Raquel Cama-Moncunill

Quantification of copper content with laser induced breakdown spectroscopy as a potential indicator of offal adulteration in beef

Laser induced breakdown spectroscopy (LIBS) is an emerging technique in the field of food analysis which provides various advantages such as minimal sample preparation, chemical free, rapid detection, provision of spatial information and portability. In this study, LIBS was employed for quantitative analysis of copper content in minced beef samples spiked with beef liver over three independent batches. Copper content was determined with graphite furnace atomic absorption spectroscopy (GFAAS) in order to obtain reference values for modelling. Partial least square regression (PLSR) was performed to build a calibration and validation model. A calibration model with a high Rcv2 of 0.85 and a RMSECV of 43.5ppm was obtained, confirming a good fit for the model. The validation model showed a good prediction accuracy with a high Rp2 of 0.85 and RMSEP of 36.8ppm. Moreover, on a further study to evaluate the spatial capabilities, LIBS was able to successfully map copper content within a pellet, indicating the suitability of LIBS to provide spatial information and therefore potential use on heterogeneous samples. Overall, it can be concluded that LIBS combined with chemometrics demonstrates potential as a quality monitoring tool for the meat processing industry.

Laser induced breakdown spectroscopy for quantification of sodium and potassium in minced beef: a potential technique for detecting beef kidney adulteration

Beef is a rich source of important minerals, with potassium (K) being the most abundant mineral quantitatively except in cured meats where sodium (Na) from the added salt predominates. This study evaluates the capability of LIBS for quantification of the Na and K contents of minced beef as a potential method of detecting beef kidney adulteration. Additionally, the study aims to demonstrate the ability of LIBS to provide spatial mineral information of minced beef. A LIBS system was employed to collect spectral information of adulterated minced beef samples. Atomic absorption spectroscopy (AAS) was used to obtain reference values for Na and K. The chemometric technique of partial least squares regression (PLSR) was used to build the prediction models. Spatial mineral maps of minced beef samples were generated based on the predicted percentages of Na and K. The models for Na and K yielded calibration coefficients of determination (Rc2) of 0.97 and 0.91 respectively. Similarly, a good calibration model was obtained for adulteration yielding a Rc2 of 0.97. Good prediction accuracy was observed for all models. Spatial mapping provided two major advantages: (a) representative measurements of samples and (b) spatial distribution of multi-elements. The results observed illustrate the ability of LIBS combined with chemometrics as a potential monitoring tool for mineral quantification as well as adulteration detection for the meat processing industry.

NIR spectrophotometry with integrated beam splitter as a process analytical technology for meat composition analysis

This study aims at evaluating the potential of a multipoint NIR spectrophotometer system based on a Fabry–Perot interferometer, which incorporates a beam splitter, combined with a four point photodiode array detector and collimated light for performing on-line analysis of beef composition. Additionally it also aims at analysing the spectral information obtained with the baseline correction setting and compares it to the information obtained using a conventional approach. The system was employed to spatially predict the composition of fat trimmings of mixed minced beef samples at varied path lengths (1, 1.5 and 4 cm). The systems performance was tested in two optional modes; (a) without baseline correction adjustment and (b) with baseline correction adjustment. Both measuring modes of the spectrophotometer demonstrated individual advantages in particular situations; the first situation evaluated the simultaneous analysis of different samples set at different path lengths per probe, whereas a second situation focused on sudden changes of path lengths of the same sample per probe during a single analysis. Partial least squares regression (PLSR) was employed to establish the quantitative relationship between the spectral data and its proximate analysis values. Overall results showed good predictions for all the chemical attributes. It was concluded from the results that a baseline correction adjustment setting is needed when sudden changes in the effective path length occur, while in a case where similar path lengths are encountered, both modes could be used, obtaining good and similar performances.

Laser-induced breakdown spectroscopy (LIBS) for rapid analysis of ash, potassium and magnesium in gluten free flours

Gluten free (GF) diets are prone to mineral deficiency, thus effective monitoring of the elemental composition of GF products is important to ensure a balanced micronutrient diet. The objective of this study was to test the potential of laser-induced breakdown spectroscopy (LIBS) analysis combined with chemometrics for at-line monitoring of ash, potassium and magnesium content of GF flours: tapioca, potato, maize, buckwheat, brown rice and a GF flour mixture. Concentrations of ash, potassium and magnesium were determined with reference methods and LIBS. PCA analysis was performed and presented the potential for discrimination of the six GF flours. For the quantification analysis PLSR models were developed; R2cal were 0.99 for magnesium and potassium and 0.97 for ash. The study revealed that LIBS combined with chemometrics is a convenient method to quantify concentrations of ash, potassium and magnesium and present the potential to classify different types of flours.

Quantification of rubidium as a trace element in beef using laser induced breakdown spectroscopy

This study evaluates the potential of laser induced breakdown spectroscopy (LIBS) coupled with chemometrics to develop a quantification model for rubidium (Rb) in minced beef. A LIBSCAN 150 system was used to collect LIBS spectra of minced beef samples. Beef liver was used to spike the Rb levels in minced beef. All samples were dried, powdered and pelleted using a hydraulic press. Measurements were conducted by scanning 100 different locations with an automated XYZ sample chamber. Partial least squares regression (PLSR) was used to develop the calibration model, yielding a calibration coefficient of determination (Rc2) of 0.99 and a root mean square error of calibration (RMSEC) of 0.05ppm. The model also showed good results with leave-one-out cross validation, yielding a cross-validation coefficient of determination (Rcv2) of 0.90 and a root mean square error of cross-validation (RMSECV) of 0.22ppm. The current study shows the potential of LIBS as a rapid analysis tool for the meat processing industry.

Challenges in Model Development for Meat Composition Using Multipoint NIR Spectroscopy from At-Line to In-Line Monitoring

This study evaluates the efficiency of multipoint near-infrared spectroscopy (NIRS) to predict the fat and moisture content of minced beef samples both in at-line and on-line modes. Additionally, it aims at identifying the obstacles that can be encountered in the path of performing in-line monitoring. Near-infrared (NIR) reflectance spectra of minced beef samples were collected using an NIR spectrophotometer, employing a Fabry-Perot interferometer. Partial least squares regression (PLSR) models based on reference values from proximate analysis yielded calibration coefficients of determination (Rc2) of 0.96 for both fat and moisture. For an independent batch of samples, fat was estimated with a prediction coefficient of determination (Rp2) of 0.87 and 0.82 for the samples in at-line and on-line modes, respectively. All the models were found to have good prediction accuracy; however, a higher bias was observed for predictions under on-line mode. Overall results from this study illustrate that multipoint NIR systems combined with multivariate analysis has potential as a process analytical technology (PAT) tool for monitoring process parameters such as fat and moisture in the meat industry, providing real-time spectral and spatial information.

Prediction of beef fat content simultaneously under static and motion conditions using near infrared spectroscopy

Fat content is one of the most important quality indicators for minced beef products. In this study, a multipoint near infrared (NIR) spectrophotometer system, based on a Fabry–Perot interferometer, combined with a four-point photodiode array detector and flexible collimator–probe arrangement, was used for real-time analysis of beef fat content. The system was employed to predict fat content of mixed minced beef samples concurrently under two different conditions: (a) static and slow motion and (b) static and fast motion. Additionally, a separate measurement was conducted to further test the independency of a collimator–probe arrangement by scanning two samples with different fat percentages concurrently under static and motion conditions. Partial least squares regression was employed, obtaining coefficients of determination in calibration (R2c) of 0.95, confirming a good fit for the three models. The fat contents of samples in the independent set were predicted with reasonable accuracy: r2 in the range 0.82–0.92 and standard error of prediction in the range 3.05–3.98%. Moreover, the spectral features observed for the probe independency test clearly illustrated the flexibility and independency of the collimator–probe arrangement. This study showed that the multipoint NIR spectroscopy system can predict beef fat content concurrently under static and motion conditions and illustrates its potential use as an in-line monitoring tool at various junctions in a meat processing plant.

Moisture determination of static and in-motion powdered infant formula utilising multiprobe near infrared spectroscopy

Moisture content of powdered infant formula is a critical factor governing product quality, especially in terms of its physicochemical stability. High levels of moisture accelerate the conversion of amorphous lactose to α-lactose monohydrate, which is the main cause of sticking and caking problems. Conversely, milk powders may become more susceptible to lipid oxidation at relatively low moisture levels. Traditionally, moisture content has commonly been determined by methods based on the loss of weight when drying under controlled conditions in an oven. However, these methods are time consuming and not suitable for in-line measurement. Near infrared (NIR) spectroscopy is a rapid method and perhaps the most used and accepted alternative technique for the measurement of moisture content in the dairy industry. Significant challenges in using NIR spectroscopy in the manufacturing process are in-line measurement and real-time monitoring. In this work, a novel multiprobe NIR system based on a Fabry–Perot interferometer combined with four fibre probes was assessed for predicting the moisture content of samples with varying moisture levels (ranging from ca 2% to 13%) and recorded under static conditions and various levels of motion. Partial least squares (PLS) regression was used to correlate the spectral response to the reference moisture values. The coefficient of determination of calibration (R2) and root mean square error of cross validation for the best model were 0.99% and 0.57%, respectively. The PLS calibration model was then applied to an independent set of samples recorded under both conditions: static [root mean square error of prediction (RMSEP) = 0.62%] and motion at 0.01 m s−1 (RMSEP = 0.66%), 0.07 m s−1 (1.07%) and 0.16 m s−1 (0.97%). Therefore, moisture predictions for both measuring modes agreed well with the reference values, even when samples were travelling at speeds of 0.16 m s−1. These results demonstrated that the approach is suitable for in-line moisture analysis of powdered infant formula.

Evaluation of Diffuse Reflectance near Infrared Fibre Optical Sensors in Measurements for Chemical Identification and Quantification for Binary Granule Blends

In recent years, the pharmaceutical industry has focused on a better understanding of the real-time manufacturing process with online measurements. Near infrared (NIR) spectroscopy is perhaps the most used and acceptable technique in such a highly regulated industry. However, one of the big challenges in using NIR systems for in-line manufacturing processes is the acquisition of measurements in real-time simultaneously at different locations. To this end, a series of different optical set-ups were investigated. Results are presented here using a multipoint NIR system based on a Fabry–Pérot Interferometer capable of recording simultaneously four independent spectra in four different points. NIR spectra of cellulose and sucrose granule mixtures were evaluated by the partial least-squares (PLS) regression method. By interpreting score values, loading of the principal components and the root mean square errors of cross-validation (RMSECV), differences between two sensors composed of fibre optics and the effect of sampling (static or motion) were analysed. Sensors that allow large illumination and detection areas against small granule sizes showed better results in reducing the prediction errors (RMSECV = 1.8%) than sensors with small illumination and detection areas against samples with large granule sizes (RMSECV = 9.6%). These results are important to better understand errors associated with optic fibre probes and their configuration in multipoint NIR spectroscopy for monitoring the pharmaceutical manufacturing process.

Multipoint NIR spectroscopy for simultaneous analyses of dairy products – Part B: Quantification:

This article constitutes the second part of the study multipoint NIR spectroscopy for simultaneous analyses of dairy products. The overall purpose was to assess the suitability of a multipoint NIR approach as an on/in-line tool for simultaneous monitoring of various quality control parameters. In this part, a multipoint NIR system capable of recording 4 spectra concurrently was tested for simultaneous casein determinations of casein-lactose blends. To this end, spectral acquisition was performed so that two sample blends were scanned at a time. Evaluation of the concurrently-recorded spectra together with the partial least squares (PLS) results demonstrated that the system holds potential for simultaneous compositional analysis of dairy powders.