Exploration of microwave dielectric and near infrared spectroscopy with multivariate data analysis for fat content determination in ground beef

peer-reviewedThis study investigated using microwave dielectric and near infrared (NIR) spectroscopy for the determination of fat content in ground beef samples (n=69) in a designed experiment. Multivariate data analysis (principal component analysis (PCA) and partial least squares (PLS) regression modelling) was used to explore the potential of these spectroscopic techniques over selected multiple frequency or wavelength ranges. Chemical reference data for fat and water content in ground beef were obtained using a nuclear magnetic resonance-based SMART Trac analyser. Best performace of PLS prediction models for fat content revealed a coefficient of determination in prediction (R²P) of 0.87 and a root mean square error of prediction (RMSEP) of 2.71% w/w for microwave spectroscopy; in a similar manner, R²P of 0.99 and RMSEP of 0.71% w/w were obtained for NIR spectroscopy. The influence of water content on fat content prediction by microwave spectroscopy was investigated by comparing the prediction performance of PLS regression models developed using a single Y-variable (PLS1; fat or water content) and using both Y-variables (PLS2; fat and water contents).The authors acknowledge funding for this work from the Irish Department of Agriculture, Food and the Marine under the Food Institutional Research Measure (FIRM) programme over 2013-2017

Fluorescence-based analyser as a rapid tool for determining soluble protein content in dairy ingredients and infant milk formula

peer-reviewedAbstract: Milk protein, in particular native whey protein, is of interest to dairy manufacturers as a measure of functional and nutritional quality. However, quantification of soluble whey protein (SP) is time consuming; giving rise to the need to develop rapid, accurate, and portable at-line process analytical technology. In this study, the performance of a fluorescence-based analyser(F) (Amaltheys II, Spectralys Innovations, France) was evaluated for quantification of SPF and whey protein nitrogen index (WPNI)F in skim milk, whey protein concentrate and infant formula powders. Rehydration of powders prior to analysis was a key factor for ensuring repeatability and reproducibility. A comparison of the analyser with reference methods for SPF and WPNIF resulted in coefficient of determination (R2) > 0.993 for both SPKjeldahl method and WPNIGEA. The results show the fluorescence-based analyser to be rapid, compact, and accurate device, suited for providing reliable support to dairy ingredient and infant formula manufacturers. Industrial relevance: The fluorescence based analysis investigated in this article is suitable for application in the dairy industry where it can be used as a rapid, at-line PAT tool for both liquid and powder samples. The technology has the potential to replace well-established methods for measurement of soluble protein. The main benefit to industry is the ability to respond more rapidly to variations in soluble protein without compromising on the accuracy associated with more time consuming methods

Prediction of naturally-occurring, industrially-induced and total trans fatty acids in butter, dairy spreads and Cheddar cheese using vibrational spectroscopy and multivariate data analysis

peer-reviewedThis study investigated the use of vibrational spectroscopy [near infrared (NIR), Fourier-transform mid-infrared (FT-MIR), Raman] and multivariate data analysis for (1) quantifying total trans fatty acids (TT), and (2) separately predicting naturally-occurring (NT; i.e., C16:1 t9; C18:1 trans-n, n = 6 … 9, 10, 11; C18:2 trans) and industrially-induced trans fatty acids (IT = TT – NT) in Irish dairy products, i.e., butter (n = 60), Cheddar cheese (n = 44), and dairy spreads (n = 54). Partial least squares regression models for predicting NT, IT and TT in each type of dairy product were developed using FT-MIR, NIR and Raman spectral data. Models based on NIR, FT-MIR and Raman spectra were used for the prediction of NT and TT content in butter; best prediction performance achieved a coefficient of determination in validation (R2V) ∼ 0.91–0.95, root mean square error of prediction (RMSEP) ∼ 0.07–0.30 for NT; R2V ∼ 0.92–0.95, RMSEP ∼ 0.23–0.29 for TT.This project was funded by the Irish Department of Agriculture, Food and the Marine as part of CheeseBoard 2015. Ming Zhao is a Teagasc Walsh Fellow

The Application of On-line Sensors and Novel Control Technologies for Food Processing

End of Project ReportThe objective of this research was to apply on-line continuous sensors in food processing, in particular in cheese and milk powder manufacture, in order to improve process control, for example, by achieving higher quality, increased yields, reduced losses and less downgrading of product. This project focused on technologies for monitoring rheologyrelated parameters. The main conclusions were as follows: * Seven systems for monitoring curd formation in cheesemaking were evaluated in the laboratory. * Two on-line systems for monitoring curd firmness (hot-wire and NIR reflectance) have been deployed in a commercial cheese plant with promising results. * Experimental results demonstrated that NIR reflectance / transmission probes have a potential for on-line application in cheesemaking. Despite the difference in scale, the commercial sensors compared well with the cheesemaker s observation of curd firming and look promising as an objective means of predicting curd cut time in an industrial cheese plan. * A detailed knowledge of the rheological variation in cheese curd has been developed and a means of investigating factors which influence the rheology of cheese curd (e.g. effect of heat treatment or fortification of cheesemilk) has been determined. * Technologies available for monitoring concentrate viscosity changes in the production of milk powder have been assembled at pilot scale, and initial trials have been encouraging. Further evaluation of the MTL plant to assess on-line performance, ruggedness and cleanability are planned.Department of Agriculture, Food and the Marin

Principles and mechanisms of ultraviolet light emitting diode technology for food industry applications

peer-reviewedThe application of ultraviolet (UV) light to water, food contact surfaces and medical equipment for microbial inactivation is widely employed. To date, UV disinfection sources employed are primarily low-pressure and medium-pressure mercury lamps; emitting monochromatic and polychromatic light, respectively. Despite the widespread use of mercury lamps, there are multiple drawbacks associated with their use including; high energy consumption, large size which limits reactor design, high heat emission and the presence of mercury. Light emitting diodes (LEDs) have potential for use as a highly efficient UV decontamination technology. Recent advances in semiconductor development have resulted in UV-LEDs becoming more widely available. UV-LEDs emit monochromatic light, which enables customised UV-LED disinfection systems at specific wavelengths to be developed. The application of UV-LEDs for disinfection purposes has been studied in recent years, particularly with respect to water disinfections systems. In this review, studies relating to UV-LED food applications are discussed. Furthermore, the chemical changes induced in foods, as a result of UV treatment, together with advantages and limitations of the technology are outlined

Evaluation of a fluorescence and infrared backscatter sensor to monitor acid induced coagulation of skim milk

peer-reviewedA prototype sensor that employs both ultraviolet excited fluorescence and infrared light backscatter was evaluated as an in-line process analytical technology (PAT) tool to monitor acid induced coagulation kinetics of skim milk. Coagulation experiments were carried out at 32 °C using three concentrations of glucono-delta-lactone (GDL). Measurement of storage modulus (G′) of acidified skim milk gel was used as a reference rheological method to monitor the coagulation kinetics. Prediction models were developed to predict the times required for acidified skim milk coagulum to reach selected G′ values (0.5 Pa, 1 Pa, 5 Pa, 10 Pa and 15 Pa) using time parameters extracted from the ultraviolet excited fluorescence and infrared light backscatter profiles. A strong correlation was observed between the predicted times developed using time parameters extracted from the prototype sensor profiles and the measured G′ times extracted from the rheometer (R2 = 0.97, standard error of prediction = 2.8 min). This study concluded that the prototype fluorescence and infrared backscatter sensor investigated combined with the developed rheological prediction model can be used as a potential PAT tool for in-line monitoring of coagulation kinetics in the manufacture of acid induced milk gels. Industrial relevance: The prototype fluorescence and infrared backscatter sensor investigated in this study combined with the developed rheological prediction model can be employed to monitor and control coagulation kinetics in a wide range of dairy processing applications including fresh cheese varieties and yoghurt manufacture

Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics

peer-reviewedRaman spectroscopy and chemometrics were investigated for the prediction of eating quality related physico-chemical traits of Holstein-Friesian bull beef. Raman spectra were collected on the 3rd, 7th and 14th days post-mortem. A frequency range of 1300–2800 cm− 1 was used for partial least squares (PLS) modelling. PLS regression (PLSR) models for the prediction of WBSF and cook loss achieved an R2CV of 0.75 with RMSECV of 6.82 N and an R2CV of 0.77 with RMSECV of 0.97%w/w respectively. For the prediction of intramuscular fat, moisture and crude protein content, R2CV values were 0.85, 0.91 and 0.70 with RMSECV of 0.52%w/w, 0.39%w/w and 0.38%w/w respectively. An R2CV of 0.79 was achieved for the prediction of both total collagen and hydroxyproline content, while for collagen solubility the R2CV was 0.88. All samples (100%) from 15- and 19-month old bulls were correctly classified using PLS discriminant analysis (PLS-DA), while 86.7% of samples from different muscles (longissimus thoracis, semitendinosus and gluteus medius) were correctly classified. In general, PLSR models using Raman spectra on the 3rd day post-mortem had better prediction performance than those on the 7th and 14th days. Raman spectroscopy and chemometrics have potential to assess several beef physical and chemical quality traits

On-line Sensor Control for Milk Powder and Cheese Manufacture.

End of Project ReportThis project investigated the use of on-line sensors of rheological characteristics which can be measured during the manufacture of milk powder and cheese. The objective is to use on-line measurements to fine tune each process, so as to compensate for the variability of milk.Department of Agriculture, Food and the Marin

Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

peer-reviewedHyperspectral imaging in the visible and near infrared spectral range (450–1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and non-brined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957–1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450–960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSEp 1.9%, R2p 0.92) and cooked (RMSEp 2.6%, R2p 0.83) samples used the 957–1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed

Preliminary study on the use of near infrared hyperspectral imaging for quantitation and localisation of total glucosinolates in freeze-dried broccoli

peer-reviewedThe use of hyperspectral imaging to (a) quantify and (b) localise total glucosinolates in florets of a single broccoli species has been examined. Two different spectral regions (vis–NIR and NIR), a number of spectral pre-treatments and different mask development strategies were studied to develop the quantitative models. These models were then applied to freeze-dried slices of broccoli to identify regions within individual florets which were rich in glucosinolates. The procedure demonstrates potential for the quantitative screening and localisation of total glucosinolates in broccoli using the 950–1650 nm wavelength range. These compounds were mainly located in the external part of florets.Universidad de SevillaJ.M. Hernández-Hierro thanks the Spanish MICINN for the Juan de la Cierva contract (JCI-2011-09201) and Universidad de Sevilla for the mobility Grant (Universidad de Sevilla Research Plan). Spanish MICINN Project AGL2011-30254-C02 and Junta de Andalucia PGC Project AGR 6331