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

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

Formative Research to Inform the Development of a Healthy Eating Social Marketing Campaign in Mississippi

Mississippi leads the nation in child obesity, chronic diseases, poverty, and food insecurity. Stemming the long-term consequences of high obesity rates will require a cultural attitude and behavioral shift towards healthy eating. This study explored the perceptions, beliefs, practices, and self-efficacy towards healthy eating among limited resource Mississippi parents to inform a SNAP-Ed social marketing campaign. A statewide telephone survey was conducted with income-eligible or current SNAP recipients who provided or prepared food for children in their household. Likert-type scale questions measured intrapersonal factors, self-efficacy, and practices regarding healthy eating, such as shopping and meal planning. A total of 206 surveys were analyzed. Seventy-nine percent (n=163) of participants were currently receiving SNAP benefits. Healthy eating was perceived as balanced meals and fruits and vegetables. Though 60% agreed that cost was a barrier to eating more fruits and vegetables, 90% of participants had positive attitudes and beliefs towards healthy eating. In summary, Mississippi parents with limited resources were interested in providing healthy balanced meals but faced cost as the major barrier. A social marketing message with this population can be effective in emphasizing affordable healthy meals

Observations on the water distribution and extractable sugar content in carrot slices after pulsed electric field treatment

peer-reviewedThe impact of pulsed electric field (PEF) processing conditions on the distribution of water in carrot tissue and extractability of soluble sugars from carrot slices was studied. Time domain NMR relaxometry was used to investigate the water proton mobility in PEF-treated carrot samples. Three distinct transverse relaxation peaks were observed in untreated carrots. After PEF treatment only two slightly-overlapping peaks were found; these were attributed to water present in the cytoplasm and vacuole of carrot xylem and phloem tissues. This post-treatment observation indicated an increase in water permeability of tissues and/or a loss of integrity in the tonoplast. In general, the stronger the electric field applied, the lower the area representing transverse relaxation (T2) values irrespective of treatment duration. Moreover an increase in sucrose, β- and α-glucose and fructose concentrations of carrot slice extracts after PEF treatment suggested increases in both cell wall and vacuole permeability as a result of exposure to pulsed electric fields.The authors acknowledge financial support from the Irish Phytochemical Food Network (IPFN) project funded under the Food Institutional Research Measure (FIRM, 06/TNI/AFRC6) of the Irish Department of Agriculture, Food and Marine. Dr. Aguiló-Aguayo thanks Generalitat of Catalonia for the postdoctoral grant Beatriu de Pinós (BP-DGR2010). E. Balagueró thanks the Lifelong Learning Programme for the internship grant Leonardo da Vinci MOTIVA3 (201 1-1-ES1-LEO02-34225)

Gerry Downey: an authentic spectroscopist

This year has seen the retirement of Gerry Downey from active service with the Irish National Agriculture and Food Research Institute, Teagasc1 in Dublin. As one of Europe’s leading innovative spectroscopic chemometricians and a great positive personality to have as a project partner, we thought it appropriate to dedicate a column to Gerry’s career, however embarrassed he may be about the idea

Using induced chlorophyll production to monitor the physiological state of stored potatoes (Solanum tuberosum L.)

A Visible/Near-infrared (Vis/NIR) spectrometer equipped with a fibre-optic probe was used to stimulate and measure chlorophyll production in potato tubers, at low levels that produce no visible greening in the skin. Subtle responses to changes in the light stimulus were also tracked. When used with a static experimental setup, these measurements are precise. However, the technique is very sensitive to the exact geometry of the tuber-probe arrangement, and careful positioning of the probe is crucial. Complementary studies established that tissue under the apical buds (‘eyes’) has greater capacity to produce chlorophyll than other locations on the tuber surface. A long-term study of multiple tubers suggested that different cultivars behave differently in terms of the rate of chlorophyll production. These behavioural differences may be related to the batch dormancy status; validating this potential relationship is the focus of ongoing work

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

Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics

peer-reviewedThe potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R2C) and cross-validation (R2CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R2CV: 0.91 (quartering, 24 h) and R2CV: 0.96 (LTL muscle, 48 h)) and drip loss (R2CV: 0.82 (quartering, 24 h) and R2CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods

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

Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes

peer-reviewedThe data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm−1