Elemental analysis of fish feed by laser-induced breakdown spectroscopy.

In this study, the potential of laser-induced breakdown spectroscopy (LIBS) as an efficient multi-elemental quantification tool for fish feed is determined. A particular focus of this paper is total chromium, an essential element that has the potential to be toxic and carcinogenic. In total six elements, four macro-elements (Ca, Fe, K and Mg) and two micro-elements (Cr and Rb), were modelled using LIBS spectra of aquafeed samples. Reference analysis was conducted via inductively coupled plasma mass spectrometry (ICP-MS) and showed good agreement with LIBS predictions. These results provide evidence that LIBS has the potential to be utilized in the field as a real-time screening tool for establishing the elemental composition of a range of fish feeds.

Quantification of calcium in infant formula using laser-induced breakdown spectroscopy (LIBS), Fourier transform mid-infrared (FT-IR) and Raman spectroscopy combined with chemometrics including data fusion.

Laser-induced breakdown spectroscopy (LIBS), Fourier transform mid-infrared (FT-IR) and Raman spectroscopy combined with chemometrics were investigated to quantify calcium (Ca) content in infant formula powder (INF). INF samples (n = 51) with calcium content levels (ca. 6.5-30 mg Ca/100 kJ) were prepared in accordance with the guidelines of Commission Directive 2006/125/EC. Atomic absorption spectroscopy (AAS) was used as the reference method for Ca content determination. To predict Ca content in INF samples, partial least squares regression (PLSR) models that developed based on LIBS, Raman and FT-IR spectral data, respectively. The model developed using LIBS data achieved the best performance for the quantification of Ca content in INF (R2 (cross-validation (CV))-0.99, RMSECV-0.29 mg/g; R2 (prediction (P))-1, RMSEP-0.63 mg/g). PLSR models that developed based on data fusion of Raman and FT-IR spectral features obtained the second best performance (R2CV-0.97, RMSECV-0.38 mg/g; R2P-0.97, RMSEP-0.36 mg/g). This study demonstrated the potential of LIBS, FT-IR and Raman spectroscopy to accurately quantify Ca content in INF.

Direct analysis of calcium in liquid infant formula via laser-induced breakdown spectroscopy (LIBS).

The present work illustrates the potential of laser-induced breakdown spectroscopy (LIBS) for the direct analysis of liquid food products. The aim of the experiment was to predict calcium content in ready-to-feed infant formula. The analysis was performed by a LIBS system coupled to a liquid sample chamber with a rotatory wheel that presents the liquid to the laser beam as a thin film. Multivariate analysis with partial least squares regression (PLSR) was performed to correlate LIBS spectral data to reference calcium contents. The obtained PLSR model exhibited a good fit and linearity, as indicated by the coefficients of determination for calibration (Rc2) and cross-validation (Rcv2), with values of 0.96 and 0.89, respectively. The robustness of the calibration model was assessed by external validation showing a root-mean-square error of prediction of 6.45 mg 100 mL-1. These results demonstrated the potential of LIBS for real-time analysis of liquid food products.

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.

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.

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.

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.

Developments and Challenges in Online NIR Spectroscopy for Meat Processing.

Meat and meat products are popular foods due to their balanced nutritional nature and their availability in a variety of forms. In recent years, due to an increase in the consumer awareness regarding product quality and authenticity of food, rapid and effective quality control systems have been sought by meat industries. Near-Infrared (NIR) spectroscopy has been identified as a fast and cost-effective tool for estimating various meat quality parameters as well as detecting adulteration. This review focusses on the on/inline application of single and multiprobe NIR spectroscopy for the analysis of meat and meat products starting from the year 1996 to 2017. The article gives a brief description about the theory of NIR spectroscopy followed by its application for meat and meat products analysis. A detailed discussion is provided on the various studies regarding applications of NIR spectroscopy and specifically for on/inline monitoring along with their advantages and disadvantages. Additionally, a brief description has been given about the various chemometric techniques utilized in the mentioned studies. Finally, it discusses challenges encountered and future prospects of the technology. It is concluded that, advancements in the fields of NIR spectroscopy and chemometrics have immensely increased the potential of the technology as a reliable on/inline monitoring tool for the meat industry.

Multipoint NIR spectroscopy for gross composition analysis of powdered infant formula under various motion conditions.

Powdered infant formula (PIF) is a worldwide, industrially produced, human milk substitute. Manufacture of PIF faces strict quality controls in order to ensure that the product meets all compositional requirements. Near-infrared (NIR) spectroscopy is a rapid, non-destructive and well-qualified technique for food quality assessments. The use of fibre-optic NIR sensors allows measuring in-line and at real-time, and can record spectra from different stages of the process. The non-contact character of fibre-optic sensors can be enhanced by fitting collimators, which allow operation at various distances. The system, based on a Fabry-Perot interferometer, records four spectra concurrently, rather than consecutively as in the "quasi-simultaneous" multipoint NIR systems. In the present study, a novel multipoint NIR spectroscopy system equipped with four fibre-optic probes with collimators was assessed to determine carbohydrate and protein contents of PIF samples under static and motion conditions (0.02, 0.15 and 0.30m/s) to simulate possible industrial scenarios. Best results were obtained under static conditions providing a R(2) of calibration of 0.95 and RMSEP values of 1.89%. Yet, considerably low values of RMSEP, for instance 2.70% at 0.15m/s, were provided with the in-motion predictions, demonstrating the system's potential for in/on-line applications at various levels of speed. The current work also evaluated the viability of using general off-line calibrations developed under static conditions for on/in-line applications subject to motion. To this end, calibrations in both modes were developed and compared. Best results were obtained with specific calibrations; however, reasonably accurate models were obtained with the general calibration. Furthermore, this work illustrated independency of the collimator-probe setup by characterizing PIF samples simultaneously recorded according to their carbohydrate content, even when measured under different conditions. Therefore, the improved multipoint NIR approach constitutes a potential in/on-line tool for quality evaluation of PIF over the manufacturing process.