Calibration models developed from hyperspectral imaging data may be applied at the pixel level to... more Calibration models developed from hyperspectral imaging data may be applied at the pixel level to generate prediction maps that estimate the spatial distribution of components in a sample. Such prediction maps facilitate direct visual interpretation of model performance, and performance indicators can be extracted from them. These maps can be used as a tool to evaluate calibration models developed on hyperspectral imaging data. This paper presents a method for calibration model evaluation based on information obtained from prediction maps and demonstrates its usefulness for preventing overfitting. Partial least-squares regression was used for model calibration in this study, although in principle the proposed method may be used to evaluate other multivariate calibration methods, e.g. ridge regression and principal-components regression.
Identification of mushrooms that have been physically damaged and the measurement of time elapsed... more Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.
The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality... more The 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 (...
The data presented in this article are related to the research article entitled "Application... more The 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 ( = 72). Models developed using selected Raman shift ranges (i.e. 250-3380 cm, 900-1800 cm and 1300-2800 cm) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300-2800 cm.
In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytic... more In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytical technologies were investigated for assessment of infant formula quality and compositional parameters namely preheat temperature, storage temperature, storage time, fluorescence of advanced Maillard products and soluble tryptophan (FAST) index, soluble protein, fat and surface free fat (SFF) content. PLS-DA models developed using spectral data with appropriate data pre-treatment and significant variables selected using Martens' uncertainty test had good accuracy for the discrimination of preheat temperature (92.3-100%) and storage temperature (91.7-100%). The best PLS regression models developed yielded values for the ratio of prediction error to deviation (RPD) of 3.6-6.1, 2.1-2.7, 1.7-2.9, 1.6-2.6 and 2.5-3.0 for storage time, FAST index, soluble protein, fat and SFF content prediction respectively. Vis-NIR, MIR and Raman were demonstrated to be potential PAT tools for process co...
This work aims to develop a rapid analytical technique to predict beef sensory attributes using R... more This work aims to develop a rapid analytical technique to predict beef sensory attributes using Raman spectroscopy (RS) and to investigate correlations between sensory attributes using chemometric analysis. Beef samples (n = 72) were obtained from young dairy bulls (Holstein-Friesian and Jersey×Holstein-Friesian) slaughtered at 15 and 19 months old. Trained sensory panel evaluation and Raman spectral data acquisition were both carried out on the same longissimus thoracis muscles after ageing for 21 days. The best prediction results were obtained using a Raman frequency range of 1300-2800 cm. Prediction performance of partial least squares regression (PLSR) models developed using all samples were moderate to high for all sensory attributes (RCV values of 0.50-0.84 and RMSECV values of 1.31-9.07) and were particularly high for desirable flavour attributes (RCVs of 0.80-0.84, RMSECVs of 4.21-4.65). For PLSR models developed on subsets of beef samples i.e. beef of an identical age or br...
Raman spectroscopy and chemometrics were investigated for the prediction of eating quality relate... more Raman 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 1300-2800cm(-1) was used for partial least squares (PLS) modelling. PLS regression (PLSR) models for the prediction of WBSF and cook loss achieved an R(2)CV of 0.75 with RMSECV of 6.82 N and an R(2)CV of 0.77 with RMSECV of 0.97%w/w respectively. For the prediction of intramuscular fat, moisture and crude protein content, R(2)CV 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 R(2)CV of 0.79 was achieved for the prediction of both total collagen and hydroxyproline content, while for collagen solubility the R(2)CV 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 (longiss...
Journal of agricultural and food chemistry, Jan 15, 2017
The U.S. Pharmacopeia has led an international collaborative project to develop a tool-box of scr... more The U.S. Pharmacopeia has led an international collaborative project to develop a tool-box of screening methods and reference standards for the detection of milk powder adulteration. During the development of adulterated milk powder reference standards, blending methods used to combine melamine and milk had unanticipated strong effects on the NIR spectrum of melamine. The prominent absorbance band at 1468 nm of melamine was retained when it was dry-blended with skim milk powder but disappeared in wet-blended mixtures, where spray dried milk powder samples were prepared from solution. Analyses using polarized light microscopy, Raman spectroscopy, dielectric relaxation spectroscopy, X-ray diffraction, and mass spectrometry indicated that wet-blending promoted reversible and early Maillard reactions with lactose that are responsible for differences in melamine NIR spectra between wet- and dry-blended samples. Targeted detection estimates based solely on dry-blended reference standards ...
Differences between cattle production systems can influence the nutritional and sensory character... more Differences between cattle production systems can influence the nutritional and sensory characteristics of beef, in particular its fatty acid (FA) composition. As beef products derived from pasture-based systems can demand a higher premium from consumers, there is a need to understand the biological characteristics of pasture produced meat and subsequently to develop methods of authentication for these products. Here, we describe an approach to authentication that focuses on differences in the transcriptomic profile of muscle from animals finished in different systems of production of practical relevance to the Irish beef industry. The objectives of this study were to identify a panel of differentially expressed (DE) genes/networks in the muscle of cattle raised outdoors on pasture compared to animals raised indoors on a concentrate based diet and to subsequently identify an optimum panel which can classify the meat based on a production system. A comparison of the muscle transcript...
The perceived benefit of functional foods in the prevention or mitigation of degenerative disease... more The perceived benefit of functional foods in the prevention or mitigation of degenerative diseases has stimulated the growth of the functional food market. This perception is based on the presence in these foods of specific molecules which have a positive pharmacological effect when consumed in sufficient quantities (bioactive compounds). The increasing market and consumer desire for quality food products with positive health benefits has created a need for efficient and accurate analytical methods for the quantification of bioactive compounds in raw materials and finished products. Near infrared (NIR) spectroscopy is a fast, non-destructive and accurate method of analysis that has been extensively utilised for the study of foods. NIR spectroscopy has been used to quantify carotenoids, polyphenols, fatty acids and glucosinolates in a wide range of food commodities, for example, wine, dairy products, tea, fruit, vegetables, herbs, spices and cereals. Often, these quantifications are based on data from both the NIR and visible spectral regions; several bioactive compounds are also considered pigments, hence the utility of the visible spectral region. Major classes of other bioactive compounds, including pre- and probiotics, have yet to be analysed using NIR spectroscopy. The use of NIR spectroscopy for analysis of bioactive compounds is expected to match the growth of the functional food and bioactive ingredients markets.
Calibration models developed from hyperspectral imaging data may be applied at the pixel level to... more Calibration models developed from hyperspectral imaging data may be applied at the pixel level to generate prediction maps that estimate the spatial distribution of components in a sample. Such prediction maps facilitate direct visual interpretation of model performance, and performance indicators can be extracted from them. These maps can be used as a tool to evaluate calibration models developed on hyperspectral imaging data. This paper presents a method for calibration model evaluation based on information obtained from prediction maps and demonstrates its usefulness for preventing overfitting. Partial least-squares regression was used for model calibration in this study, although in principle the proposed method may be used to evaluate other multivariate calibration methods, e.g. ridge regression and principal-components regression.
Identification of mushrooms that have been physically damaged and the measurement of time elapsed... more Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.
The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality... more The 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 (...
The data presented in this article are related to the research article entitled "Application... more The 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 ( = 72). Models developed using selected Raman shift ranges (i.e. 250-3380 cm, 900-1800 cm and 1300-2800 cm) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300-2800 cm.
In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytic... more In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytical technologies were investigated for assessment of infant formula quality and compositional parameters namely preheat temperature, storage temperature, storage time, fluorescence of advanced Maillard products and soluble tryptophan (FAST) index, soluble protein, fat and surface free fat (SFF) content. PLS-DA models developed using spectral data with appropriate data pre-treatment and significant variables selected using Martens' uncertainty test had good accuracy for the discrimination of preheat temperature (92.3-100%) and storage temperature (91.7-100%). The best PLS regression models developed yielded values for the ratio of prediction error to deviation (RPD) of 3.6-6.1, 2.1-2.7, 1.7-2.9, 1.6-2.6 and 2.5-3.0 for storage time, FAST index, soluble protein, fat and SFF content prediction respectively. Vis-NIR, MIR and Raman were demonstrated to be potential PAT tools for process co...
This work aims to develop a rapid analytical technique to predict beef sensory attributes using R... more This work aims to develop a rapid analytical technique to predict beef sensory attributes using Raman spectroscopy (RS) and to investigate correlations between sensory attributes using chemometric analysis. Beef samples (n = 72) were obtained from young dairy bulls (Holstein-Friesian and Jersey×Holstein-Friesian) slaughtered at 15 and 19 months old. Trained sensory panel evaluation and Raman spectral data acquisition were both carried out on the same longissimus thoracis muscles after ageing for 21 days. The best prediction results were obtained using a Raman frequency range of 1300-2800 cm. Prediction performance of partial least squares regression (PLSR) models developed using all samples were moderate to high for all sensory attributes (RCV values of 0.50-0.84 and RMSECV values of 1.31-9.07) and were particularly high for desirable flavour attributes (RCVs of 0.80-0.84, RMSECVs of 4.21-4.65). For PLSR models developed on subsets of beef samples i.e. beef of an identical age or br...
Raman spectroscopy and chemometrics were investigated for the prediction of eating quality relate... more Raman 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 1300-2800cm(-1) was used for partial least squares (PLS) modelling. PLS regression (PLSR) models for the prediction of WBSF and cook loss achieved an R(2)CV of 0.75 with RMSECV of 6.82 N and an R(2)CV of 0.77 with RMSECV of 0.97%w/w respectively. For the prediction of intramuscular fat, moisture and crude protein content, R(2)CV 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 R(2)CV of 0.79 was achieved for the prediction of both total collagen and hydroxyproline content, while for collagen solubility the R(2)CV 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 (longiss...
Journal of agricultural and food chemistry, Jan 15, 2017
The U.S. Pharmacopeia has led an international collaborative project to develop a tool-box of scr... more The U.S. Pharmacopeia has led an international collaborative project to develop a tool-box of screening methods and reference standards for the detection of milk powder adulteration. During the development of adulterated milk powder reference standards, blending methods used to combine melamine and milk had unanticipated strong effects on the NIR spectrum of melamine. The prominent absorbance band at 1468 nm of melamine was retained when it was dry-blended with skim milk powder but disappeared in wet-blended mixtures, where spray dried milk powder samples were prepared from solution. Analyses using polarized light microscopy, Raman spectroscopy, dielectric relaxation spectroscopy, X-ray diffraction, and mass spectrometry indicated that wet-blending promoted reversible and early Maillard reactions with lactose that are responsible for differences in melamine NIR spectra between wet- and dry-blended samples. Targeted detection estimates based solely on dry-blended reference standards ...
Differences between cattle production systems can influence the nutritional and sensory character... more Differences between cattle production systems can influence the nutritional and sensory characteristics of beef, in particular its fatty acid (FA) composition. As beef products derived from pasture-based systems can demand a higher premium from consumers, there is a need to understand the biological characteristics of pasture produced meat and subsequently to develop methods of authentication for these products. Here, we describe an approach to authentication that focuses on differences in the transcriptomic profile of muscle from animals finished in different systems of production of practical relevance to the Irish beef industry. The objectives of this study were to identify a panel of differentially expressed (DE) genes/networks in the muscle of cattle raised outdoors on pasture compared to animals raised indoors on a concentrate based diet and to subsequently identify an optimum panel which can classify the meat based on a production system. A comparison of the muscle transcript...
The perceived benefit of functional foods in the prevention or mitigation of degenerative disease... more The perceived benefit of functional foods in the prevention or mitigation of degenerative diseases has stimulated the growth of the functional food market. This perception is based on the presence in these foods of specific molecules which have a positive pharmacological effect when consumed in sufficient quantities (bioactive compounds). The increasing market and consumer desire for quality food products with positive health benefits has created a need for efficient and accurate analytical methods for the quantification of bioactive compounds in raw materials and finished products. Near infrared (NIR) spectroscopy is a fast, non-destructive and accurate method of analysis that has been extensively utilised for the study of foods. NIR spectroscopy has been used to quantify carotenoids, polyphenols, fatty acids and glucosinolates in a wide range of food commodities, for example, wine, dairy products, tea, fruit, vegetables, herbs, spices and cereals. Often, these quantifications are based on data from both the NIR and visible spectral regions; several bioactive compounds are also considered pigments, hence the utility of the visible spectral region. Major classes of other bioactive compounds, including pre- and probiotics, have yet to be analysed using NIR spectroscopy. The use of NIR spectroscopy for analysis of bioactive compounds is expected to match the growth of the functional food and bioactive ingredients markets.
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Papers by Gerard Downey