Ian Murray

Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy

This study was implemented to evaluate the potential of visible and near infrared reflectance (NIR) spectroscopy to predict sensory characteristics related to the eating quality of lamb meat samples. A total of 232 muscle samples from Texel and Scottish Blackface lambs was analyzed by chemical procedures and scored by assessors in a taste panel (TP). Then, these parameters were predicted from Vis/NIR spectra. The prediction equations showed that the absorbance data could explain a significant but relatively low proportion of the variability (R(2)<0.40) in the taste panel traits (texture, juiciness, flavour, abnormal flavour and overall liking) of the lamb meat samples. However, a top-tail approach, looking at the spectra of the 25 best and worst samples as judged by TP assessors, provided more meaningful results. This approach suggests that the assessors and the spectrophotometer were able to discriminate between the most extreme samples. This may have practical implications for sorting meat into a high quality class, which could be branded, into a low quality class sold for a lower price for less demanding food use. Regarding the chemical parameters, both intramuscular fat and water could be more accurately predicted by Vis/NIR spectra (R(2)=0.841 and 0.674, respectively) than sensory characteristics. In addition, the results obtained in the present study suggest that the more important regions of the spectra to estimate the sensory characteristics are related to the absorbance of these two chemical components in meat samples.

Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fishmeal with meat and bone meal

Since the link between feeding ruminant-derived meat and bone meal (MBM) and the occurrence of bovine spongiform encephalopathy (BSE) and its human equivalent variant Creutzfeldt–Jakob disease (vCJD) has been established, it is imperative that potentially infective material is excluded from the food chain. To this end, a Partial Least Squares (PLS) discriminant analysis, using visible and NIR reflectance spectra, was developed on a calibration set of 67 samples consisting of 22 authentic fishmeal (FM) specimens and 45 fishmeals deliberately adulterated with meat and bone meal (MBM) at 3%, 6% and 9% by weight, respectively; 15 samples were prepared at each concentration. Each material was unique in that any one fishmeal or meat and bone meal was used once only. In an independent validation set of 69 specimens prepared in exactly the same way, the discriminant successfully detected 44 out of 45 adulterated specimens with an error of one false positive among the remaining 24 pure fishmeals. Performance was tested on two independent monochromators and a canonical discriminant algorithm gave similar results. The NIR region (1100–2500 nm) or the visible and Herschel IR (400–1100 nm) alone did not perform as well as the combined visible and NIR regions. Modified PLS calibration for MBM % on the complete set of 136 specimens gave a standard error of calibration (SEC) of 0.85% and coefficient of determination (R2) of 0.94 based on the use of nine factors. Selection of appropriate and representative specimens for calibration and validation had a much greater effect on performance than any data treatment, scatter correction or the number of cross-validations or PLS factors used. Misclassification errors arose from specimens which were global H outliers having atypical spectra not represented in the calibration model. We believe that visible-NIR reflectance spectroscopy could routinely provide the first line of defence of the food chain against accidental contamination or fraudulent adulteration of fishmeal with meat and bone meal, which could present a health risk from transmissible spongiform encephalopathies (TSEs).

The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes

Visible and near infrared reflectance spectroscopy was used to predict pH at 24h (pH24) post-mortem, sarcomere length (SL), cooking loss (CL), Warner-Bratzler Shear Force (WBSF) and colour parameters (L(∗), a(∗), b(∗)) in beef cattle samples. Samples from M. longissimus thoracis et lumborum from 30 bulls were aged at 4°C for 1, 3, 7 and 14 days and analysed for pH, SL, CL, WBSF and colour. NIRS calibrations for pH24, luminosity at 0 (L(∗)t0) and 60min (L(∗)t60) showed good predictability (R(2)=0.97, 0.85 and 0.82; SECV=0.10, 1.16, 1.36, respectively), whereas those related to the rest of the parameters were poorer.

Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy

The useful wavelengths in both the visible and the near infrared region as well as two sample presentations (intact and minced) were evaluated to assess moisture (M), crude protein (CP) and intra muscular fat (IMF) in lamb (n = 300), beef (n = 100) and chicken (n = 48) muscle samples. Samples were scanned in reflectance in a NIRSystems 6500 (NIRSystems, Silver Spring, MD, USA). Predictive equations were performed using modified partial least squares (MPLS) with internal cross-validation. The coefficient of determination in calibration (R2CAL) and the standard error in cross-validation (SECV) were calculated for each chemical parameter. For moisture, crude protein and fat (each expressed as g kg−1), R2CAL and SECV for beef muscle were 0.98, 0.81 and 0.96, respectively, and SECV was 33.1, 21.8 and 44.8 for beef muscle; for chicken muscle the comparable statistics were 0.99, 0.97 and 0.95 and SECV was 6.9, 2.4 and 33.1; while for lamb muscle R2CAL was 0.76, 0.83 and 0.73 and SECV 10.3, 5.5 and 4.7. It was concluded that the minced presentation is the best way to analyse muscle samples. On the other hand, intact presentation could have a great potential for use in the meat industry, although more research will be needed in order to determine quality attributes on meat samples.

Visible and near infrared reflectance spectroscopy for the determination of moisture, fat and protein in chicken breast and thigh muscle

Near infrared (NIR) reflectance spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned from 400 to 2500 nm, both as intact muscle and as comminuted (minced) tissue. Modified partial least squares (MPLS) regression on scatter corrected spectra (standard normal variates and Detrend) gave calibration models for chemical variables from NIR measurements on the defrosted minced breast samples having multivariate correlation coefficients and standard errors of calibration of 0.995 (2.4), 0.974 (2.11) and 0.946 (4.55) for moisture, crude protein and fat in g kg −1, respectively.

Prediction of the in vitro gas production and chemical composition of kikuyu grass by near-infrared reflectance spectroscopy

Abstract The objective of this study was to predict the in vitro gas production and the estimated metabolisable energy (ME), crude protein (CP) and neutral detergent fibre (NDF) concentrations of kikuyu grass ( Pennisetum clandestinum ) by near infrared reflectance spectroscopy (NIRS). A total of 288 samples collected in the Poas Region, Costa Rica were scanned (Population 1). The in vitro gas production and ME calibrations were done on a subset of samples in which gas production measurements (3, 6, 12, 24, 36, 48, 72 and 96 h incubations) had been previously carried out (Population 2) while 41 samples for the CP and NDF calibrations (Population 3) were selected on the basis of their H distances from Population 1. The parameters a , b , c and lag for the exponential equation p = a + b (1 − e − c ( t − lag ) ) (McDonald, 1981), where p is the volume of gas produced at time t , were fitted to the gas production data and an attempt was also made to predict them. The volumes of gas produced between 6 and 48 h were successfully calibrated and cross-validated. Coefficients of determination for the cross-validation (1 − RV ) were 0.65, 0.74, 0.78, 0.70 and 0.60 for the volumes of gas produced at 6, 12, 24, 36 and 48 h respectively. The volumes of gas produced at 72 h could only be calibrated ( R 2 = 0.71) but not cross-validated, while the calibration results for the gas production at 3 and 96 h and the parameters for the exponential equation were poor. An analysis of the wavelength segments associated with the in vitro gas production indicated that the primary wavelength was always located between the 1664 and the 1696 nm spectral region regardless of incubation time. The estimated ME, CP and NDF concentrations were accurately calibrated and cross-validated. Standard errors of cross-validation of 0.23 MJ kg −1 DM, 11.4 g kg −1 DM and 15.9 g kg −1 DM were obtained for the ME, CP and NDF concentrations respectively. Scatter correction for particle size improved the performance of most of the equations across all constituents. The effects of different calibration methods, maths treatments and the factors affecting the results are discussed.

A Review on the Application of Infrared Technologies to Determine and Monitor Composition and Other Quality Characteristics in Raw Fish, Fish Products, and Seafood

Abstract: Demand for high levels of quality and safety in fish production obviously require high standards in quality assurance of raw materials and process control. Satisfying this demand in turn requires appropriate analytical tools for analysis both during and after production. Desirable features of such tools include speed, ease of use, minimal or no sample preparation, and the avoidance of sample destruction. These features are characteristic of a range of spectroscopic methods including mid-infrared (MIR) and near-infrared (NIR) spectroscopy. This article reviews some of the recent technical applications of infrared (IR) spectroscopy to determine and monitor composition and other quality characteristics in raw fish, fish products, and seafood.

The ability of visible and near infrared reflectance spectroscopy to predict the chemical composition of ground chicken carcasses and to discriminate between carcasses from different genotypes

The potential of visible and near infrared (NIR) spectroscopy to predict the fat, crude protein (CP) and ash content (g kg−1 DM) in dry ground chicken carcasses was evaluated. In addition, NIR spectroscopy was used to discriminate between ground carcasses from three different chicken genotypes: fast-growing broiler, slow-growing broiler and a layer-type chicken. When corrected for age and body mass (BM), the fast-growing broiler had the highest fat content and the lowest CP and ash content of the three genotypes. In contrast, the layer genotype had the highest CP and ash content and the lowest fat content. The fat, ash and CP content were intermediate in the slow-growing broilers. Spectra could explain a high proportion of the variability in carcass composition with respect to fat (R2 = 0.93) and CP (R2 = 0.86) content but less so for the ash content (R2 = 0.71). Carcasses could be accurately classified according to chicken genotype or dietary treatment using NIR. However discrimination between male and female birds was not so clear, probably because all the birds used in the study were sexually immature.

Visible and near infrared spectroscopy of beef longissimus dorsi muscle as a means of dicriminating between pasture and corn silage feeding regimes

Near infrared (NIR) reflectance spectroscopy was used as a tool to classify beef muscle samples according to their feeding regime. Seventy-eight beef longissimus dorsi muscle samples both intact and minced were scanned in a NIRS 6500 instrument (NIRSystems, MD, USA) in reflectance. A dummy regression technique was developed to differentiate beef muscle samples, which originated from beef feed exclusively on pasture or/and mainly on corn silage feeding regimes. Ninety percent of the pasture-fed beef muscle samples were correctly classified using principal component regression (PCR) and 86% of beef fed on corn silage were correctly classified. Both muscle chemical composition and physical characteristics explained the classification results. The results in the present study showed the potential of muscle optical properties for classification and traceability of meat muscles in the food chain.

Nutritive evaluation of forages by near infrared reflectance spectroscopy

Since the potential of near infrared (NIR) spectroscopy for forage evaluation was discovered three decades ago, it has become clear that it is a powerful tool for the estimation of chemical components in these feed-stuffs. In addition, it has been successfully applied for the estimation of digestibility and degradability parameters, thus facilitating livestock ration formulation. The present review deals with the main reference methods that have been used in order to achieve proper calibration of NIR spectroscopy. Special attention will be focused on the weak points of these procedures with the intention of increasing NIR spectroscopy potential in this area.