtained from cross validation (RMSECV), were similar to those obtained by reference method. For lean and fatty hams the RMSECV values were: Moisture 0.78% and 0.80; Fat 2.5 and 1.2%; Protein 0.7 and 1.7%; water activity 0.008 and 0.006; Proteolysis Index 1.6 and 1.7%; Sodium 0.11 and 0.10%; and Potassium 0.04 and 0.10. Results allow the prediction of the parameters involved in ham curing process, demonstrating the viability of the proposed method for the control and monitoring of the different stages until obtaining the final product.

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.

On-line prediction of sodium content in vacuum packed dry-cured ham slices by non-invasive near infrared spectroscopy.

In the present study, non-invasive near infrared spectroscopy (NIRS) was evaluated as a potential on-line analytical technique to predict the sodium content in dry-cured ham slices. Samples of 310 packages were scanned by applying a remote fibre-optic probe to the surface of the slices, at different temperatures, with no previous manipulation. The sodium content of the meat samples was determined by a reference method based on Inductively Coupled Plasma Atomic Emission Spectrophotometry (ICP-AES) after chemical digestion. Partial least squares (PLS) regression was used as a chemometrics method to perform the calibrations. The models yielded acceptable results with cross validation correlation coefficients (R2CV) determined 86.2-90.2%. The prediction capacity reached in the external validation was 3.63, with a standard prediction error of 0.12% Na. These results show that NIR measurements could be implemented on the packaging line of dry-cured ham slices to provide accurate and relevant information about the sodium content of each packaged products.